WO2018223006A1 - Facteurs prédictifs pour une lésion rénale aiguë - Google Patents

Facteurs prédictifs pour une lésion rénale aiguë Download PDF

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WO2018223006A1
WO2018223006A1 PCT/US2018/035618 US2018035618W WO2018223006A1 WO 2018223006 A1 WO2018223006 A1 WO 2018223006A1 US 2018035618 W US2018035618 W US 2018035618W WO 2018223006 A1 WO2018223006 A1 WO 2018223006A1
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aki
risk
risk profile
subject
value
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PCT/US2018/035618
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English (en)
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Beau MUNOZ
Seth A. SCHOBEL-MCHUGH
Eric A. ELSTER
Beverly J. GAUCHER
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The Henry M. Jackson Foundation For The Advancement Of Military Medicine, Inc.
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Publication of WO2018223006A1 publication Critical patent/WO2018223006A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P13/00—Drugs for disorders of the urinary system
    • A61P13/12—Drugs for disorders of the urinary system of the kidneys
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P7/00—Drugs for disorders of the blood or the extracellular fluid
    • A61P7/08—Plasma substitutes; Perfusion solutions; Dialytics or haemodialytics; Drugs for electrolytic or acid-base disorders, e.g. hypovolemic shock
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/475—Assays involving growth factors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/521—Chemokines
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/54—Interleukins [IL]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/555—Interferons [IFN]
    • G01N2333/57—IFN-gamma
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/34—Genitourinary disorders
    • G01N2800/347—Renal failures; Glomerular diseases; Tubulointerstitial diseases, e.g. nephritic syndrome, glomerulonephritis; Renovascular diseases, e.g. renal artery occlusion, nephropathy
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease

Definitions

  • the present invention relates to methods of determining if a su bject has an increased risk of developing acute kidney injury (AKI) prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of developing AKI prior to the onset of any detectable symptoms thereof.
  • PTAKI post-traumatic acute kidney injury
  • PTARF post-traumatic acute renal failure
  • AKI is now believed to be the result of complex biochemical interactions between epithelial cells, endothelial cells, inflammatory mediators, and cytokines resulting in prolonged injury and decremental function of the kidney (Sharfuddin, A. A. and Molitoris, B. A., Pathophysiology of ischemic acute kidney injury. Nat Rev Nephrol 2011, 7 (4), 189-200).
  • AKI represents a multifactorial, heterogenous syndrome or spectrum of disease, the molecular pathways of which may be unique to specific AKI populations (i.e. sepsis, cardiopulmonary bypass, post-surgical, trauma, transplant, etc.).
  • specific AKI populations i.e. sepsis, cardiopulmonary bypass, post-surgical, trauma, transplant, etc.
  • the specific molecular environment of the patient plays a vital role in the development and clinical trajectory of AKI. Therefore, the molecular environment of the military trauma patient population is likely to differ in subtle ways from that of other critically ill populations.
  • Therapies include efforts to achieve fluid homeostasis, intravascular fluid balance, and avoid nephrotoxic drugs while avoiding further injury by continuing to promote homeostasis.
  • One particularly effective therapy is implementation of an AKI Care Bundle (http://londonaki.net).
  • This care bundle can include determining the cause of AKI, treating the cause of AKI, and providing supporting care to include maintenance fluids, monitoring output, reviewing plans every four hours, reviewing drugs, consider proton pump inhibitors, review labs related to kidney function, monitor for complications, and escalate care to a nephrologist where appropriate.
  • a predictive model incorporating novel biomarkers, that accurately estimates a patient's risk for AKI after trauma can optimize clinical decision making and preoperative treatment strategies to minimize the risk for AKI. Predictive models can also be used as research tools by identifying high-risk patients for AKI clinical trials.
  • Preventative measures should be administered to trauma patients at risk of developing acute kidney injury. These measures include the four M's: monitor the patient, maintain circulation, minimize kidney insults, and manage the trauma. At-risk patients should be monitored with regular blood tests, pathology alerts, fluid charting, and urine volume determinations. Circulation should be maintained by hydration, resuscitation, and oxygenation, which may include administration of IV fluids or supplemental oxygen. Kidney insults should be minimized by avoiding administration of nephrotoxic medications (NSAID, aminoglycosides, ACE/ARB, diuretics), surgery, high risk interventions, iodinated contrast and prophylaxis, or exposure to hospital acquired infection.
  • NSAID nephrotoxic medications
  • This study quantified the incidence of combat-related post-traumatic AKI in the current conflicts in Iraq and Afghanistan utilizing the most recent AKI consensus criteria, identified clinical risk factors for post-traumatic AKI in these patients, and identified a pattern of inflammatory cytokines associated with PTAKI.
  • a statistical model is trained with these clinical variables and cytokines to identify patients at risk of developing this condition.
