Prediction model of glomerular filtration rate after cardiopulmonary resuscitation, construction method and application thereof

Abstract

The application discloses a prediction model of glomerular filtration rate after cardiopulmonary resuscitation, a construction method and application thereof, which comprises sample data collection and preprocessing; determining independent variables, dependent variables and outcome variables; screening independent influencing factors, and evaluating the relationship between the independent influencing factors based on a variance inflation factor; constructing a nomogram prediction model based on the independent influencing factors; performing accuracy evaluation on the prediction model based on accuracy and mean absolute error; performing calibration degree analysis on the prediction model through a calibration curve, Bland-Altman consistency evaluation and paired T test; evaluating the prediction performance of the prediction model through an ROC curve and AUC; and verifying the universality and extrapolation of the prediction model. Through construction of the early prediction model of glomerular filtration rate after cardiopulmonary resuscitation, the application can predict the probability of chronic kidney disease within 24 hours, realize early discovery and early intervention, and thus delay disease progression.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of prediction model construction, in particular to a prediction model for glomerular filtration rate after cardiopulmonary resuscitation, a construction method and application thereof. BACKGROUND

[0002] Cardiopulmonary resuscitation (CPR) is recognized worldwide as the most effective method for treating cardiac arrest (CA). After resuscitation, the body suffers from systemic ischemia-reperfusion injury, leading to multiple organ dysfunction, which is an important factor leading to high mortality after resuscitation. The kidney tissue is particularly sensitive to ischemia, and once damaged, the probability of developing chronic kidney disease (CKD) or even end-stage renal disease (ESRD) will increase significantly, and the risk of death will also increase. Therefore, early dynamic assessment of kidney function and identification of high-risk patients with CKD can provide early intervention "time window" for treatment, which is of great significance for reducing high mortality after resuscitation.

[0003] The current Kidney Disease: Improving Global Outcomes (KDIGO) guidelines for the diagnosis of CKD have a 3-month time window, which cannot assess the risk of CKD in the early post-resuscitation period, thereby missing the optimal treatment window and leading to high mortality.

[0004] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The technical task of the present application is to solve the above problems, and provide a prediction model for glomerular filtration rate after cardiopulmonary resuscitation, a construction method and application thereof. The present application can predict the probability of chronic kidney disease within 24 hours by constructing an early prediction model for glomerular filtration rate after cardiopulmonary resuscitation, achieve early detection and early intervention, thereby delaying disease progression and ultimately improving the prognosis of patients with cardiac arrest.

[0006] To achieve the above object, the present application provides the following technical scheme:

[0007] According to an aspect of the present application, a method for constructing a post-cardiac arrest glomerular filtration rate prediction model is provided, comprising: sample data collection and preprocessing; determining independent variables, dependent variables and outcome variables; screening independent influencing factors, and evaluating the relationship between the independent influencing factors based on the variance inflation factor; constructing a nomogram prediction model based on the independent influencing factors; evaluating the accuracy of the prediction model based on the accuracy and mean absolute error; analyzing the calibration degree of the prediction model through the calibration curve, Bland-Altman consistency evaluation and paired T test; evaluating the prediction performance of the prediction model through the ROC curve and AUC; and verifying the universality and extrapolation of the prediction model.

[0008] In some embodiments, the preprocessing comprises: screening sample data meeting the inclusion criteria based on the inclusion criteria, wherein the inclusion criteria comprises age ≥ 18 years and ROSC time after CA resuscitation > 24 h, and the exclusion criteria comprises baseline eGFR < 60 mL / min / 1.73m 2 or a history of kidney-related diseases or missing clinical data > 15%.

[0009] In some embodiments, the independent variables include age, gender, body mass index, cardiovascular disease history, diabetes history, hypertension history, no-flow time, ROSC time, epinephrine dosage, white blood cells, hemoglobin, platelets, total cholesterol, triglycerides, albumin, globulin, glutamic-pyruvic transaminase, glutamic-oxalacetic transaminase, baseline eGFR, urea nitrogen, uric acid, blood glucose, glycosylated hemoglobin, potassium, sodium, calcium, lactic acid, brain natriuretic peptide, troponin, D-dimer, proteinuria, 24h urine volume and urine albumin / creatinine ratio; the dependent variable is the eGFR value measured on the 7th day; and the outcome variable is the number of cases of chronic kidney disease diagnosed according to the standard on the 90th day.

