WO2008039931A2 - Pride algorithm application - Google Patents

Abstract

The present invention provides embodiments of systems and methods which combine a simple and accurate diagnostic algorithmic scoring system that integrates NT-proBNP testing and routine clinical assessment in the diagnosis or exclusion acute heart failure in patients presenting to an emergency department (ED) with acute dyspnea. Embodiments of the invention provide for detecting early stages of CHF in the absence of clinically obvious symptoms and for assessing the prognosis of patients with CHF and ACS. Embodiments of the invention also provide risk stratification of patients with ACS, heart failure and at cardiovascular risk. Embodiments of the invention further provide a prognostic algorithmic scoring system for predicting one-year mortality in patients who present dyspnea to the ED. Embodiments of the invention further provide a handheld device with input and display capabilities for mobile diagnostics.

Description

PRIDE ALGORITHM APPLICATION

FIELD OF THE INVENTION

The present invention relates generally to an algorithmic scoring method or system for the diagnosis, prognosis and validation risk stratification of dyspneic patients who may or may not suffer from acute congestive heart failure. The present invention also relates to algorithmic scoring method or system for predicting probability of mortality in patients with dyspnea.

BACKGROUND OF THE INVENTION

Dyspnea, the subjective sensation of breathlessness, is among the most commonly encountered symptoms in the emergency department. Dyspnea can be the lone cardinal manifestation or a component of a symptom complex attributed to an exhaustive list of diseases. Primary disease processes involving the heart, lungs, kidneys, nervous system, and musculoskeletal system are all common causes of this sensation. Dyspnea can be caused by relatively benign processes or can be an indicator of severe, life threatening illness. Studies have shown that the type and severity of an underlying lung and heart disease correlate well with the way the patient describes dyspnea (Zoorob, R. J. et al., American Family Physician, 68(9): 1803-1810). When a clinician is faced with an acutely dyspneic patient, the emphasis first is placed on determining the active disease process that cause this complaint. The standard medical history, physical examination, and routine radiographic testing combined with newer disease specific blood tests are relied on for this task. In the ideal scenario, a unifying diagnosis is established and the patient is further risk stratified, treated, and triaged. Unfortunately, this ideal sequence is often arrested by diagnostic uncertainty. When the clinician is unable to determine the cause(s) of dyspnea it becomes difficult to determine the optimal course of treatment and to assign an accurate prognosis.

An initial attempt to determine patient prognosis is critical to safe and effective patient care. Prognostication, the act of risk stratification, is done, often subconsciously and qualitatively, for every patient cared for in the emergency department (ED) setting. The results of this process guide decisions about the intensity of treatment, the need for hospital admission, and the patient counseling provided at the time of the encounter. For several common diseases, including the acute coronary syndromes (ACS) and pulmonary thromboembolic disease, quantitative methods of risk stratification have been developed. These methods involve sign and symptom based risk calculators and or the use of blood assays which can be reflective of disease severity. When definitive diagnosis is not possible, quantitative risk stratification is not possible. For the breathless patient, this scenario is a common one. As such, a validated risk stratification strategy that could be applied to the dyspneic patient, with or without a definitive diagnosis and that would also unify both clinical and biochemical predictors of risk for acute dyspneic patients. To date, no such tool has been developed.

The signs and symptoms of acute congestive heart failure (CHF) are frequently non-specific, highly variable, and may also be observer dependent, thereby rendering accurate diagnosis a significant clinical challenge.

B-type natriuretic peptide testing is useful for the diagnostic evaluation of patients with dyspnea and suspected acute destabilized congestive heart failure (ADHF) (Januzzi, J. L. et al, Am. J. Cardiol, 95:948-954, 2005; Maisel, A. et al, J. Am. Coll. Cardiol., 44:1328-1333, 2004; Maisel, A. S., N. Engl. J. Med., 347:161-167, 2002).

Including both the B-type natriuretic peptide and its pro fragment, amino -terminal probrain natriuretic peptide (NT-proBNP), this class of cardiac biomarkers is useful not only for diagnosis or exclusion of CHF but also for stratification of long-term risk of mortality in community-based populations without CHF (Wang, T. J. et al., N. Engl. J. Med., 350:655- 663,2004) and those with chronic CHF (Gardner, R. S. et al., Eur. Heart J., 24;1735-1743, 2003; Hartmann, F. et al, Eur. J. Heart Fail, 6:343-350, 2004; Anand, I. S. et al, Circulation, 107;1278-1283, 2003; Berger, R. et al, Circulation, 105:2392-2397, 2002; Groenning, B. A. et al, Heart, 90:297-303, 2004).

It is also useful to predict prognosis in individuals with non-CHF states, such as coronary artery disease (Kragelund, C. et al, N. Engl. J. Med., 352:666-675, 2005; Galvani, M. et al, Circulation, 110:128-134, 2004; Heeschen, C. et al, Circulation, 110:3206-3212, 2004; James, S. K. et al, Circulation, 108:275-281, 2003; Jemberg, T. et al, J. Am. Coll. Cardiol, 40:437-445, 2002; Mega, J. L. et al, J. Am. Coll. Cardiol, 44:335- 339, 2004; Omland, T. et al, Circulation, 106: 2913-2918, 2002; Omland, T. et al, Am. J. Cardiol, 95:24-28, 2005; Richards, A. M. et al, Circulation, 107:2786-2792, 2003; Sabatine, M. S. et al, Circulation, 105:1760-1763, 2002), pulmonary embolism (PE) (Kucher, N. et al, Circulation, 107:1576-1578, 2003; Pruszczyk, P. et al, Eur. Respir. J., 22:649-653, 2003) and critical illness (Tung, R. H. et al, Crit. Care Med., 32:1643-1647, 2004)and following cardiac transplantation (Ambrosi, P. et al, Am. J. Cardiol., 94:1585- 1587, 2004). However, the role of the B-type natriuretic peptides for defining the longer- term prognosis of patients who present with dyspnea to the emergency department (ED) - the most common indication for B-type natriuretic peptide measurement - remains undefined.

In addition, NT-proBNP testing is a useful adjunct to routine assessment for differentiating acute CHF from other etiologies of dyspnea. To date, there are three large trials that have examined the role of BNP or NT-proBNP in evaluating patients with suspected heart failure, namely, (1) ProBNP Investigation of Dyspnea in the Emergency Department (PRIDE) study wherein among the 600 patients who presented to the ED with dyspnea, those patients with acute CHF had a median value of NT-proBNP of more than 4,000 pg/mL vs. 130 pg/mL in patients without acute CHF. Of patients who did not have acute CHF, e.g., those suffering an allergic reaction or anxiety attack, patients with the history of CHF had higher levels than patients with no history of CHF, although the levels were not nearly as high as those with acute CHF (Baggish, A.L. et al, Crit. Pathways in Cardiol, 3(4):171-176. 2004; Januzzi, J.L. et al., Am. J. Cardiol., 85:948-954, 2005); (2) Breathing Not Properly (BNP) study wherein BNP levels, measured in 1,500 patients who presented with acute dyspnea, were evaluated as a tool to discriminate between patients with, and without CHF, and for patients with CHF, between those with and without systolic heart failure. The findings of this study showed that the value of BNP testing was uncertain

(Maisel, A. et al, J. Am. Coll. Cardiol, 41 :2010-2017, 2003); and (3) Rapid Emergency Department Heart Failure Outpatient Trial (REDHOT) wherein BNP levels in more than 450 patients at multiple centers who presented to the ED with acute dyspnea were evaluated (Maisel, A. et al, J. Am. Coll. Cardiol, 44:1328-1333, 2004). Both PRIDE and REDHOT trials showed that more severe heart failure is associated with higher levels of natriuretic peptides and the severity of heart failure was underestimated. The above-mentioned studies had similar entry criteria, i.e., undifferentiated acute dyspnea whether or not heart failure was evident. The evaluating clinicians were blinded to BNP or NT-proBNP levels. Factors such as comorbid illnesses, age, renal failure, body-mass, and baseline systolic dysfunction may affect NT-proBNP levels in manners that obscure the diagnosis of acute CHF when this marker is used in isolation. Recent literature has suggested that a combination of NT-proBNP or B-type natriuretic peptide (BNP) testing and standard clinical assessment is superior to either tool used in isolation, however the optimal method to combine natriuretic peptide testing and clinical factors of proven value for predicting a diagnosis of CHF has not been accomplished. One such method might be to combine natriuretic peptide testing and clinical evaluation in a scoring system. Clinical scoring systems have been demonstrated to be useful in numerous clinical situations including acute aortic dissection, contrast induced nephropathy, and acute coronary syndromes. One example of a clinical diagnostic scoring system is the PRIDE algorithm that can be used for the diagnostic evaluation of patients with dyspnea and suspected acute CHF (Januzzi, J. L. et al, Am. J. Cardiol, 95:948-954, 2005). The PRIDE algorithm integrates NT-ProBNP testing and routine clinical assessment, which would optimize diagnostic accuracy for detecting acute CHF among patients presenting to an Emergency Department with acute dyspnea.

Accordingly, there is a need to provide an improved algorithmic scoring method and system for the diagnosis, prognosis and risk stratification of dyspneic patients who may or may not suffer from acute heart failure and for guiding clinicians in decision- making. There is a continuing need to provide an algorithmic scoring method system that is simple but accurate, cost-effective, and time-saving, as described hereinbelow.

SUMMARY OF THE INVENTION

The present invention provides embodiments of systems and methods which combine a simple and accurate diagnostic algorithmic scoring system that integrates NT- proBNP testing and routine clinical assessment in the diagnosis or exclusion acute heart failure in patients presenting to an emergency department (ED) with acute dyspnea. Embodiments of the invention provide for detecting early stages of CHF in the absence of clinically obvious symptoms and for assessing the prognosis of patients with CHF and ACS. Embodiments of the invention also provide risk stratification of patients with ACS, heart failure and at cardiovascular risk. Embodiments of the invention further provide a prognostic algorithmic scoring system for predicting one-year mortality in patients who present dyspnea to the ED.

Another embodiment of the invention includes a simple portable, accurate and user friendly tool that incorporates NT-proBNP testing and routine assessment for risk stratification of the dyspneic patient and demonstrates portability, with validation in a distinct patient population of dyspneic patients. Embodiments of the invention provide, among other things, software that may be considered for running the diagnostic and prognostic algorithmic scoring methods and systems as described hereinabove.

Embodiments of the invention can assist clinicians in distinguishing congestive heart failure from other disease states with similar clinical symptoms, for instance, lung diseases.

The present invention provides a user-friendly device tool system for a clinician to input patient data on an internet, mobile or handheld computerized device. The device may be a remote device that is part of an intranet, internet or both and calculations may be performed on the remote device or on a server. Analysis of inputted data uses statistical models that may be downloaded from the internet, a CD, DVD, or other electronic medium or may be created by the user or by another entity via a guided process for formula construction. The results of the statistic analysis are displayed within minutes such that they may be used as a rapid clinical decision aid. Numerous display options allow clinicians and patients to choose how the results appear.