  • the present invention relates to methods of determining if a su bject has an increased risk of developing AKI prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of developing or developing symptoms associated with AKI prior to the onset of any detectable symptoms thereof.
  • the present invention relates to methods of determining the susceptibility of a human subject to acute kidney injury by (a) obtaining a biological sample from the subject, and (b) measuring the levels in the biological subject of one or more polypeptides selected from the group consisting of Interleukin-la (IL-la), lnterleukin-2 Receptor (IL-2R), lnterleukin-4 (IL-4), lnterleukin-7 (IL-7), Monokine induced by interferon-gamma (MIG), Vascular endothelial growth factor (VEGF) and Macrophage Inflammatory Protein-l-beta (MIP1B); and (c) determining a risk profile value from the polypeptide levels wherein the value of the risk profile correlates with the susceptibility of the human subject to acute kidney injury.
  • IL-la Interleukin-la
  • IL-2R lnterleukin-2 Receptor
  • IL-4 lnterleukin-4
  • IL-7
  • the polypeptides are selected from the group consisting of IL-2R, IL- 4, IL-7, and MIP1B. In some embodiments, the polypeptide is IL-2R. In some embodiments, the human subject has a traumatic wound. In some embodiments, the biological sample is blood, serum, or plasma.
  • the present invention relates to methods of determining the susceptibility of a human subject to acute kidney injury by (a) obtaining a biological sample from the subject, and (b) measuring the levels in the biological subject of one or more polypeptides selected from the group consisting of Interleukin-la (IL-la), lnterleukin-2 Receptor (IL-2R), lnterleukin-4 (IL-4), lnterleukin-7 (IL-7), Monokine induced by interferon-gamma (MIG), Vascular endothelial growth factor (VEGF) and Macrophage Inflammatory Protein-l-beta (MIP1B); (c) determining at least one clinical value selected from the group consisting of Acute Injury Score (AIS) Chest, AIS Head, AIS Skin, Wound Length, and Wound Mechanism; and (d) determining a risk profile value from the polypeptide levels and clinical values, wherein the value of the risk profile correlates with the susceptibility of the human subject
  • At least 5 polypeptides are selected. In some embodiments, at least 5 clinical values are selected.
  • the present invention relates to methods of inhibiting the development of acute kidney injury comprising: (a) determining the risk profile value of claim 1, and (b) administering medical treatment to the human subject prior to the onset of acute kidney injury symptoms.
  • the medical treatment is an administration of IV fluids.
  • the medical treatment is an administration of supplemental oxygen.
  • Figure 1 Biomarker distributions for 32 analytes compared between patients with and without AKI.
  • A All time points.
  • B Initial time point. The ends of each box mark upper and lower quartiles. The median is marked by a horizontal line. The whiskers extend to the highest and lowest observations.
  • Figure 2 Receiver operator characteristic curve analysis and area under curve analysis for the AKI model described herein. The model performs with an AUC of 0.80.
  • Figure 3 Decision curve analysis for the AKI model.
  • the decision curve shows the AKI model to have a positive net benefit for use in predicting AKI over the treat-all and treat-none models.
  • the present invention relates to methods of determining if a subject has an increased risk of developing AKI prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of developing or developing symptoms associated with AKI prior to the onset of any detectable symptoms thereof.
  • test subject indicates a mammal, in particular a human or non-human primate.
  • the test subject is in need of an assessment of susceptibility of AKI.
  • the test subject may have no symptoms that AKI may occur.
  • the risk profile comprises serum levels of at least one of Interleukin-la (IL-la), lnterleukin-2 Receptor (IL-2R), lnterleukin-4 (IL-4), lnterleukin-7 (IL-7), Monokine induced by interferon-gamma (MIG), Vascular endothelial growth factor (VEGF) and Macrophage Inflammatory Protein-l-beta (MIP1B).
  • IL-la Interleukin-la
  • IL-2R lnterleukin-2 Receptor
  • IL-4 lnterleukin-4
  • IL-7 lnterleukin-7
  • MIG Monokine induced by interferon-gamma
  • VEGF Vascular endothelial growth factor
  • MIP1B Macrophage Inflammatory Protein-l-beta
  • the present invention also relates to methods of detecting elevated levels of a specific collection of analytes in one or more samples obtained from a subject.
  • the collection of analytes comprises serum levels of at least one of Interleukin-la (IL-la), lnterleukin-2 Receptor (IL-2R), lnterleukin-4 (IL-4), lnterleukin-7 (IL-7), Monokine induced by interferon-gamma (MIG), Vascular endothelial growth factor (VEGF) and Macrophage Inflammatory Protein-l-beta (MIP1B).
  • IL-la Interleukin-la
  • IL-2R lnterleukin-2 Receptor
  • IL-4 lnterleukin-4
  • IL-7 lnterleukin-7
  • MIG Monokine induced by interferon-gamma
  • VEGF Vascular endothelial growth factor
  • MIP1B Macrophage Inflammatory Protein-
  • AKI acute kidney injury, or AKI
  • the Kidney Disease Improving Global Outcomes Acute Kidney Injury Work Group provides standardized definitions for different stages of AKI.