[0010] In some embodiments, the independent influencing factors include age, diabetes, no-flow time, baseline eGFR, ACR and lactic acid.

[0011] According to another aspect of the present application, a prediction model constructed by the method for constructing a post-cardiac arrest glomerular filtration rate prediction model is also provided, and the linear regression equation of the prediction model is Y = 89.439-0.344 x age-3.610 x hypertension-2.992 x diabetes-0.577 x no-flow time+0.0349 x baseline eGFR-0.042 x ACR-0.650 x lactic acid, wherein Y is the eGFR value on the 7th day after ROSC after cardiac arrest resuscitation.

[0012] According to another aspect of the present application, there is further provided a prediction model constructed by the method for constructing a glomerular filtration rate prediction model after cardiopulmonary resuscitation or application of the prediction model in predicting the level of kidney function after resuscitation to autonomous circulation recovery after cardiac arrest.

[0013] According to another aspect of the present application, there is further provided a prediction model constructed by the method for constructing a glomerular filtration rate prediction model after cardiopulmonary resuscitation or application of the prediction model in predicting the risk of chronic kidney disease after resuscitation to autonomous circulation recovery after cardiac arrest.

[0014] In some embodiments, the application predicts the risk of chronic kidney disease within 24 hours.

[0015] Compared with the prior art, the present application has the advantages and positive effects that: by constructing an early prediction model of glomerular filtration rate after cardiopulmonary resuscitation, the present application can predict the probability of chronic kidney disease within 24 hours, realize early detection and early intervention, thereby delaying disease progression, and ultimately improve the prognosis of patients with cardiac arrest. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0017] Figure 1 A method flowchart in the embodiments of the present application is shown.

[0018] Figure 2 A nomogram prediction model in the embodiments of the present application is shown.

[0019] Figure 3 A calibration curve scatter plot of the prediction model in the embodiments of the present application is shown.

[0020] Figure 4 A Bland-Altman consistency evaluation of the prediction model in the embodiments of the present application is shown.

[0021] Figure 5 A Paired-T test analysis of the prediction model in the embodiments of the present application is shown.

[0022] Figure 6 A ROC curve of the prediction model in the embodiments of the present application is shown.

[0023] Figure 7 A calibration scatter plot of the externally validated prediction model in the embodiments of the present application is shown.

[0024] Figure 8 The ROC curve of the external validation prediction model in the embodiments of the present application is shown. DETAILED DESCRIPTION

[0025] In order to enable the above-mentioned objects, features and advantages of the present application to be more clearly understood, the present application will be further described below with reference to the drawings and embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0026] The present application will be further described below with reference to the drawings and specific embodiments.

[0027] S100, sample data collection and pretreatment

[0028] Retrospective collection of clinical data of patients hospitalized after cardiac arrest (CA) resuscitation in Tianjin Medical University General Hospital Airport Hospital from January 2017 to December 2019 and in Tianjin Medical University General Hospital from January 2017 to June 2024. Diagnosis of chronic kidney disease (CKD) refers to the diagnostic criteria of the Kidney Disease Improving Global Outcomes (KDIGO): estimated glomerular filtration rate (eGFR) <60 mL / min / 1.73m 2 Last more than 90d or kidney injury occurs. Among them, kidney injury includes: (1) proteinuria (24h urine protein amount more than 0.15g); (2) abnormal urine sediment; (3) renal tubular related lesions; (4) renal pathological histological abnormalities; (5) structural abnormalities in the urinary system image; (6) history of kidney transplantation.

[0029] Glomerular filtration rate (glomerular filtration rate, GFR) is the gold standard for evaluating the functional status of the kidney, especially in the diagnosis and monitoring of kidney disease.

[0030] The inclusion criteria of this study are: (1) age ≥18 years old; (2) ROSC time after CA resuscitation >24h. Exclusion criteria: (1) baseline eGFR <60 mL / min / 1.73m 2 ; (2) more than 15% of the clinical data are missing.

