CN113903460A - System for predicting severe acute pancreatitis and application thereof - Google Patents
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Abstract
The invention belongs to the field of biomedicine, and particularly relates to a system for predicting severe acute pancreatitis and application thereof; the system comprises a calculating device for calculating the disease risk according to the heart rate, the respiratory rate, the serum calcium concentration and the blood urea nitrogen. Meanwhile, the invention also provides application of the system in preparation of products for calculating the risk of severe acute pancreatitis.
Description
Technical Field
The invention belongs to the field of biomedicine, and particularly relates to a system for predicting severe acute pancreatitis and application thereof.
Background
Acute Pancreatitis (AP) is an inflammatory response that results in the activation of pancreatic enzymes in the pancreas, causing the pancreatic tissue to self-digest, edema, hemorrhage and even necrosis. Clinically, the medicine is characterized by acute epigastric pain, nausea, vomiting, fever, pancreatin increase and the like. Acute pancreatitis is a common digestive emergency, with most patients diagnosed as Mild Acute Pancreatitis (MAP) or Moderate Severe Acute Pancreatitis (MSAP) due to Mild organ injury and self-limiting course. 10-20% of patients develop Severe Acute Pancreatitis (SAP) due to critical illness with persistent organ failure. Severe acute pancreatitis is acute pancreatitis accompanied with systemic and local severe complications, belongs to a special type of acute pancreatitis, and is acute abdomen with fierce illness and high disease death rate. In the 80 s of the 20 th century, most severe acute pancreatitis cases die of early stage of disease, and in recent 10 years, with the progress of SAP surgical treatment, the cure rate is improved, but the overall death rate is still about 17%.
To date, treatment options for severe acute pancreatitis are very limited, and it is therefore necessary to identify patients at high risk as early as possible and take intervention to prevent progression from mild or moderate severe acute pancreatitis to severe acute pancreatitis. The diagnosis of severe acute pancreatitis mainly comprises four aspects: clinical presentation, physical examination, laboratory examination, and imaging examination. Currently, some clinical practice guidelines recommend using scoring systems to predict the severity of the onset of acute pancreatitis, such as the APACHE-ii score (18 indices), the Ranson score system (11 indices), and the bedside index of the severity of acute pancreatitis (biasap). Taking BISAP as an example, BISAP is an abbreviation of five indexes, B is BUN (blood urea nitrogen), I is impact (consciousness disturbance), S is SIRS (systemic inflammatory response syndrome), A is age (age), and P is pleural effusion, each index of the system has an option of 0 point or 1 point, the total point is 0-5 points, and when the total point is more than or equal to 3 points, the risk of suffering from moderate severe acute pancreatitis or severe acute pancreatitis exists. The above method relies on the imaging examination, and is complex to use in practice, so that the emergency department doctor needs a scoring standard independent of the imaging examination.
Disclosure of Invention
In order to provide a simple model more suitable for clinic, multi-center retrospective cohort research is carried out, and a system for predicting the possibility of the acute pancreatitis to be advanced to Severe Acute Pancreatitis (SAP) and application thereof are developed and externally verified.
Method
In one aspect, the present invention provides a method of calculating the risk of severe acute pancreatitis, the method comprising: the following information was collected from the subjects: heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca), Blood Urea Nitrogen (BUN); and calculating the risk of the disease.
Preferably, the risk of disease is calculated by the following formula:
the risk of disease =1/(1+ exp (- (-6.42+0.05 × HR +0.08 × RR-1.30 × CA +0.14 × BUN)));
alternatively, the calculations were performed using the nomogram shown in figure 2 of the drawings of the specification.
The Nomogram (Nomogram), which is also called Alignment Diagram, converts a complex regression equation into a visual graph, so that the result of a prediction model is more readable, and a patient can be conveniently evaluated. The nomogram comprises a score scale corresponding to a plurality of variables, a total score scale and a disease risk variable of each patient, and each score scale and the total score scale respectively occupy one line in an alignment chart. When using nomogram, a score is taken on the variable score scale for each variable based on patient information, and the scores for each variable are summed to give a total score. Each overall score corresponds to a respective risk of contracting a disease value. And calculating the total score according to the information of the patient, so that the risk of the severe acute pancreatitis of the subject can be known.
Preferably, the nomogram of the present invention includes four variables: heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca), Blood Urea Nitrogen (BUN).
Preferably, the HR ranges from 50 to 170.
Preferably, the value of the RR ranges from 10-45.
Preferably, the value of Ca ranges from 1.0 to 3.6.
Preferably, the value range of BUN is 0-40.
Preferably, the score of the total score is in the range of 0 to 240.
When the SAP reading device is used, variable scores are read according to the heart rate, the breathing frequency, the serum calcium concentration and the blood urea nitrogen of a subject, and the total score is calculated, so that the SAP risk probability of the subject can be read on a nomogram.