  • the term "increased risk” is used to mean that the test subject has an increased chance of developing AKI compared to a normal individual or an individual with a trauma wound who does not develop AKI.
  • the increased risk may be relative or absolute and may be expressed qualitatively or quantitatively.
  • an increased risk may be expressed as simply determining the subject's risk profile and placing the patient in an "increased risk” category, based upon previous population studies.
  • a numerical expression of the subject's increased risk may be determined based upon the risk profile.
  • examples of expressions of an increased risk include but are not limited to, odds, probability, odds ratio, p-values, attributable risk, biomarker index score, relative frequency, positive predictive value, negative predictive value, and relative risk.
  • the attributable risk can also be used to express an increased risk.
  • the AR describes the proportion of individuals in a population exhibiting AKI to a specific member of the risk profile. AR may also be important in quantifying the role of individual components (specific member) in condition etiology and in terms of the public health impact of the individual risk factor.
  • the public health relevance of the AR measurement lies in estimating the proportion of cases of AKI in a population of subjects that could be prevented if the profile or individual factor were absent.
  • RR is the relative risk, which can be approximated with the odds ratio when the profile or individual factor of the profile under study has a relatively low incidence in the general population.
  • the increased risk of a patient can be determined from p-values that are derived from association studies. Specifically, associations with specific profiles can be performed using regression analysis by regressing the risk profile with the presence or absence of AKI. In addition, the regression may or may not be corrected or adjusted for one or more factors.
  • the factors for which the analyses may be adjusted include, but are not limited to age, sex, weight, ethnicity, type of wound if present, number of wounds if present, severity of wounds, number of days from injury, geographic location, fasting state, general health of the subject, alcohol or drug consumption, caffeine or nicotine intake and circadian rhythms, to name a few.
  • Increased risk can also be determined from p-values that are derived using logistic regression.
  • Binomial (or binary) logistic regression is a form of regression which is used when the dependent is a dichotomy and the independents are of any type.
  • Logistic regression can be used to predict a dependent variable on the basis of continuous or categorical or both (continuous and categorical) independents and to determine the percent of variance in the dependent variable explained by the independents; to rank the relative importance of independents; to assess interaction effects; and to understand the impact of covariate control variables.
  • Logistic regression applies maximum likelihood estimation after transforming the dependent into a "logit" variable (the natural log of the odds of the dependent occurring or not). In this way, logistic regression estimates the probability of a certain event occurring.
  • SAS statistical analysis software
  • R package is free, general purpose package that complies with and runs on a variety of UNIX platforms.
  • the methods may also comprise displaying the one or risk profiles on a screen that is communicatively connected to the computer.
  • two different computers can be used : one computer configured or programmed to generate one or more risk profiles and a second computer configured or programmed to determine statistical risk. Each of these separate computers can be communicatively linked to its own display or to the same display.
  • risk profile means the combination of a subject's risk factors analyzed or observed.
  • factor and/or “component” are used to mean the individual constituents that are assessed when generating the profile.
  • the risk profile is a collection of measurements, such as but not limited to a quantity or concentration, for individual factors taken from a test sample of the subject.
  • test samples or sources of components for the risk profile include, but are not limited to, biological fluids, which can be tested by the methods of the present invention described herein, and include but are not lim ited to whole blood, such as but not limited to peripheral blood, serum, plasma, cerebrospinal fluid, urine, amniotic fluid, lymph fluids, various external secretions of the respiratory, intestinal and genitourinary tracts, tears, saliva, white blood cells, myelomas and the like.
  • the risk profile can include a "biological effector” aspect and/or a non-biological effector aspect.
  • biological effector is used to mean a molecule, such as but not limited to, a protein, peptide, a carbohydrate, a fatty acid, a nucleic acid, a glycoprotein, a proteoglycan, etc. that can be assayed.
  • Specific examples of biological effectors can include, cytokines, growth factors, antibodies, hormones, cell surface receptors, cell surface proteins, carbohydrates, etc.
  • I Ls interleukins
  • I L-1RA I L-1 receptor antagonist
  • I L-2 I L-2 receptor
  • I L-2R I L-3, I L-4, I L-5, IL-6, I L-7, I L-8, I L-10, I L-12, I L-13, I L-15, I L-17
  • growth factors such as tumor necrosis factor alpha (TN Fa), granulocyte colony stimulating factor (G-CSF), granulocyte macrophage colony stimulating factor (GM-CSF), interferon alpha (I N F-a), interferon gamma (I FN-y), epithelial growth factor (EGF), basic endothelial growth factor (bEGF), hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF), and chemokines such as monocyte chemoattractant protein-1 (CCL2/MCP-1), macrophage inflammatory protein-1 alpha (CCL2/MCP-1), macrophage inflammatory protein-1 alpha (CCL2/MCP-1
  • levels of individual components of the risk profile are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed.