[0031] In this study, the included patients were randomly divided into training group and validation group in a ratio of 7:3.

[0032] S200, screening of related variables and determination of independent variables, dependent variables and outcome variables.

[0033] Since direct measurement of glomerular filtration rate (GFR) is complicated, estimation formula (eGFR) based on serum markers is widely used in clinical practice, which represents the amount of plasma filtered through the glomerulus per unit of time (per minute). Guidelines recommend using the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) formula to calculate eGFR, which takes into account age, gender, and serum creatinine levels.

[0034] The eGFR formula is:

[0035] (1) Female: when Scr≤0.7 mg / dL,

[0036] ;

[0037] When Scr>0.7 mg / dL:

[0038] ;

[0039] (2) Male: when Scr≤0.9 mg / dL,

[0040] ;

[0041] When Scr>0.9 mg / dL,

[0042] ;

[0043] Where Scr is the serum creatinine (mg / dL) level.

[0044] Combined with the guidelines and clinical significance, 62 variables related to GFR and CKD were preliminarily screened. In data preprocessing, variables with strong correlation with other independent variables were excluded, and variables with data missing>15% were excluded; for data missing≤15%, normal distribution was supplemented with mean, and skewed distribution was supplemented with median.

[0045] Finally, 33 independent variables were established, including (1) demographic information: age, gender, body mass index; (2) underlying diseases and personal family history: history of cardiovascular disease, history of diabetes, history of hypertension; (3) cardiopulmonary resuscitation related indicators: no blood flow time (i.e. the time interval from the occurrence of CA to the start of CPR), ROSC time, adrenaline dosage; (4) first laboratory test results within 24 hours after resuscitation: white blood cells, hemoglobin, platelets, total cholesterol, triglycerides, albumin, globulin, glutamic-pyruvic transaminase, glutamic-oxalacetic transaminase, baseline eGFR (Scr is the first detection value after resuscitation), urea nitrogen, uric acid, blood glucose, glycosylated hemoglobin, blood potassium, blood sodium, blood calcium, lactic acid, brain natriuretic peptide, troponin, D-dimer, proteinuria, 24-hour urine volume, albumin-to-creatinine ratio (ACR). Dependent variable: eGFR value measured on the 7th day (7d-eGFR). Outcome variable: number of cases diagnosed as CKD according to standard diagnosis on the 90th day. Table 1 shows the comparison of clinical baseline characteristics of the training group and the validation group in the present application.

[0046] Table 1 Comparison of clinical baseline characteristics of the training group and the validation group

[0047]

[0048] Note: BMI is body mass index, ROSC is return of spontaneous circulation, ALT is glutamic-pyruvic transaminase, AST is aspartate aminotransferase, BNP is B-type natriuretic peptide, ACR is albumin-to-creatinine ratio, eGFR is estimated glomerular filtration rate, and CKD is chronic kidney disease.

[0049] S300, screen independent influencing factors, and evaluate the relationship between the independent influencing factors based on the variance inflation factor.

[0050] SPSS 25.0 and R software (version 4.3.2) were used for data analysis and plotting. Normally distributed measurement data were expressed in the form of x ± s, and two independent sample t tests were used for comparison between the two groups; non-normally distributed measurement data were expressed in the form of median (quartile) [ ( ), ( ) ], and Mann-Whitney U tests were used for comparison between the two groups. Count data were expressed as the number and / or percentage, and χ2tests were used for comparison between the two groups. s M Q1 Q3 U

[0051] ​​​​​Firstly, single factor linear analysis was used to screen out the variables with P<0.05 for inclusion in the multiple factor linear regression model (backward method) to obtain independent influencing factors, and the variance inflation factor (variance inflation factor, VIF) was used to evaluate the relationship between the influencing factors (VIF>5 was considered to have multicollinearity).