Preferably, the subject is an Acute Pancreatitis (AP) patient, i.e., the risk of developing is the probability that an acute pancreatitis patient will progress further to severe acute pancreatitis.
Index combination
In another aspect, the invention provides an index combination for calculating the risk of severe acute pancreatitis, wherein the index combination comprises Heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca) and Blood Urea Nitrogen (BUN).
Preferably, the combination of indicators may be used in conjunction with other patient information.
Preferably, the other patient information includes, but is not limited to: age, gender, body temperature, systolic blood pressure, Glasgow Coma Score (GCS), white blood cells, hematocrit, platelets, serum electrolyte concentration (K, Na), creatinine (Cr), blood glucose, pleural effusion.
Preferably, the index combination can also be used with other scoring systems.
Preferably, the other scoring system comprises: glasgow score (Imrie criteria), APACHE-II score, BISAP score, Balthazar score, Ranson score System.
Model construction method
In another aspect, the invention provides a method for constructing a model for calculating the risk of acute pancreatitis, the method comprises constructing the model by using the index combination.
Preferably, the method of constructing the model includes, but is not limited to, regression analysis.
Preferably, the regression analysis comprises linear regression, logistic regression, Cox regression.
Preferably, the regression analysis is multivariate logistic regression (multivariate logistic regression).
Preferably, the model may be in the form of a formula or a nomogram.
Preferably, the formula is:
PA=1/(1+exp(-(-6.42+0.05×HR+0.08×RR-1.30×CA+0.14×BUN)))。
preferably, the nomogram is as shown in figure 2.
Model (model)
In another aspect, the invention provides a model constructed by the model construction method; specifically, the model is a model for calculating the risk of the severe acute pancreatitis, which comprises four indexes of Heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca) and Blood Urea Nitrogen (BUN);
preferably, the model may be in the form of a formula or a nomogram.
Preferably, the formula is:
PA=1/(1+exp(-(-6.42+0.05×HR+0.08×RR-1.30×CA+0.14×BUN)))。
preferably, the nomogram is as shown in figure 2.
System for controlling a power supply
In another aspect, the invention provides a system for calculating the risk of acute pancreatitis, the system comprises a calculating device for calculating the risk of acute pancreatitis according to Heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca) and Blood Urea Nitrogen (BUN).
Preferably, the calculation is based on the aforementioned model.
Preferably, the calculation is performed by the calculation formula:
PA=1/(1+exp(-(-6.42+0.05×HR+0.08×RR-1.30×CA+0.14×BUN)))；
alternatively, the calculations were performed using the nomogram shown in figure 2 of the drawings of the specification.
Preferably, the system comprises detection means for detecting Heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca), Blood Urea Nitrogen (BUN).
Preferably, the system comprises a collection device for collecting Heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca), Blood Urea Nitrogen (BUN).
Preferably, the system includes an output device that outputs the calculation result.
Preferably, the system further comprises a result display unit for displaying the conclusion drawn by the computing device.
Preferably, the display device can also be intelligent hardware with a display (such as a weight scale, a body fat scale, a sphygmomanometer, a blood glucose meter, a blood lipid meter, an electrocardio sticker, an intelligent bracelet and the like).
Preferably, the system may further comprise wireless communication means and/or information storage means.
Preferably, the system comprises a detection device, a collection device, a calculation device, an output device and a display device in sequence.
Preferably, the devices in the system can be connected in a wired mode and/or a wireless mode; further, the wireless connection mode can be wireless local area network, Bluetooth, infrared ray and the like; the wired connection mode can be a fixed telephone network and the like. By adopting the connection mode, the use of the prediction system/device by a user can be greatly facilitated, and meanwhile, accurate prediction of the risk probability of further developing severe acute pancreatitis can be provided for a subject by means of increasingly developed information technology and increasingly popularized network resources.
Preferably, the subject includes any human.
More preferably, the subject is an Acute Pancreatitis (AP) patient.
Device
In another aspect, the present invention provides an apparatus for calculating the risk of severe acute pancreatitis, the apparatus comprising: a memory and a processor;
the memory is to store program instructions;
the processor is configured to invoke program instructions that, when executed, are configured to: based on the following patient information: heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca), Blood Urea Nitrogen (BUN); the risk probability, i.e. the probability of a patient with Acute Pancreatitis (AP) developing into a patient with Severe Acute Pancreatitis (SAP), is calculated.
Preferably, the following operations are performed:
1) detecting Heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca), Blood Urea Nitrogen (BUN);
2) collecting Heart Rate (HR), Respiratory Rate (RR), serum calcium concentration (Ca), Blood Urea Nitrogen (BUN);
3) and calculating the risk probability.