  • levels of the individual factors in the serum of the risk profile are assessed using mass spectrometry in conjunction with ultra-performance liquid chromatography (UPLC), high-performance liquid chromatography (H PLC), gas chromatography (GC), gas chromatography/mass spectroscopy (GC/MS), and U PLC to name a few.
  • UPLC ultra-performance liquid chromatography
  • H PLC high-performance liquid chromatography
  • GC gas chromatography
  • GC/MS gas chromatography/mass spectroscopy
  • U PLC ultra-performance liquid chromatography
  • Other methods of assessing levels of some of the individual components include biological methods, such as but not limited to ELISA assays, Western Blot and multiplexed immunoassays etc.
  • Other techniques may include using quantitative arrays, PCR, Northern Blot analysis.
  • determining levels of, for example, a fragment of protein being analyzed may be sufficient to conclude or assess that an individual component of the risk profile being analyzed is increased or decreased.
  • the presence/absence/strength of a detectable signal may be sufficient to assess levels of components.
  • non-biological effector is a component that is generally considered not to be a specific molecule. Although not a specific molecule, a non-biological effector may nonetheless still be quantifiable, either through routine measurements or through measurements that stratify the data being assessed. For example, number or concentrate of red blood cells, white blood cells, platelets, blood cells received, plasma received, coagulation time, blood oxygen content, etc. would be a non-biological effector component of the risk profile. All of these components are measureable or quantifiable using routine methods and equipment. Other non-biological components include data that may not be readily or routinely quantifiable or that may require a practitioner's judgment or opinion.
  • the mechanism of injury is included in the risk profile.
  • the phrase "mechanism of injury” means the manner in which the subject received an injury.
  • the mechanism of injury may be described as a blast, crush, gunshot wound, a vehicle accident, laceration, etc.
  • data regarding injury type is included in the risk profile.
  • data on the occurrence of multiple wounds is included in the risk profile.
  • data on the number of days from injury is included in the risk profile.
  • data on the wound size and/or wound length is included in the risk profile.
  • data on the number of operations, number of wounds which failed, total blood units transfused, total blood units transfused within 48 hours of injury, and/or incidence of pneumonia or bacteremia are included in the risk score.
  • abbreviated injury scale (AID) data for the chest (AIS Chest), head (AIS Head) and/or skin (AIS Skin) is included in the risk profile.
  • AIS Chest abbreviated injury scale
  • AIS Head head
  • AIS Skin skin
  • ISS injury severity score
  • data on APACHE II score is used in the risk score.
  • data on APACHE II score is used in the risk score.
  • characteristics of the wound, including wound length and area are included in the risk profile.
  • the Abbreviated Injury Scale is an anatomical scoring system first introduced in 1969. Since this time it has been revised and updated against survival so that it now provides a reasonably accurate was of ranking the severity of injury. The latest incarnation of the AIS score is the 1990 revision.
  • the AIS is monitored by a scaling committee of the Association for the Advancement of Automotive Medicine. Injuries are ranked on a scale of 1 to 6, with 1 being minor, 5 severe and 6 an unsurvivable injury. This represents the 'threat to life' associated with an injury and is not meant to represent a comprehensive measure of severity.
  • the AIS is not an injury scale, in that the difference between AIS1 and AIS2 is not the same as that between AIS4 and AIS5.
  • the Injury Severity Score is an anatomical scoring system that provides an overall score for patients with multiple injuries. Each injury is assigned an Abbreviated Injury Scale (AIS) score and is allocated to one of six body regions (Head, Face, Chest, Abdomen, Extremities (including Pelvis), External). Only the highest AIS score in each body region is used. The 3 most severely injured body regions have their score squared and added together to produce the ISS score.
  • the ISS score takes values from 0 to 75. If an injury is assigned an AIS of 6 (unsurvivable injury), the ISS score is automatically assigned to 75.
  • the ISS score is virtually the only anatomical scoring system in use and correlates linearly with mortality, morbidity, hospital stay and other measures of severity. Baker SP et al., The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma. 1974 Mar;14(3):187-96.
  • An Acute Physiology and Chronic Health Evaluation (APACHE) II score is a point score based upon initial values of 12 routine physiologic measurements, age, and previous health status to provide a general measure of severity of disease. An increasing score (range 0 to 71) was closely correlated with the subsequent risk of hospital death for 5815 intensive care admissions from 13 hospitals. This relationship was also found for many common diseases. When APACHE II scores are combined with an accurate description of disease, they can prognostically stratify acutely ill patients and assist investigators comparing the success of new or differing forms of therapy. Knaus WA et al., APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-29.
  • routine statistical methods can be employed. For example, rflmpute from the randomForest R package can be used to impute missing data. Up-sampling and predictor rank transformations can be performed on the data set only for variable selection to accommodate class imbalance and non-normality in the data.