Other features, objects, and advantages of the present invention are apparent in the detailed description that follows. It should be understood, however, that the detailed description, while indicating preferred embodiments of the present invention, is given by way of illustration only, not limitation. Various changes and modifications within the scope of the invention will become apparent to those skilled in the art from the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings are provided for illustration, not limitation. FIGURE 1 is a flow diagram showing a scoring algorithm for NT-proBNP and clinically-guided evaluation and triage of patients with suspected acute congestive heart failure and/or dyspnea.

FIGURE 2 is a graph showing receiver operator characteristic curve of NT- pro-BNP testing for the estimation of 1-yr mortality in dyspneic patients. The area under the curve was 0.76 (PO.001).

FIGURE 3 shows the rate of mortality of dyspneic patients in the emergency department by 1 year, expressed as a function of NT-proBNP in deciles. A strong threshold effect is noted at the NT-proBNP decile, corresponding to an NT-proBNP concentration in excess of 972 pg/mL (P<0.001 for trend across groups).

FIGURES 4A and 4B are graphs illustrating age-adjusted Kaplan Meier survival curves demonstrating the rates of mortality by 1 year associated with an elevated NT-proBNP concentration at emergency department presentation with dyspnea. The risk is observed in those both with (FIGURE 4A) (PO.001) and without (FIGURE 4B) (PO.001) acute congestive heart failure at presentation.

FIGURE 5 shows the histogram of PRIDE Mortality Scores across the entire derivation cohort. FIGURE 6 shows the Pride Mortality Score quintile vs. 1-year mortality.

FIGURE 7 illustrates the Kaplan-Meier curves demonstrating rates of survival through 1 year as a function of PRIDE Mortality Score quintile.

FIGURE 8 shows the histogram of PRIDE Mortality Scores across the entire validation cohort of dyspneic patients from Christchurch, New Zealand. FIGURES 9A-9H illustrate PRIDE PDA program algorithm.

FIGURE 10 illustrates the application of the PRIDE mortality calculator.

DETAILED DESCRIPTION OF THE INVENTION

Definitions In the description that follows, a number of terms are used extensively. The following definitions are provided to facilitate understanding of the invention. Unless otherwise specified, "a" or "an" means "one or more." This invention is a computer-based tool through which a clinician can establish or rule out the diagnosis of heart failure in acutely dyspneic patient. In brief, we have created a scoring system consisting of variables comprised of readily available demographic information and the results of routine testing performed during the initial patient/ clinician interaction. At the conclusion of patient evaluation, a clinician enters a patient specific value for each component of the score and the software generates a total score value for that individual patient. The software ultimately provides text suggestions about how to interpret the score. A brief description of this scoring tool derivation is warranted.

NT-proBNP results and clinical factors from 599 dyspneic patients were analyzed. The b-coefficients of the 8 independent predictors of CHF were used to assign a weighted integeric score for predictor. The sum of these integers provided a diagnostic CHF "score" for each patient. ROC curve analysis determined the optimal cut-point for the diagnosis of acute CHF.

Eight factors comprised the score: elevated NT-proBNP (4 points), interstitial edema on chest X-ray (2 points), orthopnea (2 points), absence of fever (2 points), loop diuretic use, age>75 years, rales, and absence of cough (all 1 point). Median scores in patients with acute CHF were higher than those without acute CHF (9 versus 3, p<0.001). At a cut-point of >6, the score had a sensitivity of 96% and a specificity of 84% for the diagnosis of acute CHF (p<0.001). The score improved diagnostic accuracy over NT- proBNP testing alone and retained discriminative capacity in patients in whom clinical uncertainty was present. Lastly, the accuracy of the score was validated in the external dataset of patients with suspected acute CHF.

Following development of the scoring system, we sought to test the scores performance in a patient group other than the derivation cohort. Patients from Christchurch, New Zealand who had previously been enrolled in an emergency room based heart failure diagnosis trial formed the validation cohort. The score retained diagnostic accuracy in this population suggesting that it is a tool with wide spread portability.

The present invention is directed to a method and system that employs a scoring tool through which a clinician can risk stratify an acute dyspneic patient. The scoring system includes a plurality of predictor variables that comprises readily available demographic information and results of routine testing performed during the initial patient/clinician interaction. At the conclusion of patient evaluation, a clinician would enter a patient specific value for each component of the score and a software would generate a total score value for that individual patient. The software would ultimately provide text suggestions about how to interpret the score.

To carry out the present invention, NT-proBNP results and clinical factors from 599 dyspneic subjects were evaluated. The β-coefficients from a set of independent predictor variables of CHF were used to assign a weighted integeric score for each predictor. The sum of these integers provided a diagnostic CHF "score" for each patient. ROC curve analysis determined the optimal cut-point for the diagnosis of acute CHF.

The eight predictor variables that comprise the score includes, namely: elevated NT-proBNP (4 points), interstitial edema on chest X-ray (2 points), orthopnea (2 points), absence of fever (2 points), loop diuretic use fever (1 point), age>75 years (1 point), rales (1 point), and absence of cough (1 point). Median scores in patients with acute CHF were higher than those without acute CHF (9 versus 3, p<0.001). At a cut-point of >6, the score had a sensitivity of 96% and a specificity of 84% for the diagnosis of acute CHF (p<0.001). The score improved diagnostic accuracy over NT-proBNP testing alone and retained discriminative capacity in patients in whom clinical uncertainty was present. Lastly, the accuracy of the score was validated in the external dataset of patients with suspected acute CHF. The first group of the above-mentioned 599 dyspneic makes up the derivation cohort group.

Scores obtained from the derivation cohort group were tested for score performance in a validation cohort group. The validation cohort group consists of patients from Christchurch, New Zealand who had previously been enrolled in an emergency room- based heart failure diagnosis trial. The score from this group retained diagnostic accuracy in this population suggesting that indeed the scoring tool of the present inventoon has wide spread portability. The present invention also includes a scoring tool through which a clinician can risk stratify an acutely dyspneic patient, this scoring system also includes predictor variables that comprise readily available demographic information and the results of routine testing performed during the initial patient/ clinician interaction. At the conclusion of patient evaluation, a clinician would use a patient specific value for each component of the score to determine a total score value for that individual patient. This cumulative score can then be used to predict one -year mortality risk. A brief description of the score derivation is as follows.

Again, 599 dyspneic subjects were evaluated in an emergency department to examine the utility of NT-proBNP (Elecsys® proBNP, Roche Diagnostics, Indianapolis, IN) measurement. Assessment of vital status was performed at one -year post-enrollment and factors independently predictive of death by one year were identified. Based on the β- coefficients, a set of plurality of predictor variables were used to develop a scoring tool capable of determining mortality risk for subjects presenting with acute dyspnea.

Seven predictor variables comprise the final scoring tool (adjustments for β coefficient noted): age (multiplied by 0.7), heart rate (multiplied by 0.5), blood urea nitrogen (multiplied by 0.5), NHYA Class (class multiplied by 5), NT-proBNP > 986 pg/ml (18 points), systolic blood pressure < 100 mmHg (11 points), and presence of a murmur (11 points). With a rising score, there was a monotonically rise in the risk for mortality across score quintiles: those in the highest score quintile (n=l 16, score > 85.5) had a one year mortality rate of 43.1% compared to those in the lowest score quintile (n=l 18, score £ 48.5) with a mortality rate of only 1.7% (p<0.001). ROC analysis of the scores accuracy to predict death produced an area under the curve (AUC) of 0.82 (95% CI= 0.78-0.85). Subjects with a final diagnosis of acute HF tended to have higher scores and subsequent higher rates of death, however, the scoring tool performed well both in subjects with acute HF (AUC=O.73, 95% CI=O.67-0,79) and in those with dyspnea attributed to an alternative etiology (AUC=0.83, 95% CI=0.77-0.85).

Following development of the scoring system, the test scores performance were further test in a validation cohort group from patients of Christchurch, New Zealand. The patients from the validation cohort group had previously been enrolled in an emergency room based heart failure diagnosis trial formed the validation cohort. The score retained prognostic accuracy in this population suggesting that it is a tool with wide spread portability. The term "acute coronary syndromes" ("ACS") has been applied to a group of vascular diseases that result from ischemic insult to the heart. ACS is a manifestation of vascular injury to the heart, also referred to as myocardial injury or myocardial damage, that is commonly secondary to atherosclerosis or hypertension, and is the leading cause of death in the United States. ACS is commonly caused by occlusion associated with coronary artery disease, which, in turn, is caused by atherosclerotic plaque formation and progression to either further occlusion or fissure. ACS can be manifested as stable angina, unstable angina, or myocardial infarction. ACS is believed to result largely from thrombus deposition and growth within one or more coronary arteries, resulting in a partial or complete occlusion of the artery, and frequently involves rupture of the plaque, resulting in an ischemic injury. ACS may also be precipitated by a coronary vasospasm or increased myocardial demand. For review, see, e.g., Davies, Clin. Cardiol. 20 (Supp. I): 12-17, 1997.

The term "test sample" as used herein refers to a sample of bodily fluid obtained for the purpose of diagnosis, prognosis, or evaluation of a subject of interest, such as a patient. In certain embodiments, such a sample may be obtained for the purpose of determining the outcome of an ongoing condition or the effect of a treatment regimen on a condition. Preferred test samples include blood, serum, plasma, cerebrospinal fluid, urine, saliva, sputum, and pleural effusions. In addition, one of skill in the art would realize that some test samples would be more readily analyzed following a fractionation or purification procedure, for example, separation of whole blood into serum or plasma components.

As used herein, a "plurality" as used herein refers to at least two. Preferably, a plurality refers to at least 3, more preferably at least 5, even more preferably at least 7, and most preferably at least 15.

As used herein, the term "subject" or "individual" refers to a human or other vertebrate animal. It is intended that the term encompass "patients."

The term "diagnosis" as used herein refers to methods by which the skilled artisan can estimate and/or determine whether or not a patient is suffering from a given disease or condition. The skilled artisan often makes a diagnosis on the basis of one or more diagnostic indicators, i.e., a marker, the presence, absence, amount, or change in amount of which is indicative of the presence, severity, or absence of the condition.

Similarly, a prognosis is often determined by examining a set of plurality of predictor variables. These predictor variables, the presence or amount of which in a patient (or a sample obtained from the patient) signal a probability that a given course or outcome will occur.