Computer readable storage medium
In another aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the aforementioned method for calculating the risk of acute severe pancreatitis.
This "computer-readable storage medium" as used herein includes, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. Specifically, the computer-readable storage medium includes: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Applications of
The index combination, the model constructed by the model construction method, the model, the system, the equipment and the computer readable storage medium are applied to preparing products for calculating the risk of acute severe pancreatitis.
Preferably, the subject is an Acute Pancreatitis (AP) patient, i.e., the risk of developing is the probability that an acute pancreatitis patient will progress further to severe acute pancreatitis.
Implementation of the "method, system, apparatus, computer-readable storage medium" described herein may include performing or completing selected tasks manually, automatically, or a combination thereof.
Moreover, according to actual instrumentation and equipment of embodiments of the method, system of the present invention, a number of selected tasks could be implemented by hardware, by software, or by firmware, or by a combination thereof using an operating system.
Drawings
FIG. 1 is a LASSO regression extracted SAP predictor.
FIG. 2 is a Nomous graph of SAP diagnostics based on multiple logistic regression.
FIG. 3 is a ROC curve for the SAP model, A: ROC curve of SAP model in development queue, B: the SAP model is the ROC curve in the validation queue.
FIG. 4 is a quantitative calibration curve for the SAP model.
FIG. 5 is a decision curve analysis of the SAP model.
Detailed Description
The present invention will be further described with reference to the following examples, which are intended to be illustrative only and not to be limiting of the invention in any way, and any person skilled in the art can modify the present invention by applying the teachings disclosed above and applying them to equivalent embodiments with equivalent modifications. Any simple modification or equivalent changes made to the following embodiments according to the technical essence of the present invention, without departing from the technical spirit of the present invention, fall within the scope of the present invention.
Example 1 development and verification of model
Subject information
The present invention was conducted as a multicenter retrospective study. Patients diagnosed with AP from different areas in China (Beijing coordination Hospital, Beijing sixth Hospital, Harbin medical university fourth subsidiary Hospital, Sichuan university Huaxi Longquan Hospital) between 2017, 1 month and 2019, 12 months and 31 days are collected as development queues to participate in establishing clinical prediction models. An independent external validation cohort consisted of AP patients attending the hospital in the peking cooperation from 1/2020 to 5/31/2021.
An AP is diagnosed if at least two of the following three criteria are met: abdominal pain consistent with acute pancreatitis manifestations, elevation of serum amylase and/or lipase by more than three times the upper limit of the normal range, and compliance with acute pancreatitis manifestations as seen by Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or ultrasound examinations. SAP was determined by organ failure duration >48 hours.
Clinical variables
Demographic and laboratory data were collected from the electronic medical record system over 24 hours of admission: age, sex, body temperature (Tem), Heart Rate (HR), Respiratory Rate (RR), Systolic Blood Pressure (SBP), Glasgow coma score (GCS, Glasgow coma score), White Blood Cells (WBC), Hematocrit (HCT), Platelets (PLT), serum electrolyte concentrations (K, Na, Ca), creatinine (Cr), Blood Urea Nitrogen (BUN), Glucose (GLU). At the same time, other clinical information such as date of admission, local complications, length of stay, Intensive Care Unit (ICU) stay, mortality, ventilator usage were collected. Wherein, the weekend admission refers to saturday admission and sunday admission; local complications include acute peripancreatic fluid accumulation, acute necrotic mass accumulation, pseudocysts, and encapsulated necrosis. Ventilators include invasive or non-invasive mechanical ventilation. The scores of qSOFA (quick sequential organic failure assessment), SIRS (systematic in-flight response), and BISAP (binary index for sensitivity in access networks) were calculated based on the above data. Cases with incomplete data were not included in the final study.
The final development cohort included 407 AP patients and the external validation cohort included 190 AP patients, with patient details as shown in table 1 below.
TABLE 1 comparison of SAP and non-SAP patient information
Statistical analysis
Categorical variables are expressed in terms of frequency and percentage and compared using the χ 2 test or Fisher exact test. The continuous variables of the normal distribution are represented as mean ± Standard Deviation (SD) and compared using the two-sided student t-test. Non-normally distributed continuous variables are represented by median and interquartile range (IQR) and compared to the Mann-Whitney U-test.
The continuous variables were analyzed in their original form to retain information. The predictors in the development queue are selected using a Least Absolute Shrinkage and Selection Operator (LASSO) regression method. The following four variables were extracted by LASSO regression: HR, RR, Ca, BUN. That is, HR, RR, Ca, BUN are predictors of SAP (FIG. 1, Table 2).
TABLE 2 LASSO regression analysis for predictor extraction from development cohorts
Model performance verification
Multiple logistic regression (multivariate logistic regression) showed that all four variables were independent predictors (table 3).