  • variable selection the constraint-based algorithms fast. iamb, iamb and gs and the constraint-based local discovery learning algorithms mmpc and si.hiton.pc from the "bnlearn" R package can be used to search the input dataset for nodes of Bayesian networks.
  • the nodes can be chosen as the reduced variable sets.
  • the variables Before running the data through variable selection and binary classification algorithms, the variables may or may not randomly re-ordered.
  • the data can be run through the variable selection and binary classification algorithms more than once, for example, 10, 20, 30, 40, 50 or even more times.
  • each variable set can be pulled from the raw data and run in sundry binary classification algorithms using the train function from the R caret package: linear discriminant analysis (Ida), classification and regression trees (cart), k-nearest neighbors (knn), support vector machine (svm), logistic regression (glm), random forest (rf), generalized linear models (glmnet) and naive Bayes (nb).
  • Ida linear discriminant analysis
  • cart classification and regression trees
  • knnn k-nearest neighbors
  • svm logistic regression
  • rf random forest
  • generalized linear models glmnet
  • nb naive Bayes
  • the assessment of the levels of the individual components of the risk profile can be expressed as absolute or relative values and may or may not be expressed in relation to another component, a standard an internal standard or another molecule of compound known to be in the sample. If the levels are assessed as relative to a standard or internal standard, the standard may be added to the test sample prior to, during or after sample processing.
  • a sample may be taken from the subject.
  • the sample may or may not processed prior assaying levels of the components of the risk profile.
  • whole blood may be taken from an individual and the blood sample may be processed, e.g., centrifuged, to isolate plasma or serum from the blood.
  • the sample may or may not be stored, e.g., frozen, prior to processing or analysis.
  • the individua l levels of each of the risk factors are higher or lower than those compared to normal levels.
  • one, two, three, four, five, six or seven of the levels of each of the factor are higher or lower than normal levels while others, if any, are lower than or the same as normal levels.
  • the levels of depletion of the factors or components compared to normal levels can vary.
  • the levels of any one or more of the factors or components is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 higher than normal levels (where, for sake of clarity, a marker a level of "1" would indicate that the component is at the same level in both the subject and normal samples).
  • the number of "times" the levels of a factor are higher over normal can be a relative or absolute number of times.
  • the levels of the factors or components may be normalized to a standard and these normalized levels can then be compared to one another to determine if a factor or component is lower, higher or about the same.
  • the risk profile comprises at least one, two or three, four, five, six, seven, eight, nine or ten of the factors or components for the prediction of AKI. If one factor or component of the biological effector aspect of the risk profile is used in generating the risk profile for the prediction of AKI, then any one of the listed factors or components can be used to generate the profile. If two factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of AKI, any combination of the two listed above can be used. If three factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of AKI, any combination of three of the factors or components listed above can be used.
  • any combination of four of the factors or components listed above can be used. If four factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of AKI, any combination of four of the factors or components listed above can be used. If five factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of AKI, any combination of five of the factors or components listed above can be used. If six factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of AKI, any combination of six of the factors or components listed above can be used. If seven factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of AKI, any combination of seven of the factors or components listed above can be used. Of course all members of the biological effector aspect of each profile panel can be used to generate a profile for the prediction of AKI.
  • the subject's risk profile is compared to the profile that is deemed to be a normal risk profile.
  • an individual or group of individuals may be first assessed to ensure they have no signs, symptoms or diagnostic indicators of AKI.
  • the risk profile of the individual or group of individuals can then be determined to establish a "normal risk profile.”
  • a normal risk profile can be ascertained from the same subject when the subject is deemed as healthy with no signs, symptoms or diagnostic indicators of AKI.
  • a risk profile from a "normal subject,” e.g., a "normal risk profile” is a subject that does not exhibit or display AKI, but may still may not be considered as "healthy.”
  • a "normal" risk profile is assessed in the same subject from whom the sample is taken prior to the onset of any signs, symptoms or diagnostic indicators that they may exhibit AKI. That is, the term "normal” with respect to a risk profile can be used to mean the subject's baseline risk profile prior to the onset of any signs, symptoms or diagnostic indicators of potential AKI. The risk profile can then be reassessed periodically and compared to the subject's baseline risk profile.
  • the present invention also includes methods of monitoring the progression of AKI in a subject, with the methods comprising determining the subject's risk profile at more than one time point.
  • some embodiments of the methods of the present invention will comprise determining the subject's risk profile at two, three, four, five, six, seven, eight, nine, 10 or even more time points over a period of time, such as a week or more, two weeks or more, three weeks or more, four weeks or more, a month or more, two months or more, three months or more, four months or more, five months or more, six months or more, seven months or more, eight months or more, nine months or more, ten months or more, 11 months or more, a year or more or even two years.