As used herein, a receiver operating characteristic (ROC) curve plots an independent variable's sensitivity (true positive fraction) on the y-axis against 1 -specificity (the false positive fraction on the x-axis as the cutoff value for a predicted positive observation is varied. A positive observation means that the predicted probability is greater than or equal to an investigator selected cutoff. The ROC curve or plot is useful for determining the sensitivity, specificity and negative and positive predictive values of a single test or a multiparameter test. In addition, the ROC curve can be used to establish the optimum threshold cutoff for a continuous variable. The area under the ROC curve is a measure of the probability that the perceived measurement will allow correct identification of a condition. ROC curves can be used even when test results don't necessarily give an accurate number. This ranking can be correlated to results in the "normal" population, and a ROC curve created. These methods are well known in the art. See, e.g., Hanley, et al., Radiology 143: 29-36 (1982). Preferably, a threshold is selected to provide a ROC curve area of greater than about 0.5, more preferably greater than about 0.7, still more preferably greater than about 0.8, even more preferably greater than about 0.85, and most preferably greater than about 0.9. The term "about" in this context refers to +1-5% of a given measurement. The clinical parameters of sensitivity, specificity, negative predictive value, positive predictive value and accuracy are calculated using true positives, false positives, true negatives and false negatives. A "true positive" sample is a sample positive for the indicated stage of dyspnea or CHF according to the evaluation or diagnosis, which is also diagnosed positive according to a method of the invention. A "false positive" sample is a sample negative for the indicated stage of stage of dyspnea or CHF, which is diagnosed positive according to a method of the invention. Similarly, a "false negative" is a sample positive for the indicated stage of stage of dyspnea or CHF, which is diagnosed negative according to a method of the invention. A "true negative" is a sample negative for the indicated stage of stage of dyspnea or CHF, and also negative for stage of dyspnea or CHF according to a method of the invention.

As used herein, the term "sensitivity" means the probability that a diagnostic method of the invention gives a positive result when the sample is positive. Sensitivity is calculated as the number of true positive results divided by the sum of the true positives and false negatives. Sensitivity essentially is a measure of how well a method correctly identifies those with stage of dyspnea or CHF. In a method of the invention, the cut-off values can be selected such that the sensitivity of diagnosing an individual is at least about 70%, and can be, for example, at least 75%, 80%, 85%, 90% or 95% in at least 60% of the patient population assayed, or in at least 65%, 70%, 75% or 80% of the patient population assayed.

As used herein, the term "specificity" means the probability that a diagnostic method of the invention gives a negative result when the sample is not positive, for example, dyspnea with CHF with a PRIDE score of >6. Specificity is calculated as the number of true negative results divided by the sum of the true negatives and false positives. Specificity essentially is a measure of how well a method excludes those who do not have dyspnea or CHF. In a method of the invention, the cut-off values can be selected such that, when the sensitivity is at least about 70%, the specificity of diagnosing an individual is in the range of 70 100%, for example, at least 75%, 80%, 85%, 90% or 95% in at least 60% of the patient population assayed, or in at least 65%, 70%, 75% or 80% of the patient population assayed. The term "negative predictive value," as used herein, is synonymous with

"NPV" and means the probability that an individual diagnosed as not having dyspnea. Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the diagnostic method as well as the prevalence of dyspnea.

The term "positive predictive value," as used herein, is synonymous with "PPV" and means the probability that an individual diagnosed as having fibrosis actually has the condition. Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. Positive predictive value is determined by the characteristics of the diagnostic method as well as the prevalence of dyspnea in the population analyzed.

As used herein, the term "accuracy" means the overall agreement between the diagnostic method and the disease state. Accuracy is calculated as the sum of the true positives and true negatives divided by the total number of sample results and is affected by the prevalence of fibrosis in the population analyzed.

In some embodiments described herein, identification of independent predictors of death at 1 year following dyspnea presentation was assessed utilizing an age- stratified Cox Proportional Hazards Model Analysis, which is a regression method for survival data that provides an estimate of the hazard ratio and its confidence interval. The Cox model is a well-recognized statistical technique for exploring the relationship between the survival of a patient and predictor variables. This statistical method permits estimation of the hazard (i.e., risk) of individuals given their predictor variables. Cox model data are commonly presented as Kaplan-Meier curves. The "hazard ratio" is the risk of death at any given time point for patients displaying particular clinical variables. See generally Spruance et al., Antimicrob. Agents & Chemo. 48:2787-2792, 2004. In particular embodiments, the independent predictors of death at 1 year following presentation of dyspnea are statistically significant for assessment of the likelihood of acute dyspneas with or without CHF. Methods for assessing statistical significance are well known in the art and include, for example, using a log-rank test Cox analysis and Kaplan-Meier curves. In some aspects of the invention, a p-value of less than 0.05 constitutes statistical significance.

In the case of a hazard ratio, a value of 1 indicates that the relative risk of an endpoint (e.g., death) is equal in both the "diseased" and "control" groups; a value greater than 1 indicates that the risk is greater in the diseased group; and a value less than 1 indicates that the risk is greater in the control group. In certain preferred embodiments, the independent predictors of death at 1 year following presentation of dyspnea are preferably selected to exhibit a hazard ratio of at least about 1.1 or more or about 0.91 or less, more preferably at least about 1.25 or more or about 0.8 or less, still more preferably at least about 1.5 or more or about 0.67 or less, even more preferably at least about 2 or more or about 0.5 or less, and most preferably at least about 2.5 or more or about 0.4 or less. The term "about" in this context refers to +1-5% of a given measurement. The skilled artisan will understand that associating an independent predictor of death, with a diagnosis or with a prognostic risk of a future clinical outcome is a statistical analysis. For example, a marker level of greater than X may signal that a patient is more likely to suffer from an adverse outcome than patients with a level less than or equal to X, as determined by a level of statistical significance. Additionally, a change in marker concentration from baseline levels may be reflective of patient prognosis, and the degree of change in marker level may be related to the severity of adverse events. Statistical significance is often determined by comparing two or more populations, and determining a confidence interval and/or a p value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983. Preferred confidence intervals of the invention are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while preferred p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001.

As used herein, the term "subject" or "individual" refers to a human or other vertebrate animal. It is intended that the term encompass "patients."

The invention involves comparing the level in a sample of a subject's plasma, blood, serum, body fluid or tissue.

As used herein, the term antibody includes polyclonal and monoclonal antibodies of any isotype (IgA, IgG, IgE, IgD, IgM), or an antigen-binding portion thereof, including but not limited to F(ab) and Fv fragments, single chain antibodies, chimeric antibodies, humanized antibodies, and a Fab expression library. Antibodies useful as detector and capture antibodies in the present invention may be prepared by standard techniques well known in the art. The antibodies can be used in any type of immunoassay. This includes both the two-site sandwich assay and the single site immunoassay of the non-competitive type, as well as in traditional competitive binding assays. Particularly preferred, for ease and simplicity of detection, and its quantitative nature, is the sandwich or double antibody assay of which a number of variations exist, all of which are contemplated by the present invention. For example, in a typical sandwich assay, unlabeled antibody is immobilized on a solid phase, e.g. microtiter plate, and the sample to be tested is added. After a certain period of incubation to allow formation of an antibody- antigen complex, a second antibody, labeled with a reporter molecule capable of inducing a detectable signal, is added and incubation is continued to allow sufficient time for binding with the antigen at a different site, resulting with a formation of a complex of antibody- antigen-labeled antibody. The presence of the antigen is determined by observation of a signal which may be quantitated by comparison with control samples containing known amounts of antigen.

The assays may be competitive assays, sandwich assays, and the label may be selected from the group of well-known labels such as radioimmunoassay, fluorescent or chemiluminescence immunoassay, or immunoPCR technology. Extensive discussion of the known immunoassay techniques is not required here since these are known to those of skilled in the art.

In one particular embodiment, the immunoassay method exemplified herein is known and is commercially available. An example of a commercially known and widely available is the Elecsys proBNP assay from Roche Diagnostics, Indianapolis, IN. Enzyme-linked immunosorbent assays (ELISAs) can be useful in the methods of the invention. An enzyme such as horseradish peroxidase (HRP), alkaline phosphatase (AP), β-galactosidase or urease can be linked to the primary or a secondary antibody for use in a method of the invention. Other convenient enzyme-linked systems include, for example, the alkaline phosphatase detection system, a β-galactosidase detection system, a urease detection system. Useful enzyme-linked primary and secondary antibodies can be obtained from a number of commercial sources such as Jackson Immuno-Research (West Grove, Pa.).

Chemiluminescent detection also can be useful for detecting or for determining a level of the biomarker according to a method of the invention. Chemiluminescent secondary antibodies can be obtained commercially from various sources such as Amersham.

Fluorescent detection also can be useful for detecting or for determining a level of the biomarker in a method of the invention. Useful fluorochromes include, without limitation, DAPI, fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin, R- phycoerythrin, rhodamine, Texas red and lissamine. Fluorescein or rhodamine labeled biomarker-specifϊc binding agents or fluorescein- or rhodamine-labeled secondary antibodies can be useful in the invention. Useful fluorescent antibodies can be obtained commercially, for example, from Tago Immunologicals (Burlingame, Calif). Radioimmunoassays (RIAs) also can be useful in the methods of the invention. Such assays are well known in the art. Radioimmunoassays can be performed, for example, with ¹²⁵I-labeled primary or secondary antibody (Harlow and Lane, supra, 1988).

A signal from a detectable reagent can be analyzed, for example, using a spectrophotometer to detect color from a chromogenic substrate; a radiation counter to detect radiation, such as a gamma counter for detection of ¹²⁵I; or a fluorometer to detect fluorescence in the presence of light of a certain wavelength. Where an enzyme-linked assay is used, quantitative analysis of the amount of the biomarker can be performed using a spectrophotometer such as an EMAX Microplate Reader (Molecular Devices; Menlo Park, Calif.) in accordance with the manufacturer's instructions. It is understood that the assays of the invention can be automated or performed robotically, if desired, and that the signal from multiple samples can be detected simultaneously.

The methods of the invention also encompass the use of capillary electrophoresis based immunoassays (CEIA), which can be automated, if desired. Immunoassays also can be used in conjunction with laser-induced fluorescence as described, for example, in Schmalzing and Nashabeh, Electrophoresis 18:2184 93 (1997), and Bao, J. Chromatogr. B. Biomed. Sci. 699:463 80 (1997). Liposome immunoassays, such as flow- injection liposome immunoassays and liposome immunosensors, also can be used to detect or to determine a level of the biomarker according to a method of the invention (Rongen et al, J. Immunol. Methods 204:105 133 (1997)).