[0052] Table 2 shows the single factor and multiple factor linear regression analysis in the embodiments of the present application. The multiple factor linear regression analysis shows that age (β=-0.344, 95%CI:-0.528~ -0.160, P<0.001), hypertension (β=-3.610, 95%CI:-5.968~ -1.252, P<0.01), diabetes (β=-2.992, 95%CI:-5.295~ -0.689, P<0.05), blood flow time (β=-0.577, 95%CI:-0.996~ -0.158, P<0.01), baseline eGFR (β=0.349, 95%CI:0.269~0.429, P<0.001), ACR (β=-0.042, 95%CI:-0.073~ -0.011, P<0.05), and lactic acid (β=-0.650, 95%CI:-1.214~ -0.086, P<0.05) are independent influencing factors.

[0053] Table 2 Single factor and multiple factor linear regression analysis

[0054]

[0055] In addition, no multicollinearity was observed between the variables (VIF<5): age VIF=1.105; hypertension VIF=1.157; diabetes VIF=1.116; blood flow time VIF=1.078; baseline eGFR VIF=1.043; ACR VIF=1.091; and lactic acid VIF=1.184.

[0056] S400, constructing a nomogram prediction model based on the independent influencing factors.

[0057] A nomogram prediction model was constructed based on the independent influencing factors, and was verified in the validation group.

[0058] Figure 2 The nomogram prediction model in the embodiments of the present application is shown. According to each index, a vertical line is drawn to the score axis to obtain a score, and finally the scores are added to obtain a total score. The vertical line downward of the total score corresponds to the predicted value of 7d-eGFR of the patient after resuscitation.

[0059] As Figure 2As shown, age (55, 60, 65, 70, 75, 80, 85, 90) corresponds to scores (49, 42, 35, 28, 21, 14, 7, 0) points;

[0060] Hypertension (yes, no) corresponds to scores (0, 15) points;

[0061] Diabetes (yes, no) corresponds to scores (0, 12) points;

[0062] No blood flow time (0, 2, 4, 6, 8, 10, 12, 14, 16) corresponds to scores (38, 33, 28, 24, 19, 14, 9, 5, 0) points;

[0063] Lactate (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16) corresponds to scores (37, 35, 32, 29, 27, 24, 24, 21, 19, 16, 13, 11, 8, 5, 3, 0) points;

[0064] ACR (20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280) corresponds to scores (45, 41, 38, 34, 31, 27, 24, 21, 17, 14, 10, 7, 3, 0) points;

[0065] Baseline eGFR (45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115) corresponds to scores (0, 7, 14, 21, 29, 36, 43, 50, 57, 64, 71, 79, 86, 93, 100).

[0066] Total score corresponds to predicted 7d-eGFR value:

[0067] 33 points predict a value of 45 mL / min / 1.73m 2 ; 54 points predict a value of 50 mL / min / 1.73m 2 ; 77 points predict a value of 55 mL / min / 1.73m 2 ; 97 points predict a value of 60 mL / min / 1.73m 2 ; 118 points predict a value of 65 mL / min / 1.73m 2 ; 138 points predict a value of 70 mL / min / 1.73m 2 ; 159 points predict a value of 75 mL / min / 1.73m 2 ; 179 points predict a value of 80 mL / min / 1.73m 2; 200th percentile value is 85 mL / min / 1.73 m 2 ; 220th percentile value is 90 mL / min / 1.73 m 2 ; 241st percentile value is 95 mL / min / 1.73 m 2 ; 261st percentile value is 100 mL / min / 1.73 m 2 ; 282nd percentile value is 105 mL / min / 1.73 m 2 .

[0068] The linear regression equation of the nomogram prediction model is: Y = 89.439 - 0.344 x age - 3.610 x hypertension - 2.992 x diabetes - 0.577 x time without blood flow + 0.0349 x baseline eGFR - 0.042 x ACR - 0.650 x lactic acid.

[0069] S500, accuracy of the prediction model is evaluated based on accuracy and mean absolute error; calibration degree of the prediction model is analyzed by calibration curve, Bland-Altman consistency evaluation and paired T test.

[0070] The accuracy of the training group and the validation group is evaluated by Accuracy analysis and mean absolute error (MAE). The results show that (1) the Accuracy of the training group is 97.07%; the MAE of the training group is 8.546; (2) the Accuracy of the training group is 95.45%; the MAE of the validation group is 8.465.