  • the methods of monitoring a subject's risk of developing AKI would also include embodiments in which the subject's risk profile is assessed before and/or during and/or after treatment of AKI.
  • the present invention also includes methods of monitoring the efficacy of treatment of AKI by assessing the subject's risk profile over the course of the treatment and after the treatment.
  • the methods of monitoring the efficacy of treatment of AKI comprise determining the subject's risk profile at least one, two, three, four, five, six, seven, eight, nine or 10 or more different time points prior to the receipt of treatment for AKI and subsequently determining the subject's risk profile at least one, two, three, four, five, six, seven, eight, nine or 10 or more different time points after beginning of treatment for AKI, and determining the changes, if any, in the risk profile of the subject.
  • the treatment may be any treatment designed to cure, remove or diminish the likelihood of developing AKI.
  • a normal risk profile is assessed in a sample from a different subject or patient (from the subject being analyzed) and this different subject does not have or is not suspected of developing AKI.
  • the normal risk profile is assessed in a population of healthy individuals, the constituents of which display no signs, symptoms or diagnostic indicators that they may have or will develop AKI.
  • the subject's risk profile can be compared to a normal risk profile generated from a single normal sample or a risk profile generated from more than one normal sample.
  • measurements of the individual components e.g., concentration, ratio, log ratios etc.
  • concentration, ratio, log ratios etc. of the normal risk profile can fall within a range of values, and values that do not fall within this "normal range” are said to be outside the normal range.
  • These measurements may or may not be converted to a value, number, factor or score as compared to measurements in the "normal range.” For example, a measurement for a specific factor or component that is below the normal range, may be assigned a value or -1, -2, -3, etc., depending on the scoring system devised.
  • the measurements of the individual components themselves are used in the risk profile, and these levels can be used to provide a "binary" value to each component, e.g., “elevated” or “not elevated.”
  • Each of the binary values can be converted to a number, e.g., "1" or "0,” respectively.
  • the "risk profile value" can be a single value, number, factor or score given as an overall collective value to the individual components of the profile. For example, if each component is assigned a value, such as above, the component value may simply be the overall score of each individual or categorical value.
  • the risk profile value could be a useful single number or score, the actual value or magnitude of which could be an indication of the actual risk of developing AKI, e.g., the "more positive" the value, the greater the risk of developing AKI.
  • the "risk profile value” can be a series of values, numbers, factors or scores given to the individual components of the overall profile.
  • the "risk profile value” may be a combination of values, numbers, factors or scores given to individual components of the profile as well as values, numbers, factors or scores collectively given to a group of components, such as a biological effector portion.
  • the risk profile value may comprise or consist of individual values, number or scores for specific component as well as values, numbers or scores for a group of components.
  • individual values from the risk profile and/or the mechanism of injury can be used to develop a single score, such as a "combined risk index,” which may utilize weighted scores from the individual component values reduced to a diagnostic number value.
  • the combined risk index may also be generated using non-weighted scores from the individual component values.
  • the threshold value would be or could be set by the combined risk index from one or more normal subjects.
  • the value of the risk profile can be the collection of data from the individual measurements and need not be converted to a scoring system, such that the "risk profile value" is a collection of the individual measurements of the individual components of the profile.
  • a subject is diagnosed with having an increased risk of suffering from AKI if the subject's eight, seven, six, five, four, three, two or even one of the components or factors herein are at abnormal levels.
  • the attending health care provider may subsequently prescribe or institute a treatment program.
  • the present invention also provides for methods of treating individuals for AKI.
  • the attending healthcare worker may begin treatment, based on the subject's risk profile, before there are perceivable, noticeable or measurable signs of AKI in the individual.
  • the invention provides methods of treating AKI in a subject in need thereof.
  • the treatment methods include obtaining a subject's risk profile as defined herein and prescribing a treatment regimen to the subject if the risk profile indicates that the subject is at risk of developing AKI.
  • the invention also encompasses a method of determining the susceptibility of a human subject with a traumatic wound to the development of acute kidney injury, the method comprising
  • the methods of the invention encompass a method of determining a subject's stratified risk level of acute kidney injury by (a) measuring the protein concentration level of one or more biomarkers specific for one or more risk classifiers defined using Random Forest Classifiers (RFC), logistic regression or another appropriate systems biology or statistical approach in a sample obtained from the subject, wherein said measuring comprises contacting the sample with a substrate having at least one antibody against each of the one or more biomarkers on the substrate,
  • RRC Random Forest Classifiers
  • the methods of treatment also include methods of monitoring the effectiveness of a treatment for AKI.
  • a treatment regimen has been established, with or without the use of the methods of the present invention to assist in a diagnosis of a risk of developing AKI
  • the methods of monitoring a subject's risk profile over time can be used to assess the effectiveness of treatments for AKI.
  • the subject's risk profile can be assessed over time, including before, during and after treatments for AKI .