Sandwich enzyme immunoassays also can be useful in the methods of the invention. In a two-antibody sandwich assay, a first antibody is bound to a solid support, and the antigen is allowed to bind to the first antibody. The amount of the biomarker antigen is quantitated by measuring the amount of a second antibody that binds the biomarker. Quantitative western blotting also can be used to detect or to determine a level of the biomarker antigen in a method of the invention. Western blots can be quantitated by well known methods such as scanning densitometry. As an example, protein samples are electrophoresed on 10% SDS-PAGE Laemmli gels. Primary murine monoclonal antibodies, for example, against human biomarker are reacted with the blot, and antibody binding confirmed to be linear using a preliminary slot blot experiment. Goat anti-mouse horseradish peroxidase-coupled antibodies (BioRad) are used as the secondary antibody, and signal detection performed using chemiluminescence, for example, with the Renaissance chemiluminescence kit (New England Nuclear; Boston, Mass.) according to the manufacturer's instructions. Autoradiographs of the blots are analyzed using a scanning densitometer (Molecular Dynamics; Sunnyvale, Calif.) and normalized to a positive control. Values are reported, for example, as a ratio between the actual value to the positive control (densitometric index). Such methods are well known in the art as described, for example, in Parra et al, J. Vase. Surg. 28:669 675 (1998).

In another embodiment, a quantitative prediction of mortality by 1 year is applied, where multiple predictive variables are taken into consideration. Multiple predictive variables may be applied using the PRIDE algorithm, as disclosed below. These predictive variables include age (multiplied by 0.7), heart rate (multiplied by 0.5), blood urea nitrogen (multiplied by 0.5), New York Heart Association class (multiplied by 5), NT- proBNP > 986 pg/mL (18 points), systolic blood pressure < 100 mmHg (11 points), and presence of a murmur (11 points).

A Cox proportional hazard model using the above-mentioned predictive variables is generated, followed by a point scoring system based on the β-coeffϊcients, hazard ration and p-value. The output of the PRIDE algorithm is expressed as a function of PRIDE risk score quintile, demonstrated with respect to specificity, sensitivity, positive predictive value and negative predictive value.

As used herein the terms prognostic information and predictive information are used interchangeably to refer to any information that may be used to foretell any aspect of the course of a disease or condition either in the absence or presence of treatment. Such information may include, but is not limited to, the average life expectancy of a patient, the likelihood that a patient will survive for a given amount of time {e.g., 6 months, 1 year, 5 years, etc.), the likelihood that a patient will be cured of a disease, the likelihood that a patient's disease will respond to a particular therapy (wherein response may be defined in any of a variety of ways). Prognostic and predictive information are included within the broad category of diagnostic information.

The methods of the invention can readily be embodied in software, hardware, or a combination of these in order to provide automated and efficient detection and analysis of one year-mortality risk of dyspnea in a user-friendly and reproducible manner. The invention allows researchers and physicians to readily derive increased benefit from existing disease management and/or treatment strategies. These important diagnostic and prognostic methods and systems will thus improve therapy and outcomes with respect to predicting mortality risk of patients with acute dyspnea. Computer-Based Implementations.

The various techniques, methods and systems, and aspects of the invention described above can be implemented in part or in whole using computer-based systems and methods. Additionally, computer-based systems and methods can be used to augment or enhance the functionality described above, increase the speed at which the functions can be performed, and provide additional features and aspects as a part of or in addition to those of the present invention described elsewhere in this document. Various computer-based systems, methods and implementations in accordance with the above-described technology are now presented.

The various embodiments, aspects, and features of the invention described above may be implemented using hardware, software, or a combination thereof and may be implemented using a computing system having one or more processors. In fact, in one embodiment, these elements are implemented using a processor-based system capable of carrying out the functionality described with respect thereto. An example processor-based system includes one or more processors. Each processor is connected to a communication bus. Various software embodiments are described in terms of this example computer system. The embodiments, features, and functionality of the invention in this specification are not dependent on a particular computer system or processor architecture or on a particular operating system. In fact, given the instant description, it will be apparent to a person of ordinary skill in the relevant art how to implement the invention using other computer or processor systems and/or architectures.

In general, a processor-based system may include a main memory, preferably random access memory (RAM), and can also include one or more other secondary memories, including disk drives, tape drives, removable storage drives (e.g., pluggable or removable memory devices and tape drives, CD-ROM drives, DVD drives, floppy disk drives, optical disk drives, etc.). In alternative embodiments, secondary memories include other data storage devices for allowing computer programs or other instructions to be called or otherwise loaded into the computer system. A computer system of the invention can also include a communications interface (preferably compatible with a telecommunications network) to allow software and data to be transferred to, from, or between the computer system and one or more external devices. Examples of communications interfaces include modems, a network interface (such as, for example, an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via communications interface will be in the form of signals that can be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface. These signals are usually provided to communications interface via a channel that carries signals and can be implemented using a wireless medium, wire, cable, fiber optics, or other communications medium. Some examples of a channel include a phone line, a cellular phone link, an RF link, a network interface, and other communications channels.

In this document, the terms "computer program product" and the like generally refer to media such as removable storage device, a disk capable of installation in disk drive, and signals on channel. These computer program products provide software or program instructions to the computer processor(s). Computer programs (also called computer control logic) are usually stored in a main memory and/or secondary memory. Computer programs can also be received via a communications interface. Computer programs, when executed, enable the computer system to perform the features of the present invention as described herein. In particular, the computer programs, when executed, enable the processor(s) to perform the features of the present invention. Accordingly, computer programs represent controllers of the computer system.

In embodiments where the invention is implemented using software, the software may be stored in, or transmitted via, a computer program product and loaded into computer system using any suitable device or communications interface. The control logic (software), when executed by the processor(s), causes the processor to perform the functions of the invention as described herein. In other embodiment, the methods of the invention implemented primarily in hardware, or a combination of hardware and software, using, for example, hardware components such as PALs, application specific integrated circuits (ASICs), or other hardware components. Implementation of a hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

According to an embodiment of the invention, a system is provided to facilitate the storing of patient related information obtained during diagnosis, prognosis, triaged or risk stratification of patients with acute dyspnea with or without CHF into a database. The system comprises computer readable memory that stores a database, e.g., the database referred to above, including data described herein, e.g., relating to patients treated by the diagnostic facility, data relating to events occurring with respect to patients treated by the facility and etc. The system also includes at least one computer which receives data entered by an input device, e.g., a keyboard and/or a pointing device such as a mouse, via a user interface displayed on a display device. The user interface receives data identifying at least one event, data identifying at least one calendar time period and data identifying a cohort including a plurality of patients and the at least one computer accesses the memory in response to received data entered by the input device and provides statistical data that is presented on the display device.

In an embodiment, the system described above may be provided to operate over a network. In this embodiment, at least one network computer and at least one other computer are provided. The at least one network computer is capable of accessing the memory and providing the statistical data. The at least one other computer provides information to the at least one network computer and receives statistical data from the at least one network computer via the network. The user interface is provided at the at least one computer for receiving the data entered by the input device, the display device is provided at the at least one computer and the at least one network computer provides the statistical data via the network to the at least one computer.

In an embodiment, a system user inputs, e.g., via a user interface such as a browser, e.g., a standard web browser such as Microsoft Internet Explorer, or a thin client interface, or other user interface, values or parameters to focus the analysis in an area of interest. An embodiment of the inventive system queries data records in a database, preferably in real time, to obtain the records and other data associated with patients falling within the user specified values or parameters, which are provided to the user, e.g., in a selectable or pre-set data representation format.

In an embodiment, a system incorporating the invention comprises a database capable of storing data of the type disclosed herein which can be accessed by one or more computers and/or electronic devices via a network, e.g., a LAN, WAN, intranet or the Internet. A server or host or other computer or computers may be provided to manage and/or control the database, and/or run analytics and/or algorithms with respect to data stored in the database and/or provide data to a computer or electronic device which accesses the network, e.g., on pull and push bases.

In an embodiment, a user friendly interface provides a formal query in a computer language (e.g., SQL) to extract the records and variables of patients in response to data input by a user. For example, data may be input to templates or charts provided by a user interface such as a browser. Programming converts the data to a form, e.g., an SQL query, that may be transferred to a server or host, e.g. , a statistical server. Statistical servers are commercially available, and programming and algorithms implementing functionality disclosed herein in a statistical server may provide, e.g., analytics, data, reports disclosed herein.

A remote access device embodied by the present invention may be any remote device capable of transmitting and/or receiving data from diagnostics facility such as, for example, a personal computer, a wireless device such as a laptop computer, a cell phone or a personal digital assistant (PDA), or any other suitable remote access device. Multiple remote access devices may be included in the systems of the claimed invention (e.g., to allow a plurality of physicians or other individuals at a corresponding plurality of remote locations to communicate data with diagnostics facility). A diagnostics facility may include a server capable of receiving and processing communications to and/or from remote access device. Such a server may include a distinct component of computing hardware and/or storage, but may also be a software application or a combination of hardware and software. The server may be implemented using one or more computers.

Each of communications links may be any suitable wired or wireless communications path or combination of paths such as, for example, a local area network, wide area network, telephone network, cable television network, intranet, or Internet. Some suitable wireless communications networks may be a global system for mobile communications (GSM) network, a time-division multiple access (TDMA) network, a code- division multiple access (CDMA) network, a Bluetooth network, or any other suitable wireless network.

It should be understood that the above-described embodiments and the following examples are given by way of illustration, not limitation. Various changes and modifications within the scope of the present invention will become apparent to those skilled in the art from the present description.

EXAMPLES The following examples illustrate various methods for preparing liposome compositions and using the compositions in the treatment method of the invention. The examples are intended to illustrate, but in no way limit, the scope of the invention. Methods

I. The Pride Study

The institutional review board approved all investigational procedures involved in this study. The design and results of the PRIDE Study were recently reported (Januzzi, J. L. et al, Am. J. Cardiol, 95:948-954, 2005). Briefly, 599 patients who presented to an urban ED with complaints of dyspnea were enrolled in a blinded study that evaluated the role of NT-proBNP testing for the diagnosis of acute CHF. At 60 days following enrollment, a follow-up telephone call was made to the participants and/or their physicians to review subsequent medical course following presentation. Study physicians (blinded to the results of NT-proBNP testing) assigned a final diagnosis for each patient using all clinical information available from the time of presentation through the 60-day follow-up period, including the results of telephone contact with each participant, as previously described (Januzzi, J. L. et al., Am. J. Cardiol, 95:948-954, 2005). The diagnosis of acute CHF was based on clinical records and results of direct contact with patients and their caregivers.

Of the 599 patients in the study, 209 (35%) were judged to have acute CHF, whereas 390 were not. Of those who did not have acute CHF, 150 had exacerbation of obstructive airway disease, 64 had pneumonia, 31 had acute coronary syndrome (ACS), 19 had acute PE, and 10 had acute bronchitis. In an additional 116 participants, various other diagnoses were recorded, including anxiety attacks, allergic reactions, and ascites.¹

II. NT-proBNP Analysis

NT-proBNP analysis was performed using a commercially-available immunoassay (Elecsys proBNP assay; Roche Diagnostics, Indianapolis, IN) on an Elecsys 1010 analyzer (Roche Diagnostics), using established methods. This assay is reported to have less than 0.001% cross-reactivity with B-type natriuretic peptide.