[0071] The calibration degree is analyzed by calibration curve, Bland-Altman consistency evaluation and Paired-T test.

[0072] Figure 3 The calibration curve scatter plot of the prediction model in the embodiment of the application is shown, wherein, Figure 3 A is the training group, Figure 3 B is the validation group. As Figure 3 shown, the calibration curve scatter plot of the training group: R = 0.928 (95% CI: 0.902-0.946), Accuracy = 97.07%, P < 0.001; indicating that the prediction value of the prediction model has high correlation with the observed value, suggesting that the fitting degree of the model is good. The calibration curve scatter plot of the validation group: R = 0.893 (95% CI: 0.876-0.924), Accuracy = 95.45%, P < 0.001; indicating that the prediction value of the model has high correlation with the observed value, suggesting that the fitting degree of the model is good.

[0073] Figure 4This application illustrates the Bland-Altman consistency evaluation of the prediction model in an embodiment of the present application, wherein... Figure 4 A is the training group. Figure 4 B is the verification group. For example... Figure 4 As shown in the Bland-Altman plot, the horizontal axis represents the mean of the results for each sample measured by the two methods, and the vertical axis represents the difference between the results measured by the two methods. The upper and lower blue horizontal dashed lines in the plot represent the upper and lower limits of the 95% agreement threshold, i.e., 1.96 times the standard deviation; the middle red horizontal solid line represents the mean of the differences; and the red horizontal dashed line indicates the position where the mean of the differences is 0. The higher the degree of agreement between the two measurement methods, the closer the line representing the mean of the differences (the red solid line) is to the line representing the mean of the differences (the red dashed line). Figure 4 Group A's Mean value is 4.5. Figure 4 The Mean value for Group B is 3.6, and most of the differences between the two groups are within the 95% consensus limit, indicating that the prediction results of the two groups have good consistency.

[0074] Figure 5 This paper illustrates the Paired-T test analysis of the prediction model in the embodiments of this application, wherein, Figure 5 A is the training group. Figure 5 B is the verification group. For example... Figure 5 As shown, the training group Paired-T test: P=0.89, indicating that there was no statistically significant difference between the model's predicted values ​​and the actual values ​​(P>0.05), suggesting that the model has a good predictive fit. The validation group Paired-T test: P=0.63, indicating that there was no statistically significant difference between the model's predicted values ​​and the actual values ​​(P>0.05), suggesting that the model has a good predictive fit.

[0075] S600: The predictive power of the linear regression model for CKD is evaluated using ROC curves and AUC.

[0076] The predictive power of the linear regression model for CKD was evaluated using ROC (receiver operating characteristic) curves and AUC (area under the ROC curve). P <0.05 indicates a statistically significant difference.

[0077] Figure 6 The ROC curve of the prediction model in the embodiments of this application is shown. Figure 6 As shown, the prediction model's AUC for predicting CKD occurrence after resuscitation in the training group was 0.882 (95%). CI (0.857~0.925); validation group AUC=0.859 (95%) CI:0.843~0.891), the results of the two groups showed that the model had good prediction performance; the AUC of the two groups was subjected to Delong nonparametric test, and the difference was not statistically significant (P=0.492), indicating that the grouping data was good in homogeneity. P

[0078] S700, verify the universality and extrapolation of the model by external data.

[0079] The same as step S100, the clinical data of 105 patients who successfully underwent CPR in Shenzhen University General Hospital from January 2021 to June 2023 were collected.

[0080] Figure 7 The calibration scatter plot of the external validation prediction model in the embodiment of the application is shown. As shown in Figure 7 , the accuracy analysis and MAE were used to evaluate the accuracy of the external validation group: Accuracy=91.08%; MAE=6.215. The calibration curve scatter plot: R=0.499 (95% CI: 0.386-0.577), P<0.001; indicating that the model prediction value and the observed value result have high correlation, suggesting that the fitting degree of the model is good.

[0081] Figure 8 The ROC curve of the external validation prediction model in the embodiment of the application is shown. As shown in Figure 8 , the eGFR prediction model of the external validation group has an AUC of 0.784 (95% CI :0.720~0.822) for CKD risk prediction, and the results show that the model has good CKD prediction performance.

