Biomarker for predicting whether patients suffering from pyotoxic myocardial injury have short-term death risk or not and application of biomarker

Abstract

The invention relates to the technical field of biological and medical diagnosis, in particular to a biomarker for predicting whether a patient suffering from pyotoxic myocardial injury has a short-term death risk or not and application of the biomarker, and the biomarker is the ratio of erythrocyte distribution width to albumin. According to the method, retrospective queue research design is adopted, two data sets, namely MIMIC-IV and eICU-CRD, are used, suppurative myocardial injury patients meeting conditions are included, covariants such as baseline features, laboratory inspection results and complication information of the patients are collected, the patients are divided into two groups according to RAR medians, the relation between RAR and short-term death of the patients is analyzed by adopting multiple statistical methods, and the accuracy of the short-term death of the patients is improved. A risk prediction model of short-term death of the patient suffering from the pyotoxic myocardial injury is constructed, so that a clinician can identify the patient suffering from the pyotoxic myocardial injury with high death risk in an early stage, a personalized treatment strategy can be formulated more accurately, the survival rate of the patient suffering from the pyotoxic myocardial injury is increased, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the field of biological and medical diagnostic technology, and in particular to a biomarker for predicting whether a patient with septic myocardial injury has a short-term mortality risk and its application. Background Art

[0002] Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. It is a major cause of systemic inflammatory response syndrome and impaired health. It is a major global health problem and poses a serious threat to human health worldwide. It is estimated that over 19 million cases of severe sepsis occur worldwide each year, resulting in at least 5 million deaths. Sepsis can lead to a variety of complex conditions, with multi-organ dysfunction being a highly common complication. Among the many organs susceptible to sepsis, myocardial injury is particularly prominent, not only a common complication but also a major factor in mortality. Sepsis-induced myocardial injury is often closely associated with an excessive inflammatory response. Studies have shown that nearly half of sepsis patients experience myocardial suppression, and the mortality rate in patients with septic myocardial injury is significantly higher, reaching 35-50%, compared to those without myocardial injury. These patients also have a more severe prognosis, highlighting the severity of sepsis-related myocardial injury and the urgent need for effective predictive markers. Therefore, early identification and effective management of myocardial injury in patients with sepsis are crucial for improving patient prognosis and reducing mortality. This also makes it urgent to explore easily accessible and sensitive indicators to predict the prognosis of myocardial injury in clinical practice.

[0003] The search for effective prognostic markers for myocardial injury in sepsis has been a hot topic in numerous clinical studies. Red blood cell distribution width (RDW), a readily accessible biomarker obtained from routine hematology, has become a prognostic indicator for various diseases, particularly infectious, inflammatory, and cardiovascular conditions. RDW also reflects red blood cell volume, and its increase may be associated with impaired erythropoiesis and abnormal red blood cell survival. Meanwhile, albumin (ALB) is an important plasma protein closely linked to nutritional status, fluid imbalance, circulatory dysfunction, and inflammation, but its use as a prognostic marker may be limited by other factors. Overall, RDW and ALB are considered comprehensive biomarkers of multidimensional dysfunctional physiological states related to inflammation, oxidative stress, and nutrition. They reflect pathological issues from different perspectives, and their integration has important value in predicting mortality. Studies have shown that the ratio of red blood cell distribution width to albumin concentration (RAR) has become a potential risk biomarker for adverse outcomes in various diseases, such as diabetes, sepsis, coronary artery disease, and acute pancreatitis. However, there is still a lack of research on the relationship between RAR index and septic myocardial injury. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention provides a biomarker for predicting short-term mortality in patients with septic myocardial injury and its application. A retrospective cohort study design is adopted, using two datasets, MIMIC-IV and eICU-CRD, to enroll eligible patients with septic myocardial injury. Covariates such as the patients' baseline characteristics, laboratory test results, and comorbidity information are collected. The patients are divided into two groups according to the median RAR. Multiple statistical methods are used to analyze the relationship between RAR and patients' short-term mortality. A risk prediction model for short-term mortality in patients with septic myocardial injury is constructed, which helps clinicians identify septic myocardial injury patients with a high risk of death at an early stage, thereby more accurately formulating personalized treatment strategies and improving the survival rate of patients with septic myocardial injury. The model has broad application prospects.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a biomarker for predicting whether a patient with septic myocardial injury has a short-term mortality risk, wherein the biomarker is the ratio of red blood cell distribution width to albumin;

[0006] in,

[0007] The short-term mortality risk refers to 28-day mortality, hospital mortality, and ICU mortality.