In the PRIDE Study, it was determined that the optimal strategy for identifying the presence of acute CHF using NT-proBNP measurement was an age-stratified approach, with a diagnostic threshold of 450 pg/mL for patients younger than 50 years and 900 pg/mL for patients 50 years or older. The manufacturer's recommended cut points of 125 and 450 pg/mL (for ages <75 or >75 years) for excluding CHF had a strong negative predictive value (NPV) of 100%, whereas a more simplified, age-independent cut point of 300 pg/mL had an NPV of 99.7%. For the purposes of the study, vital status at 1 year was ascertained by study physicians via telephone interview of the primary care physician or cardiologist for each study participant and/or review of hospital medical records. Physicians obtaining follow-up data were blinded to the results of cardiac biomarker testing. Of the 599 study participants, follow-up was complete in 595 (99.3%). Those lost to follow-up were assumed to be alive.

III. Receiver Operating Characteristic Analyses

Receiver operating characteristic (ROC) analyses were performed in an effort to assess the relationship between NT-proBNP concentration and 1-year mortality. Once an optimal cut point for predicting mortality was identified, sensitivity, specificity, positive predictive value (PPV), and NPV were generated. The ROC analyses were performed with Analyze-It software (Leeds, England).

IV. Statistical Analyses

Comparisons of baseline clinical characteristics between survivors and nonsurvivors were performed using x² or Fisher exact tests for categorical data and t tests or Wilcoxon rank sum tests for continuous data, as appropriate. The NT-proBNP concentrations were expressed as medians and interquartile ranges. To identify independent predictors of death at 1 year following presentation with dyspnea, age-adjusted Cox proportional hazards regression models were first performed to examine clinical variables of interest, with 1-year mortality as the dependent variable. All covariates associated with 1-year mortality with age-adjusted P<0.05 were potentially eligible for inclusion in the final multivariable Cox model. The final multivariable model was selected using stepwise Cox regression with P<0.05 as the cutoff for retention in the model. The results of the final multivariable model were presented. The variables in the final multivariable model were assessed in pairs for all possible first-order interactions and found to have none. Kaplan-Meier survival curves were plotted, and groups were compared using the log-rank test. Statistical analyses were performed with the use of Stata software, version 8SE (Stata Corp, College Station, TX). A 2-sided P<0.05 was considered statistically significant. V. Patient Characteristics

Of the original cohort of 599 dyspneic study participants, 91 (15.2%) died within 1 year of enrollment. All but 5 participants (94.6%) who died at 1 year were admitted to the hospital at index presentation (compared with an overall 75.8% admission rate in the PRIDE Study), but of these 91 decedents, only 14 (15.4%) died during index hospitalization.

Of those patients who died by 1 year, 55 (60%) had acute CHF at presentation, whereas 36 (40%) did not. The discharge diagnoses of the 36 patients without acute CHF who died by 1 year included pulmonary thromboembolism (8.3%), chest pain or ACS (11.1%), bacterial sepsis (11.1%), carcinoma (11.1%), pneumonia (16.6%), and exacerbation of obstructive airway disease (22.2%), whereas other single diagnoses accounted for the cause of death in the remaining 7 patients (19.4%). Of the 36 patients without acute CHF who died by 1 year, 5 (13.8%) had had prior CHF.

Given the high percentage of decedents with CHF, it is not surprising that compared with those surviving, those who died were older and more likely to have prior structural heart disease (including cardiomyopathy or prior CHF), to have more prevalent symptoms or signs consistent with CHF, to be using medications for CHF management such as diuretics or digoxin, and to have physical examination and radiographic results consistent with CHF (see Table 1). Laboratory values of patients who died by 1 year were notable for significantly worse renal function.

Table 1. Characteristics of Dyspneic Patients Presenting to the Emergency Department According to Survival Status at 1 Year*

Abbreviations. IQR, interquartile range, NT-proBNP. amino-terminal pro-brain natriuretic peptide.

SI conversion factors: To convert creatinine to micromoles per liter, multiply by 88.4; creatinine clearance to milliliters per second, multiply by 0.0167, and urea nitrogen to millimoles per liter, multiply by 0.357 *D ata are ρresented as percentage of patients unless otherwise indicated. ■ Calculated as weight in kilograms divided by the square of height in meters A total of 139 patients (23.2%) underwent echocardiography as the standard of care at a mean of 51 hours following presentation. In this selected patient population, there were no significant differences in echocardiographic results between those dying and those surviving at 1 year (data not shown), with the exception of a relationship between ejection fraction and mortality.

Among those who died by 1 year, the median NT-proBNP concentration at presentation was 3277 pg/mL (interquartile range, 1086-9868 pg/mL), which was significantly higher than the NT-proBNP concentration in those surviving at 1 year, which was 299 pg/mL (interquartile range, 71-1807; P<.001 for difference).

VI. Roc Curves

The ROC analyses were perforated to assess the ability of NT-proBNP to predict mortality at 1 year in all patients. The NT-proBNP concentration was both sensitive and specific, reflected in the area under the ROC curve (AUC) of 0.76 (FIGURE 1; P<0.001). The cut point that yielded 90% sensitivity was 120 pg/mL, which had a PPV of 20.1 % and an NPV of 94.6%. The cut point that yielded 90% specificity was 6899 pg/mL, which had a PPV of 41.2% and an NPV of 88.7%. The cut point that offered the best balance of sensitivity and specificity for predicting 1-year mortality among the entire cohort of dyspneic patients in the PRIDE Study was 986 pg/mL, which was 79% sensitive and 68% specific, with a PPV of 31 % and an NPV of 95 % .

VII. Mortality Rate as A Function Of Increasing NT-ProBNP Concentration

Following stratification by NT-proBNP deciles, the percentage of patients who died by 1 year was assessed (FIGURE 2). For participants with levels below the seventh decile of NT-proBNP (representing a cutoff value of 972 pg/mL), mortality rates were low; however, at this point, a threshold effect for mortality was observed. Above this threshold, there was a marked increase in mortality (P<0.001 for trend), which was particularly great for participants with the highest levels of NT-proBNP at presentation. VIII. Independent Predictors of Mortality at 1 Year

Results of the multivariable model predicting 1-year mortality are given in Table 2.

Table 2. Multivariate Predictors of Death by 1 Year Following Emergency Department Presentation with Dyspnea

Abbreviations: CI, confidence interval; HR, hazard ratio; NT-proBNP, amino- terminal pro-brain natriuretic peptide; NYHA, New York Heart Association. Per increase in classification.

An NT-proBNP concentration greater than 986 pg/mL was the single strongest predictor of mortality at 1 year (hazard ratio [HR], 2.88; 95% confidence interval [CI], 1.64-5.06; P<.001). Among the factors in the model predictive of death at 1 year were age, heart rate, blood urea nitrogen level, systolic blood pressure less than 100 mm Hg, heart murmur on examination, and New York Heart Association classification at presentation. Neither hospitalization at index presentation nor intervening clinical events during index hospitalization (such as ACS) influenced the powerful prognostic impact of an elevated NT- proBNP concentration at presentation. As a single variable, an NT-proBNP concentration greater than 986 pg/mL alone had an AUC of 0.76 for predicting death; combining each of the other significant covariates without NT-proBNP results yielded a model with an AUC of 0.80. The final, model combining NT-proBNP results with other covariates yielded a superior AUC (0.82); this final model had a likelihood ratio X₇ ² of 104.89, yielding a highly statistically significant P value (<0.001) for the fit of the model. The pseudo R² for this model was 0.21.

To determine whether any echocardiographic variables were significant predictors of 1 -year mortality in addition to the covariates given in Table 2, a second analysis was performed. In this analysis, restricted to the 139 participants who underwent echocardiography during the index hospitalization (of whom 19% died by 1 year), an NT- proBNP concentration greater than 986 pg/mL (HR, 7.12; 95% CI, 1.67-30.4; P=.OO8), blood urea nitrogen decile. (HR, 1.29; 95% CI, 1.01-1.66; P=0.04), and ejection fraction decile (HR, 1.03; 95% CI, 1.00-1.06; P=0.04) were all significant predictors of 1-year mortality, with systolic blood pressure less than 100 mm Hg of borderline significance (HR, 2.66; 95% CI, 0.89-7.91; P=.08).

The 1-year crude mortality rates for patients with an NT-proBNP concentration greater than 986 pg/mL were strikingly higher than those of participants with NT-proBNP concentrations of 986 pg/mL or less, in patients both without acute CHF (30.2% versus 5.9%; P<.001) and those with acute CHF, in whom the mortality rate of those with concentrations below 986 pg/mL was 0% at 1 year (versus 29.9% for those with concentrations above 986 pg/ mL; P<0.001).

Notably, among those 53 patients without acute CHF whose NT-proBNP concentration was above 986 pg/ mL, 18 (34%) had had prior CHF. As noted herein, of these 18 patients, 5 had died by 1 year, accounting for only 13.8% of decedents without acute CHF but with elevated NT-proBNP concentrations.

Age-adjusted 1-year survival curves are shown in Figure 3, stratified by NT- proBNP concentration for those with and without CHF at ED presentation. As depicted, increased mortality risk associated with high NT-proBNP concentration was observed in participants both with and without acute CHF. The risk of mortality appeared early following presentation and remained constant throughout the entire year of follow-up, as demonstrated by the continuously diverging event rates in patients both with and without acute CHF at presentation. Notably, no patients with acute destabilized CHF and an NT- proBNP concentration of 986 pg/ml or less died by I year. In the cohort of patients who presented with dyspnea to the ED, an elevated

NT-proBNP concentration was observed at presentation which was strongly predictive of death by 1 year, whereas lower concentrations of NT-proBNP had a high NPV for excluding risk of death by 1 year. Although natriuretic peptides have been shown to predict prognosis in healthy individuals,⁴ those with chronic CHF,^5"9 and patients with a wide variety of medical conditions other than CHF,^10"17'¹⁹ (Weber, M. et al, Am. Heart J., 148:612-620, 2004; Jernberg, T. et al., J. Am. Coll. Cardiol., 42:1909-1916, 2003), the role of natriuretic peptide testing for predicting long-term prognosis in patients who present with dyspnea to the ED is unknown. Since evaluation of the dyspneic patient represents the area of heaviest use of natriuretic peptide measurement, demonstration of an association between natriuretic peptide concentrations and long-term risk of mortality in patients with undifferentiated dyspnea is thus particularly important. To the inventors' knowledge, such an association has not previously been reported. Above an NT-proBNP cut point value (986 pg/mL), comparable to the optimal cut points in the PRIDE Study for the diagnosis of acute CHF, the risk of mortality at 1 year began to rise significantly. This risk was observed independent of a diagnosis of acute CHF. These findings, thus, establish the prognostic utility of natriuretic peptide testing for the dyspneic patient; not only is natriuretic peptide testing useful for the diagnosis, triage and management (Mueller, C. et al, N. Engl. J. Med., 350:647-654, 2004) of patients with dyspnea, but also the results of the same blood sample used for diagnosis in the ED may now be applied to risk-stratify patients with respect to likelihood of death in the ensuing year.