[0082] Based on multiple linear regression analysis, the early prediction model of GFR is constructed, which not only helps to accurately quantify the level of renal function and guide key treatment decisions such as dialysis timing and drug dose adjustment, but more importantly, it predicts the probability of CKD within 24 hours, realizes early detection and early intervention, thereby delaying disease progression and ultimately improving the prognosis of CA patients.

[0083] Based on the above specific embodiments, those skilled in the art can easily implement the present application. However, it should be understood that the present application is not limited to the above specific embodiments. Based on the disclosed embodiments, those skilled in the art can arbitrarily combine different technical features to realize different technical solutions.​

Claims

1. A method for constructing a predictive model for glomerular filtration rate after cardiopulmonary resuscitation, characterized in that, The method comprises the following steps: Collecting and preprocessing sample data; Determining independent variables, dependent variables and outcome variables; Screening independent influencing factors and evaluating the relationship between the independent influencing factors based on variance inflation factor; Building a nomogram prediction model based on the independent influencing factors, and the linear regression equation of the prediction model is Y = 89.439-0.344×age-3.610×hypertension-2.992×diabetes-0.577×no blood flow time+0.0349×baseline eGFR-0.042×ACR-0.650×lactate, wherein Y is the eGFR value on the 7th day after resuscitation after cardiac arrest and reaching ROSC; Evaluating the accuracy of the prediction model based on accuracy and mean absolute error; analyzing the calibration degree of the prediction model through calibration curve, Bland-Altman consistency evaluation and paired T test; Evaluating the prediction performance of the prediction model through ROC curve and AUC; Verifying the universality and extrapolation of the prediction model; The independent influencing factors are obtained through single factor and multiple factor linear regression analysis, and the independent influencing factors are age, hypertension, diabetes, no blood flow time, baseline eGFR, ACR and lactate; The β value of the age is-0.344, the 95% CI is-0.528-0.160, and P<0.001; the β value of the hypertension is-3.610, the 95% CI is-5.968-1.252, and P<0.01; the β value of the diabetes is-2.992, the 95% CI is-5.295-0.689, and P<0.05; the β value of the no blood flow time is-0.577, the 95% CI is-0.996-0.158, and P<0.01; the β value of the baseline eGFR is 0.349, the 95% CI is 0.269-0.429, and P<0.001; the β value of the ACR is-0.042, the 95% CI is-0.073-0.011, and P<0.05; and the β value of the lactate is-0.650, the 95% CI is-1.214-0.086, and P<0.

05.

2. The method for constructing a post-cardiac arrest glomerular filtration rate prediction model according to claim 1, characterized in that, The preprocessing comprises the following steps: screening sample data meeting the inclusion criteria based on the inclusion criteria, wherein the inclusion criteria in the inclusion criteria are age≥18 years old and CA after resuscitation ROSC time>24h, and the exclusion criteria in the inclusion criteria are baseline eGFR<60 mL / min / 1.73m² or prior history of kidney-related diseases or missing clinical data>15%.

3. The method for constructing a post-cardiac arrest glomerular filtration rate prediction model according to claim 2, characterized in that, The independent influencing factors include age, hypertension, diabetes, no blood flow time, baseline eGFR, ACR and lactic acid.

4. The method for constructing a post-cardiac arrest glomerular filtration rate prediction model according to claim 3, characterized in that, The prediction model is constructed by the method for constructing a prediction model of glomerular filtration rate after cardiopulmonary resuscitation according to any one of claims 1-4.

5. A method of predicting the level of renal function after return of spontaneous circulation following resuscitation from cardiac arrest, characterized in that, The prediction model is constructed by the method for constructing a prediction model of glomerular filtration rate after cardiopulmonary resuscitation according to any one of claims 1-4.

6. A method of predicting the risk of developing chronic kidney disease after return of spontaneous circulation after resuscitation from cardiac arrest, characterized in that, The risk of suffering from chronic kidney disease is the risk of suffering from chronic kidney disease within 24 hours.

7. The method of claim 6, wherein, ​

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