  • the risk profile can be monitored, with, for example, the normalization or decline in the values of the profile over time being indicative that the treatment may be showing efficacy of treatment.
  • the present invention also provides kits that can be used in the methods of the present invention.
  • kits for assessing the increased risk of developing AKI comprising one or more sets of antibodies that are immobilized onto a solid substrate and specifically bind to at least one of the factors or components listed herein.
  • the kits comprise at least two, three, four, five, six or seven sets of antibodies immobilized onto a solid substrate, with each set corresponding to a factor.
  • the antibodies that are immobilized onto the substrate may or may not be labeled.
  • the antibodies may be labeled, e.g., bound to a labeled protein, in such a manner that binding of the specific protein may displace the label and the presence of the marker in the sample is marked by the absence of a signal.
  • the antibodies that are immobilized onto the substrate may be directly or indirectly immobilized onto the surface.
  • Methods for immobilizing proteins, including antibodies are well-known in the art, and such methods may be used to immobilize a target protein, e.g., I L-12, or another antibody onto the surface of the substrate to which the antibody directed to the specific factor can then be specifically bound. In this manner, the antibody directed to the specific biomarker is immobilized onto the surface of the substrate for the purposes of the present invention.
  • kits of the present invention may or may not include containers for collecting samples from the subject and one or more reagents, e.g., purified target biomarker for preparing a calibration curve.
  • the kits may or may not include additional reagents such as wash buffers, labeling reagents and reagents that are used to detect the presence (or absence) of the label.
  • Study Population -Study participants enrolled in an Institutional Review Board approved study were recruited from wounded US service members evacuated to the National Capital Area from Iraq and Afghanistan from 2007 to 2008. Inclusion criteria for this study were defined as all service men and women that sustained penetrating injuries to one or more extremities.
  • Demographics We collected a comprehensive set of demographic and clinical information, including gender, age, body mass index (BMI), tobacco use, mechanism of injury, type of injury, size of wound, Injury Severity Score (ISS), APACHE II score, time from injury, time to definitive wound closure, number of surgical operations, units of transfused blood products, and secondary adverse events such as bacteremia and pneumonia.
  • BMI body mass index
  • ISS Injury Severity Score
  • APACHE II score time from injury, time to definitive wound closure, number of surgical operations, units of transfused blood products, and secondary adverse events such as bacteremia and pneumonia.
  • Post-Traumatic Acute Kidney Injury The study cohort consisted of combat-related trauma patients as defined above. Post-traumatic AKI was defined as a new diagnosis of AKI within 72 hours following traumatic injury. Serum creatinine levels were obtained from standard peripheral blood draws done at each operating room (OR) visit for wound irrigation and debridement per protocol as outlined below. Additionally, serum creatinine values obtained incident to routine course of care were utilized for diagnosing and trending AKI. KDIGO Guidelines (KDIGO) (Table 1.) were used for diagnosing AKI in this study. In this otherwise healthy cohort, baseline creatinine levels were obtained through historical electronic medical record review; Essentris/Composite Health Care System (CHCS/AHLTA).
  • CHCS/AHLTA Essentris/Composite Health Care System
  • baseline creatinine was substituted with estimated baseline creatinine derived from the Modification of Diet in Renal Disease (MDRD) equation assuming a glomerular filtration rate of 75 mL/min per 1.73 m 2 (Earley, 2012, Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review) The lowest of the creatinine values was used as a surrogate for baseline creatinine. Urine output was not uniformly available in the patient population and was therefore not referenced as a diagnostic measure of AKI. Table 1 Kidney Disease Improving Global Outcomes Criteria for AKI
  • Serum Cytokine and Chemokine Sample collection Samples were collected as described previously 12. In brief, 8 m L of peripheral blood was drawn during each operating room (OR) visit for wound irrigation and debridement, typically on admission, at 48 and 72 hours post-admission. All serum samples were separated immediately using a centrifuge at 2,500 g for 10 minutes. Serum and supernatant samples were transferred to labeled polypropylene tubes, flash-frozen in liquid nitrogen, and stored at -80 C until analysis.
  • Cytokine and chemokine analysis - Serum samples were filtered using a 0.65-mm filter (Millipore, Billerica, MA) and evaluated using a Human Cytokine 30-plex panel supplemented with a custom Human 2-plex kit (cat. no.