The previous experience examining the association between natriuretic peptide values and outcomes among dyspneic patients has largely been restricted to studies with shorter follow-up and included outcomes other than mortality (Maisel, A. et al, J. Am. Coll. Cardiol, 44:1328-1333, 2004; Harrison, A. et al., Ann. Emerg. Med.,39: 131-138, 2002) or examined groups of patients consisting solely of those with acute CHF, in whom NT-proBNP concentrations at presentation did not predict death (Betteneourt, P. et al, Circulation, 110:2168-2174, 2004; O'Brien, R. J. et al, Eur. J. Heart Fail, 5:499-506, 2003). This study benefited from a large sample size and inclusion of dyspneic patients without acute CHF, and notably, our follow-up period extends to a year, which is considerably longer than prior studies of B-type natriuretic peptide in dyspneic patients. In this context, the inventors were able to demonstrate a remarkably robust association between presenting NT-proBNP levels and the risk of death in the year following presentation. A recent analysis by Fonarow, G. C. et al. {JAMA, 293:572-580, 2005) of patients with acute CHF demonstrated the important association between abnormal renal function, low systolic blood pressure, and risk of death. However, that study examined only patients with CHF, and natriuretic peptide results were not included in their analysis. Although our study confirms their findings, it also establishes the independent and additive importance of NT-proBNP for predicting death in dyspneic patients, regardless of diagnosis of CHF at presentation. In our sample, the AUC was 0.64 for a model that contained the same covariates described by Fonarow and colleagues (blood urea nitrogen level 243 mg/dL [15.35 mmol/L] and systolic blood pressure <115 mm Hg), compared with an AUC of 0.82 (P<.001 for differences in models) when an NT-proBNP concentration greater than 986 pg/mL was added to the analysis.

The risk of death by 1 year associated with an elevated NT-proBNP was not simply related to the unfavorable prognosis connected to a diagnosis of acute or chronic CHF. Whereas patients with acute CHF accounted for 60% of patients who died by 1 year, 40% of those who died by 1 year had diagnoses other than acute CHF, and the prognostic utility of an elevated NT-proBNP concentration was useful in these patients as well. Considering that NT-proBNP concentration is expected to be elevated in those with prior heart failure, we examined the event rates in patients with chronic CHF who presented with dyspnea from another cause in the PRIDE Study, and those patients accounted for only

13.8% of the deaths in the non-acute CHF group. Thus, some other diagnosis was operative in most patients without acute CHF who died. The importance of NT-proBNP for the prediction of death in patients without CHF likely reflects the value of this marker for predicting mortality among dyspneic patients with diagnoses other than CHF, such as coronary artery disease¹⁰'¹³'¹⁷'²⁴'²⁵ or PE,²⁰'²¹ which may present in a manner similar to acute CHF. It is also worthwhile to point out the value of NT-proBNP for the exclusion of risk of mortality by 1 year, since an NT-proBNP concentration of 986 pg/mL or less was valuable for identifying an extremely low risk of mortality. In this sample, not a single participant with acute CHF with an NT-proBNP concentration of 986 pg/mL or less at presentation had died at 1 year.

A major strength of our study is that outcomes in the trial were not confounded by knowledge of natriuretic peptide concentrations in our study participants because the treating physicians were blinded to the results of NT-proBNP testing, and routine natriuretic peptide testing was not performed at our institution at the time of the trial or the subsequent follow-up period. Our study is limited in that we have results only of NT- proBNP testing from presentation and were not able to evaluate the risk of mortality with respect to serial measurements of NT-proBNP, as has been previously described for patients with acute CHF²⁵'²⁹ or ACS.¹² However, the value of routine serial natriuretic peptide measurements for the diagnosis or management of the dyspneic patient has not been described; such a strategy remains much less established than the value of natriuretic peptide testing at presentation for diagnosis and triage purposes, 2 uses clearly established in several trials.^1"3'²⁶ Also, although the prognostic impact of an elevated NT-proBNP concentration appeared to be independent of a diagnosis of acute (or prior) CHF in breathless patients, the relatively small number of deaths in the non-CHF group is a limitation.

In summary, NT-proBNP testing to be a valuable prognostic tool for the identification of a high risk of mortality by 1 year following presentation to the ED with dyspnea with or without CHF. Because evaluation of the dyspneic patient represents the most common indication for natriuretic peptide testing, our results are of particular importance. In light of the value of NT-proBNP testing for the diagnosis,¹'³ triage,² management,²⁶ and now long-term prognostic evaluation of dyspneic patients, we recommend that routine NT-proBNP testing for dyspneic patients in the ED be strongly considered. For those with elevated NT-proBNP concentrations, a diagnostic workup for the deleterious diagnoses known to elevate concentrations of NT-proBNP (notably including acute CHF, ACS, and PE) with appropriate diagnosis-specific therapeutic efforts is advisable.

IX. Derivation and Validation of a Clinical and Biochemical Score for Risk Stratification of Patients with Acute Dyspnea

A. The PRIDE Study: Patient Cohort

The patients studied for this analysis were derived from a prior study of NT- proBNP testing for the evaluation of dyspneic patients, the ProBNP Investigation of

Dyspnea in the Emergency Department (PRIDE) study. The design, results, and conclusions of this study have been described. Januzzi, J. L. et al, Am. J. Cardiol, 95(8):948-954, 2005. Briefly, 599 consecutive dyspneic patients were enrolled in a trial examining the value of NT-proBNP testing compared to clinical judgment for the identification of ADHF. Exclusion criteria for the study included: age <21 years, severe renal insufficiency (defined as a serum creatinine >2.5 mg/dl), dyspnea following chest trauma, dyspnea secondary to severe coronary ischemia (identified with >0.1 mV ST segment elevation or ST segment depression on 12 lead electrocardiogram, if performed on presentation), >2 hour time delay following urgent intravenous loop diuretic administration (above any baseline maintenance dose), and un-blinded natriuretic peptide level measurement. Clinical data and a blinded NT-proBNP level were obtained for each study participant. Study physicians (blinded to the results of NT-proBNP testing) assigned a final diagnosis for each patient. As previously described, this diagnosis relied on all available clinical information available from the time of enrollment through the 60-day follow-up period. Januzzi, J. L. et al., Am. J. Cardiol, 95(8):948-954, 2005.

B. Present Study For the purposes of the study, vital status at 1 year was ascertained by study physicians via a telephone interview of the primary care physician/cardiologist for each study participant and/or by review of hospital medical records. Physicians obtaining follow- up data were blinded to the results of cardiac biomarker testing. In this analysis we dichotomized patients by NT-proBNP status (either < or >986 pg/mL) as this has previously been shown to be the optimal cut off for 1-year mortality prediction (Januzzi, J. L. et al., Arch. Intern. Med., 166(3):315-320, 2006). Those lost to follow-up were assumed to be alive. Of the 599 participants in the PRIDE study, follow-up data regarding one-year mortality status were available in 595 (99.3%).

Univariable logistic regression was performed to screen each variable as a potential independent predictor of 1-year mortality. Those factors associated with 1-year mortality in a statistically significant fashion were then entered into a multivariate regression model as described below. For covariates retained in the final regression model, calculated β-coefficients were used to determine an appropriate weight for each factor in the final scoring tool.

C. Statistical Analysis

All 38 clinical and biochemical factors assessed at the time of index presentation were considered as potential candidates for score inclusion. To identify independent predictors of death at 1 year following presentation, age-adjusted Cox proportional hazards regression models were first performed to examine clinical variables of interest with 1-year mortality as the dependent variable. In this univariable analysis, all factors associated with 1-year mortality with age-adjusted P-values <0.05 were considered for inclusion in the final multivariable Cox model. Components of the multivariable model were selected using stepwise Cox regression with P-values <0.05 as the cutoff for retention in the model. Covariates retained in the final model comprise the individual components of the PRIDE Mortality Score. The results of the final multivariable model are presented. The variables in the final multivariable model were assessed in pairs for all possible first-order interactions and found to have none. Statistical analyses were performed with the use of Stata software, version 8SE (Stata Corp, College Station, Tex). A 2-sided P-value <0.05 was considered statistically significant.

D. Assessment of Score Scores were calculated for all patients with complete follow-up data. The observed prevalence of death by 1 year in the cohort was assessed for each calculated score value. Observed mortality rates were then compared to score value quintiles. Receiver operating characteristic (ROC) analyses were performed to assess the relationship between observed death by 1 year and calculated absolute score value. Score value cut points both for the prediction of death (maximized positive predictive value) and survival (maximized negative predictive value) were determined. We then examined the distribution of score values compared with observed mortality in patients with dyspnea attributable to ADHF and in dyspnea explained by an alternative diagnosis. Finally, the PRIDE Mortality Score was applied to a cohort of dyspneic patients previously enrolled in a New Zealand ED-based trial performed to evaluate the diagnostic role of natriuretic peptides in patients with dyspnea. Lainchbury, et al., J. Am. Coll. Cardiol., 42(4):728-735, 2003.

E. Results

Patient Population

Among the study subjects, dyspnea was attributed to the following diagnoses: ADHF (209/595, 35.1%), chronic obstructive pulmonary disease or asthma (150/595, 25.2%), pneumonia (64/595, 10.8%), acute coronary syndromes (31/595, 5.2%), pulmonary embolism (19/595, 3.7%), acute bronchitis (10/595, 1.7%), and other (116/595, 19.5%). An overall 1-year mortality rate of 15.2% (91/595) was observed. Of those deceased by one year, 55 (60%) had ADHF at the time of index presentation and 36 (40%) did not. In the 40% without ADHF, dyspnea was attributable to the following alternative causes: exacerbation of chronic obstructive airway disease (22.2%), pneumonia (16.6%), chest pain or acute coronary syndromes (11.1%), sepsis (11.1%), neoplastic disease (11.1%), pulmonary embolism (8.3%), and other single diagnoses (19.4%). Of the 36 patients with non-ADHF causes of dyspnea, 5 (13.8%) had had prior heart failure. Univariable Analyses

As previously reported, patients deceased by 1-year were more likely to be older age (mean age of deceased = 72±14 vs. mean age alive = 61±17, p<0.001), to have a prior history of heart failure, hypertension, or diabetes, and to have presented with several distinct signs or symptoms including orthopnea, lower extremity swelling, nocturnal cough, or dyspnea at rest. The use of specific medications including loop diuretics and digoxin was also more common in those deceased at 1 year than in survivors. On physical examination, those deceased were more likely to have had lower body mass indexes, faster heart rates, lower systolic and diastolic blood pressures, elevated jugular venous pressures, heart murmurs, lower extremity edema, and pulmonary rales. Both interstitial edema and pleural effusion on chest radiograph were statistically more common in those deceased by 1 year compared with survivors as were lab parameters including reduced renal function and higher NT-proBNP levels.