TABLE 3 multivariate logistic regression analysis of SAP predictions in development cohorts
R4.0.3 (https:// www.r-project. org /) and MedCalc15.8 statistical software were used for all statistical analyses. Double side p<0.05 was considered statistically significant. Nomogram (nomogram) and calibration curve usermsPacket drawing, DCA usagermdaAnd (6) drawing the package. ROC is plotted using medcalc 15.8.
Establishing a nomogram based on multiple logistic regression (fig. 2), the probability of SAP (PA, likelihood) can be calculated according to the following equation:
SAP：PA=1/(1+exp(-(-6.42+0.05×HR+0.08×RR-1.30×CA+0.14×BUN)))；
bootstrap (bootstrap) was performed 1000 times in the original model to minimize the risk of overfitting, with AUC 0.879 (95% CI, 0.830-0.928) at 25% cutoff values in the development cohort for prediction of SAP not lower than the BISAP score (AUC =0.888, 95% CI 0.847-0.929, p = 0.6629), but significantly better than SIRS (AUC =0.808, 95% CI 0.757-0.859, p = 0.002) and qSOFA (AUC =0.730, 95% CI 0.672-0.789, p < 0.001) (figure 3, table 5). In addition, the model also showed good behavior in the validation cohort (AUC =0.898, 95% CI 0.848-0.949) (fig. 3).
When the cutoff for SAP prediction (cut-off) was set to PA >25% (i.e. when PA >25%, the patient was considered to be progressing to SAP), the new model performed well in the combined dataset (sensitivity 0.78, specificity 0.88) (table 4).
TABLE 4 model Effect in predicting SAP
The ability of each index in the model to predict SAP alone was calculated, and each index alone also showed a better prediction function, as shown in table 5.
TABLE 5 Effect of index alone in predicting SAP
The Receiver Operating Characteristic (ROC) curve and the area under the ROC curve (AUC) were used to evaluate the discriminatory power of the predictive model. Calibration curves were drawn to assess the prediction accuracy of the model, which reflects the consistency between model predictions and observations. Well-calibrated models show predictions lying on or near the 45 ° line of the calibration plot. The Hosmer-Lemeshow (H-L) goodness-of-fit test was used to quantify the calibration curves. A p value >0.05 in the H-L assay indicates good agreement between model prediction and standard diagnostic criteria. Decision Curve Analysis (DCA) is used to assess the clinical utility of the model, which shows the relationship between the model's predicted probability threshold and the net benefit relative value.
The H-L test showed that the difference between the predicted and observed results was not significant in both the development cohort (χ 2=12.675, p = 0.124) and the validation cohort (χ 2=5.852, p = 0.664). Graphical evaluation showed verification in two new models (fig. 4).
DCA showed that if the threshold PA was <80%, there was a positive net benefit over full or no treatment using the new model to identify and manage SAP (fig. 5).
Claims (9)
1. A method of constructing a model for calculating risk of severe acute pancreatitis, the method comprising constructing a model using at least one of heart rate, respiratory rate, serum calcium concentration, blood urea nitrogen.
2. The method of claim 1, wherein the method of constructing the model includes, but is not limited to, regression analysis.
3. A model constructed by the method of claim 1.
4. A system for calculating the risk of acute pancreatitis comprises a calculating device for calculating the risk of acute pancreatitis according to at least one of heart rate, respiratory rate, serum calcium concentration and blood urea nitrogen.
5. The system of claim 4, wherein the calculation of the risk of disease is based on a model constructed by the method of claim 1 or the model of claim 3.
6. The system of claim 4 or 5, further comprising any one or more of: detection device, collection device, calculating device, output device, display device.
7. An apparatus for calculating risk of severe acute pancreatitis, the apparatus comprising: a memory and a processor;
the memory is to store program instructions;
the processor is configured to invoke program instructions that, when executed, are configured to:
1) collecting at least one of the following: heart rate, respiratory rate, serum calcium concentration, blood urea nitrogen;
2) calculating the risk of the disease;
or the like, or, alternatively,
1) detecting at least one of: heart rate, respiratory rate, serum calcium concentration, blood urea nitrogen;
2) and (4) calculating the disease risk according to the detection result of the step (1).
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of calculating a risk of severe acute pancreatitis: based on the subject's information: heart rate, respiratory rate, serum calcium concentration, blood urea nitrogen, and calculating the risk of the disease.
9. Use of heart rate, respiratory rate, serum calcium concentration, blood urea nitrogen, a model constructed by the method of claim 1, a model of claim 3, a system of claim 4, an apparatus of claim 7, and a computer-readable storage medium of claim 8 in the preparation of a product for calculating the risk of severe acute pancreatitis.
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