  • I L interleukin
  • I L-la interleukin
  • I L-lb interleukin
  • I L-1RA interleukin
  • I L-2 interleukin-2
  • IL- 2R interleukin-2
  • I L-3 IL-4
  • IL-5 I L-6
  • I L-7 IL-8
  • IL-10 I L-12
  • I L-13 IL-15
  • I L-17 granulocyte macrophage colony- stimulating factor
  • G-CSF granulocyte colony-stimulating factor
  • I FN interferon
  • I FN interferon
  • I FN tumor necrosis factor alpha
  • EG F epidermal growth factor
  • bFGF basic fibroblast growth factor
  • HGF hepatocyte growth factor
  • VEGF vascular endothelial growth factor
  • eotaxin monocyte chemotactic protein-1 ( MCP-1), macrophage inflammatory protein 1 alpha (MI)
  • Multivariate analysis was conducted using machine learning techniques. Bayesian Belief Networks were used to perform variable selection and Random Forest was used to construct a binary classification model. We use transformed data to accommodate non-normality to choose the nodes of a Bayesian Belief Network (BBN). The BBN was searched using various algorithms to find a minimal set of variables for downstream modeling. This minimal set of variables describes a Bayesian Belief Network that represents the underlying joint distribution of the entire variable set. From the previously created Bayesian Belief Network variables we constructed a random forest model with built in three-fold cross validation that classifies a patient as ⁇ or 'No ⁇ .
  • BBN Bayesian Belief Network
  • Patient characteristics are summarized in Table 2.
  • the patient population was exclusively male, with a mean age of 23 and average BM I of 25.1.
  • Fifteen patients (20.5%) met the diagnostic criteria for AKI.
  • Twelve patients (16.4%) met the AKI criteria within 48 hours of injury, while 2 (2.7%) were diagnosed within 7 days post-injury and, 1 (1.3%) was diagnosed greater than 7 days post- injury.
  • KDIGO Stages 1, 2, 3 were identified in 12 ( 16.4%), 1 (1.3%), and 2 (2.7%) patients respectively.
  • Post-Traumatic AKI can be associated with clinical factors, markers of systemic inflammatory response, and prolonged surgical treatment of extremity wounds
  • Multivariate modeling of AKI with specific clinical factors along with systemic cytokines and chemokines revealed close conditional dependence relationships between AKI and the following variables: AIS Chest, AIS Head, AIS Skin, Wound Length, Wound Mechanism, Serum biomarkers IL-la, IL-2R, IL-7, MIG, and VEGF (Figure 2).
  • AIS Chest AIS Head
  • AIS Skin Wound Length
  • Wound Mechanism Serum biomarkers IL-la
  • IL-2R IL-2R
  • IL-7 IL-7
  • MIG vascular endothelial growth factor
  • NCI nosocomial infections
  • VEGF is a mediator of the renoprotective effects of multipotent marrow stromal cells in acute kidney injury. J. Cellular and Molecular Medicine 2009, 13, 2109-14; Yuan, VEGF-modified human embryonic mesenchymal stem cell implantation enhances protection against cisplatin-induced acute kidney injury. Renal physiology 2011, 300, F207-18).
  • human anti-VEGF therapies have been associated with nephrotoxicity (Lameire, Nephrotoxicity of recent anti-cancer agents. Clinical kidney journal 2014, 7 (1), 11-22).
  • the inflammation associated cytokines of the innate and adaptive immune response namely: IL-la, IL-2R, and I L-7 have similarly been associated with inflammatory related mechanisms of various forms of AKI and acute renal failure (Garlanda, The interleukin-1 family: back to the future. Immunity 2013, 39, 1003-18; Kadiroglu, The evaluation of effects of demographic features, biochemical parameters, and cytokines on clinical outcomes in patients with acute renal failure. Renal failure 2007, 29, 503-8; Komada, Role of N LRP3 Inflammasomes for Rhabdomyolysis-induced Acute Kidney Injury. Scientific Reports 2015, 5, 10901; Swaminathan, Emerging therapeutic targets of sepsis-associated acute kidney injury. Seminars in Nephrology 2015, 35, 38-54). Taken together, these findings and the multivariate model suggest an important association between specific biomarkers and AKI in addition to identify candidate biomarkers for future studies.
  • AKI as defined by KDIGO is common among modern-day military casualties.
  • Injury type size of wound
  • ISS Injury Severity Score
  • APACHE I I score time from injury, time to definitive wound closure, number of surgical operations in CON US, units of transfused blood products, and secondary adverse events such as bacteremia and pneumonia were all associated with increased risk of AKI.
  • the systemic cytokine response and the need for prolonged treatment with hemoderivates, in addition to occurrence of remote or polytrauma may have predictive value in identifying patients with AKI based on statistical models.
  • prolonged times to definitive wound closure in more severely injured patients may increase their risk of developing nosocomial infections.

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Abstract

La présente invention concerne des méthodes permettant de déterminer si un sujet présente un risque accru de développer une lésion rénale aiguë (AKI) avant l'apparition de quelconques symptômes détectables associés. Ces méthodes comprennent l'analyse d'au moins un échantillon du sujet pour déterminer une valeur du profil de risque du sujet, et la comparaison de la valeur du profil de risque du sujet à la valeur d'un profil de risque normal. Une modification de la valeur du profil de risque du sujet, au-dessus ou au-dessous des valeurs normales indique que le sujet présente un risque accru d'avoir ou de développer des symptômes associés à AKI avant l'apparition de quelconques symptômes détectables associés.
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