Multivariate Analysis & Score Derivation

Of the 26 candidate variables significantly associated with death by 1 year after univariable analysis, 7 remained independently associated with this outcome after multivariable analysis (Table 3). As previously reported, an NT-proBNP level of >986 pg/mL most strongly predicted death by 1 year. Additional factors identified by multivariable regression included age (by decade), heart rate (by decile), blood urea nitrogen level (by decile), systolic blood pressure <100 mmHg, the presence of a heart murmur, and NYHA classification. These 7 variables form the set of predictor variables that comprise the final PRIDE Mortality Risk Score, β-coeffϊcients generated by the regression model were used to determine the appropriate relative score value for each variable as follows: age (multiplied by 0.7), heart rate (multiplied by 0.5), blood urea nitrogen (multiplied by 0.5), New York Heart Association class (multiplied by 5), NT-proBNP > 986 pg/mL (18 points), systolic blood pressure < 100 mmHg (11 points), and presence of a murmur (11 points). Table 3. Multivariate Predictors of Death by 1-Year Following Emergency Department Presentation with Dyspnea.

Abbreviations: CI=Confidence Interval, NT-proBNP= Amino-terminal pro-B-type natriuretic peptide, BUN=blood urea nitrogen, SBP=systolic blood pressure, NYHA=NeW York Heart Association

Score Performance

The distribution of the PRIDE Mortality Score among patients in the entire derivation cohort is demonstrated in FIGURE 5. Score values ranged from 20.8 to 119.3 points. The overall mean score in the cohort was 66.4 ± 19.5, with a median score of 63.1 points (interquartile range= 50.7 to 82.5 points). Score distributions in patients with dyspnea due to ADHF were influenced by the dominant variable of NT-proBNP; thus, patients with ADHF had a higher mean score value (83.7±13.3 points) than those with non-ADHF causes of dyspnea (mean score = 57.0 ± 15.1 points, p-value <0.001 for difference), however the range of scores was similar when considering subjects with ADHF (score range 37.5 to

119.3 points) or those dyspneic subjects without ADHF (score range 20.8 to 116.5 points).

ROC curve analysis of the scores accuracy to predict death produced an area under the curve (AUC) of 0.82 (95% CI= 0.78-0.85; p<0.001). Subjects presenting with ADHF had higher scores and subsequent higher rates of one-year mortality when compared to those with alternative causes of dyspnea. However, the scoring tool performed well both in subjects with ADHF (AUC=0.73, 95% CI=0.67-0.79; p<0.001) and in those with alternative causes of dyspnea (AUC=0.83, 95% CI=0.77-0.85; p<0.001).

Following division of subjects into score quintiles, we then examined characteristics of subjects as a function thereof (Table 4). The sensitivity, specificity, PPV and NPV of each score quintile is also demonstrated, illustrating the continuous nature of the score with respect to risk prediction (Table 5). Table 4: Characteristics of Study Subjects as A Function of PRIDE Risk Score Quintile.

P values are chi-square for categorical and Kruskal-Wallis for continuous.

Table 5: Sensitivity, Specificity, Positive and Negative Predictive Value of Each Score Quintile for Death at One Year Following Presentation with Dyspnea.

Abbreviations: PPV=positive predictive value, NPV=negative predictive value

The rates of death by 1-year as a function of score quintile are depicted in

FIGURE 6. With each successive higher score quintile, we observed a continuous rise in the risk for mortality with the highest risk of death by 1-year in the highest score quintile (n=l 16, score > 85.5) with a one-year mortality rate of 43.1% compared to those in the lowest score quintile (n=l 18, score ≤ 48.5) with a mortality rate of only 1.7% (p<0.001) (FIGURE 6). The higher rates of death associated with higher score quintiles appeared early and were continuously higher from presentation to one year of follow up (FIGURE 7).

Validation

Of the 205 patients previously reported in a natriuretic peptide diagnosis trial from Christchurch New Zealand, 172 had data defining 1-year mortality status. When compared to the patients in the PRIDE study, patients from the Christchurch dataset were more likely to be older (mean age 70.5 versus 63.3 years, p<0.001), however, there were no significant differences between the PRIDE and Christchurch patients with respect to numerous other characteristics known to affect NT-proBNP levels, such as gender, body- mass index, left ventricular ejection fraction (when known), serum creatinine level, or a history of prior CHF or left ventricular dysfunction (all p=NS). When applied to the cohort of patients from Christchurch, a rising PRIDE

Mortality Score was associated with a higher risk of death by 1-year (FIGURE 8). With subdivision by score quintile, we observed a continuous rise in the risk for mortality with the highest risk of death by 1-year in the highest score quintile (n=32, score > 85.5) with a one- year mortality rate of 50.6% compared to those in the lowest score quintile (n=12, score ≤ 48.5) with a mortality rate of 9.1% (p<0.001).

Discussion

The present invention relates to a development and validation of a simple method for estimating likelihood for death by 1 year following ED presentation with dyspnea. This risk prediction tool unites findings from the standard clinical evaluation with the results of NT-proBNP testing and can be used in a diagnosis-independent fashion. Prior to this report, quantitative risk stratification of the acutely dyspneic patient has relied on an accurate determination of the causal diagnosis. For several common disease entities, including the acute coronary syndromes, pulmonary thromboembolic disease, community acquired pneumonia, and chronic obstructive lung disease, quantitative methods of risk stratification have been developed. Fine, M. J. et al., N. Engl. J. Med.,

336(4):243-250, 1997; Celli, B.R. et al, N. Engl. J. Med., 350(10): 1005-1012, 2004;

Aujesky, D. et al., Arch. Intern. Med., 166(2) : 169- 175 , 2006; Boersma, E. et al., Circulation, 101(22):2557-2567, 2000; Antman, E.M. et al, JAMA, 284(7):835-842, 2000. Unfortunately, the initial evaluation of the acutely dyspneic patients often yields significant diagnostic uncertainty thereby invalidating these diagnosis-specific risk stratification methods, which by definition require a diagnosis prior to stratification of risk. Thus, symptom-specific, rather than diagnosis-specific risk models would be expected to be of greater use in the ED setting.

NT-proBNP has been shown to be an important component of risk stratification in numerous clinical situations including pulmonary thromboembolic disease, stable atherosclerotic vascular disease, valvular heart disease, and chronic heart failure. Kucher, N. et al, Circulation, 107(12): 1576-1578, 2003; Kragelund, C. et al, N. Engl J. Med., 352(7):666-675, 2005; Blankenberg, S. et al, Circulation, 114(3):201-208, 2006; Richards, A.M. et al, J. Amer. Coll Cardiol, 47(l):52-60, 2006; Weber, M. et al, Eur. Heart J., 26(10):1023-1030, 2005; Hartmann, F. et al, Circulation, 110(13): 1780-1786 2004. In a recently published study, the inventors have demonstrated that NT- proBNP was a powerful predictor of mortality in patients presenting with dyspnea independent of their diagnosis at the time of assessment. Januzzi, J. L. et al, Arch. Intern. Med., 166(3):315-320, 2006. In an effort to characterize the additive value of NT-proBNP testing and clinical factors for risk stratification of dyspneic patients, we simultaneously analyzed a large number of other candidate variables for mortality risk prediction. Though NT-proBNP had the strongest association with mortality status by 1 year, additional factors as described above retained independent association with hazard. A mortality prediction model combining NT-proBNP with these 6 additional covariates (age, HR, systolic blood pressure, serum creatinine concentration, the presence of a cardiac murmur, and NYHA class) served as the optimal model for prediction of death, and is intuitively more useful, as it combines several commonly-gathered (and complementary) factors from standard clinical evaluation, including history, physical examination, and laboratory assessment. This model serves as the basis for the PRIDE Mortality Score that is now presented.

The applicability of this mortality score is substantial as it allows clinicians to determine risk of death by 1 year in all patients with dyspnea even when a causal diagnosis is not made with certainty. Such a symptom-specific approach for risk stratification is less dependent on certainty of diagnosis. This would, in particular, assist in triage decision- making, such that patients at highest risk would potentially merit higher levels of monitoring, diagnostic evaluation, and therapeutic intervention.

In addition, we have demonstrated the portability of this risk prediction tool by applying it to a demographically distinct group of acutely dyspneic patients. Though individuals in the validation cohort were older and consequently had higher absolute score values, the PRIDE Mortality Risk Score retained its relative accuracy in predicting risk of death by 1 year in this group.

A similar method for risk stratification of patients with ADHF was recently reported by Fonarow and colleagues, who generated an in-hospital mortality risk prediction tool, relying on several readily available clinical variables (age, heart rate, systolic blood pressures, and blood urea nitrogen level), for patients admitted with a principle diagnosis of ADHF.⁹ Though the PRIDE Mortality Score also incorporates similar clinical parameters, our model differs importantly in several ways. First, unlike the tool reported by Fonarow and colleagues, the risk score reported here includes natriuretic peptide measurement which, as previously discussed, has emerged as a powerful predictor of mortality in dyspneic patients. Next, the PRIDE Mortality Score is designed to be applied to individuals identified by chief complaint on presentations (acute dyspnea) rather than by diagnosis thereby eliminating the uncertainty of how to risk stratify patients with no clear causal diagnosis. Finally, our model is design as a longer-term risk prediction tool that can be used to guide prognostication far beyond the index hospitalization.

Several important uncertainties about the PRIDE Mortality Score exist. It is beyond the scope of the data here presented to make conclusions about the utility of the score in guiding treatment and triage decisions, as these were not recorded. However, it is intuitive to conclude that a quantitative measure of mortality risk will be a useful tool in determining the type(s) and intensity of therapy, the value of hospital admission, and the necessity and timing of post-emergency room follow-up. Further, the value of natriuretic peptide measurement for the triage and management of patients is now established. Mueller, C. et al, N. Engl. J. Med., 350(7):647-654, 2004. While the positive predictive value of NT- proBNP alone is significant, it remains limited nonetheless; with the addition of clinical variables predictive of hazard, the value of natriuretic peptide testing for correctly identifying risk is strengthened, as previously shown for diagnostic application. Baggish, A. L. et al, Am. Heart J., 151(l):48-54, 2006. Lastly, the AUC for the risk score was only 0.82; however, this compares very favorably with other tools for hazard prediction such as the ADHF risk stratification model of Fonarow and colleagues as well as others currently in widespread clinical use, such as the Thrombolysis in Myocardial Infarction Risk Score. Fonarow, G.C. et al, JAMA, 293(5):572-580, 2005; Antman, E. M. et al, JAMA, 284(7):835-842, 2000; de Araujo Goncalves, P. et al, Eur. Heart J., 26(9):865-887, 2005. In conclusion, the inventors present the PRIDE Mortality Score as an accurate method of prognosis prediction in patients presenting with dyspnea. This is the first available quantitative method for mortality risk prediction in patients with dyspnea allowing for the division of patients into varying levels of risk, even within this generally higher risk patient population. The inventors have previously demonstrated the value of NT-proBNP testing in the context of clinical evaluation to improve the positive predictive value for diagnosis and now demonstrate the power of similar strategy for mortality prediction.

X. The PRIDE PDA Program

The Welcome Screen of the PRIDE PDA Program allows the operator to directly select any of the three entry choices: PRIDE CHF Calculator, Clinical Algorithm or Information Components of the Program. To choose the PRIDE CHF Calculator, the operator simply taps the selected portion of the Welcome Screen which will take the operator to a screen shot that displays the components of the PRIDE Score. By tapping on the selected box, an individual component is checked on the screen along with the associated points added to the total score at the bottom of the screen. Each component of the PRIDE score has been assigned to an associated point. If all boxes are checked, the total PRIDE score of 13 is obtained. In addition, the age group can be selected from a pull down menu at the top of the screen shot. The NT-proBNP cut-off value changes automatically for the appropriate age. For example, an age group of less than 55 years of age, the NT-proBNP cut-off value is >450 while an age group of over 55-75 years of age, the NT-proBNP cut-off value is >900. By choosing the age group of over 75 years of age an extra point is automatically added to the total PRIDE score (14). The NT-proBNP cut-off value under this group is >1800. See FIGURES 9A-B. To uncheck all the boxes on the screen, the operator simply has to tap the clear button located at the bottom of the screen. To navigate away from the Calculator function, the operator can tap on the Navigation button at the bottom of the screen. A pop-up box will then appear with the different functions of the program. Tapping on the Algorithm takes one to a step-by-step clinical algorithm for the evaluation and triage of patients with suspected acute CHF. Tapping will continue to take the operator to the first screen shot for the Clinical Algorithm, wherein in the operator will be asked a number of questions which will lead one down different clinical pathways. To begin, the operator will be asked to provide the patient's NT-proBNP level. If <300 NT-proBNP level is selected, you will be informed that acute CHF is unlikely and you are also told to search of alternative causes of dyspnea. When more than 10,000 NT-proBNP level is selected, you are told to consider other confounding factors, and if not present, CHF is very likely and can be likely severe. A suggestion to admit to appropriate inpatient unit (see FIGURE 9C).

For the majority of patients whose NT-proBNP values fall between the two extremes, the algorithm is more complex (Algorithm 2). If the NT-proBNP value falls below the age specific cut-off or PRIDE CHF score <7, tapping on the correct box would inform you that acute CHF is unlikely and it is recommended to search for alternative causes of dyspnea (FIGURE 9D).

However, if the NT-proBNP value is above the age-specific cut-off, it is recommended to consider confounding factors and then to determine whether the patient has a history of CHF. If no history of CHF is chosen, a question will be presented as to whether the PRIDE CHF score of the patient was <7 or >7. If >7, then CHF is considered likely and triage is based on response to medical therapy. If <7, acute CHF is unlikely. See FIGURE 9E.

Returning to an earlier screenshot, if patient has a prior history of CHF, the user will be asked to calculate the patient's "DryNT-pro-BNP." If >25% is selected, PRIDE CHF score needs to be calculated. If <7, acute CHF is again unlikely. If >7, then CHF is considered likely and triage is based on response to medical therapy. See FIGURE 9F. At any point along the PRIDE PDA Algorithm, the user can navigate to another function of the program by selecting from the pop-up menu at the bottom of the screen. IfTNFORMATION" menu is selected, the user is taken to a screen shot which will quickly direct the user to the wanted information. By tapping the appropriate topic, screens for "Rationale for the PRIDE Score," "Components of the PRIDE Score," "How to Interpret the PRIDE Score," "Causes of False Positive NT-ProBNP Values," and "Causes of False Negative NT-ProBNP Values." See FIGURE 9G. As a reminder, the "Clinical Algorithm" or "Information" sections can also be directly navigated from the "Welcome Screen." See FIGURE 9H. XL PRIDE Mortality Calculator

The PRIDE Mortality Calculator, as illustrated in FIGURE 10, measures the mortality score, score quintile and one year mortality rate of patients having acute dyspnea. The calculator allows a health worker or end user to enter patient's information with respect to their predictor variables such as age, heart rate, BUN, NYHA class, levels of NY-proBNP, SBP < 100mm Hg, and heart murmur. The PRIDE Mortality Calculator interpolates the mortality score, score quintile and one year mortality rate values by using the PRIDE algorithm.

The contents of the articles, patents, and patent applications, and all other documents and electronically available information mentioned or cited herein, are hereby incorporated by reference in their entirety to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference. Applicants reserve the right to physically incorporate into this application any and all materials and information from any such articles, patents, patent applications, or other documents. The inventions illustratively described herein may suitably be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, for example, the terms "comprising", "including," containing", etc. shall be read expansively and without limitation. Additionally, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the inventions embodied therein herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention.

The invention has been described broadly and generically herein. Each of the narrower species and subgeneric groupings falling within the generic disclosure also form part of the invention. This includes the generic description of the invention with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein. In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

Claims

THE CLAIMSWhat is claimed:

1. A system for assessing heart failure propensity in a patient, said system comprising: an interface, said interface allowing entry of a plurality of physiological and clinical data associated with said patient, said physiological data being derived form a sample from said patient and comprising at least one biomarker therein; a calculation means, said calculation means computing a weighted sum of physiological and clinical data of said patient; and a display, said display showing said weighted sum for said patient, whereby a health worker can assess the heart failure propensity of said patient,

2. The system according to claim 1, wherein said system is a handheld device.

3. The system according to claim 1, wherein said system is a computer system.

4. The system according to claim 1, wherein said calculation means is displayed on said display, whereby said health worker may interact with said calculation means through said interface.

5. The system according to claim 1, wherein said physiological and clinical data are input in integer format.

6. The system according to claim 1, wherein said clinical data on said patient is a member of a group of predictor variables, the group of predictor variables consisting of age (by decade, multiplied by 0.7), heart rate (by decile, multiplied by 0.5), blood urea nitrogen (by decile, multiplied by 0.5), NHYA Class (class multiplied by 5), NT-proBNP > 986 pg/mL (18 points), systolic blood pressure < 100 mmHg (11 points), and presence of a murmur (11 points).

7. The sytem according to claim 6, wherein said weighted sum is a predictor of mortality in one year.

8. The system according to claim 6, wherein said weighted sum is a predictor of coronary heart failure.

9. A method for assessing heart failure propensity in a patient, said method comprising: determining a plurality of physiological parameters associated with said patient; calculating said mortality scores for said patient based upon physiological parameters; and displaying said mortality score on a display.

10. The method according to claim 9, wherein said physiological parameters associated with said patient are a member of a group of predictor variables, the group of predictor variables consisting of age (by decade, multiplied by 0.7), heart rate (by decile, multiplied by 0.5), blood urea nitrogen (by decile, multiplied by 0.5), NHYA Class (class multiplied by 5), NT-proBNP > 986 pg/mL (18 points), systolic blood pressure < 100 mmHg (11 points), and presence of a murmur (11 points).

11. The method according to claim 9, wherein said mortality score is a predictor of coronary heart failure.

12. The method according to claim 9, wherein said mortality score is a predictor of mortality in one year.

13. The method according to claim 12, wherein said mortality score is a preditor of mortality among dyspneic patients with diagnoses other than CHF such as obstructive airway disease, pneumonia, chest pain or acute coronary syndrome, acute pulmonary thromboembolism, acute bronchitis, bacterial sepsis, carcinoma, pneumonia.

14. The method according to claim 9, wherein the cut point of NT-proBNP concentration for predicting 1-yr mortality in dyspneic patients is 986 pg/mL, with a sensitivity of 79%, specificity of 68%, positive predictive value of 31% and negative predictive value of 95%.

15. The method according to claim 9, wherein an NT-proBNP concentration for predicting 1-yr mortality in dyspneic patients of greater than 986 pg/mL, with a hazard ratio (HR) is 2.88, 95% confidence interval (CI) is 1.64-5.06, and a P value of <0.001.

16. The method according to claim 9, wherein the ROC curve analysis of the score accuracy to predict death in dyspneic subjects with ADHF has an AUC of 0.73, 95% CI = 0.67-0.69 and P value of <0.001.

17. The method according to claim 9, wherein the ROC curve analysis of the score accuracy to predict death in dyspneic subjects with alternative causes of dyspnea has an AUC = 0.83, 95% CI = 0.77-0.85 and P value of <0.001.

18. The method according to claim 9, wherein dyspneic patients who have acute destabilized CHF and whose NT-proBNP concentration is 986 pg/mL or less survive after one year of index presentation to the ED.

19. The method according to claim 9, wherein the mortality risk is independent of a diagnosis of acute CHF.

20. The method according to claim 9, wherein the NT-proBNP concentration reflected in he area under the ROC curve is 0.76 and P value of <0.001.

21. The method according to claim 9, wherein the combination of NT-proBNP concentration with one of the covariates yielded an AUC of 0.80 for predicting death within one year.

22. The method according to claim 9, wherein the combination of NT-proBNP concentration with all of other covariates yielded an AUC of 0.82 for predicting death within one year.

23. The method according to claim 9, wherein the cut point of NT-proBNP concentration for predicting 1-yr mortality in dyspneic patients that yielded a 90% sensitivity is 120 pg/mL, with a positive predictive value of 20.1% and negative predictive value of 94.6%.

24. The method according to claim 9, wherein the cut point of NT-proBNP concentration for predicting 1-yr mortality in dyspneic patients that yielded a 90% specificity is 6899 pg/mL, with a positive predictive value of 41.2% and negative predictive value of 88.7%.

25. The method according to claim 9, wherein the diagnostic threshold of 450 pg/mL for patients younger than 50 years old and 900 pg/mL for patients 50 years or older, and the cut point of NT-proBNP concentration for predicting anxiety attacks, allergic reactions, and ascites in dyspneic patients is an age-independent cut point of 300 pg/mL with an NPV of 99.7%.

26. A computer readable medium having computer readable program code means embodied therein, the computer program code means comprising code that: accepts a plurality of physiological parameters associated with a patient; calculates a mortality score for said patient based upon physiological parameters; and displays said mortality score to a user.

27. The computer readable medium according to claim 26, wherein said physiological parameters associated with said patient are a member of a group of predictor variables, the group of predictor variables consisting of age (by decade, multiplied by 0.7), heart rate (by decile, multiplied by 0.5), blood urea nitrogen (by decile, multiplied by 0.5), NHYA Class (class multiplied by 5), NT-proBNP > 986 pg/mL (18 points), systolic blood pressure < 100 mmHg (11 points), and presence of a murmur (11 points).

28. The computer readable medium according to claim 27, wherein said mortality score is a predictor of coronary heart failure.

29. The computer readable medium according to claim 27, wherein said mortality score is a predictor of mortality in one year.