[0008] In a second aspect, the present invention provides a use of a biomarker in preparing a kit for predicting whether a patient with septic myocardial injury has a short-term mortality risk, wherein the biomarker is the biomarker described in the first aspect.

[0009] In a third aspect, the present invention provides a kit for predicting whether a patient with septic myocardial injury has a short-term risk of death, characterized in that the kit includes a reagent for determining the biomarker for predicting whether a patient with septic myocardial injury has a short-term risk of death as described in the first aspect.

[0010] Preferably, the reagent is a reagent for detecting the ratio of red blood cell distribution width to albumin.

[0011] Preferably, the test samples of the kit include one or two or more of venous whole blood, serum and plasma.

[0012] Preferably, the detection limit is set to the standard value. When the detected red blood cell distribution width to albumin ratio is higher than or equal to the standard value, it is judged as positive, indicating that the patient has a risk of death in the short term; when the detected red blood cell distribution width to albumin ratio is lower than the standard value, it is judged as negative, indicating that the patient has no risk of death in the short term.

[0013] In a fourth aspect, the present invention provides a system for predicting whether a patient with septic myocardial injury has a short-term risk of death, the system comprising a data analysis module; the data analysis module is used to analyze the detection values ​​of biomarkers, the biomarkers comprising one or two or more of red blood cell distribution width to albumin ratio, age, gender, weight, ethnicity, CAD, heart failure, atrial fibrillation, hypertension, diabetes, chronic kidney disease, chronic lung disease, liver disease, steroids, vasopressors, ventilation, renal replacement therapy, heart rate, body temperature, carbon dioxide partial pressure, oxygen partial pressure, cardiac troponin, lactate, white blood cells, neutrophils, lymphocytes, hematocrit, red blood cells, platelets, albumin, serum creatinine, bilirubin, urea nitrogen, glucose, alanine aminotransferase, and aspartate aminotransferase.

[0014] Preferably, the biomarkers include red blood cell distribution width to albumin ratio, age, sex, weight, ethnicity, CAD, heart failure, atrial fibrillation, hypertension, diabetes, chronic kidney disease, chronic lung disease, liver disease, steroids, vasopressors, ventilation, renal replacement therapy, heart rate, body temperature, partial pressure of carbon dioxide, partial pressure of oxygen, cardiac troponin, lactate, white blood cells, neutrophils, lymphocytes, hematocrit, red blood cells, platelets, albumin, serum creatinine, bilirubin, urea nitrogen, glucose, alanine aminotransferase, and aspartate aminotransferase.

[0015] Preferably, the data analysis module calculates a predicted value for predicting whether a patient with septic myocardial injury has a risk of death by substituting the detection value of the biomarker into a regression equation, thereby evaluating the risk of death in patients with septic myocardial injury.

[0016] In the above technical solution, the biomarker for predicting short-term death in patients with septic myocardial injury of the present invention can be used as a predictive indicator of death in patients with septic myocardial injury, which is helpful for risk stratification and prognosis prediction, and helps clinicians to identify patients with high risk of death at an early stage, thereby formulating personalized treatment strategies more accurately and improving patient survival rates.

[0017] The present invention conducted a retrospective analysis of two large databases, MIMIC-IV and eICU-CRD. The results showed that RAR values ​​were closely related to short-term all-cause mortality in patients with septic myocardial injury, and were stable and consistent in different subgroups. Restricted cubic spline analysis also showed that as RAR levels increased, the risk of short-term mortality in patients with septic myocardial injury also increased, showing a nonlinear relationship. This shows that RAR is a novel and highly promising biomarker that can be used in clinical practice to identify individuals with a high risk of death in patients with septic myocardial injury, and has broad application prospects.

[0018] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the present invention but do not constitute a limitation of the present invention. In the accompanying drawings:

[0020] Figure 1 Flowchart for selecting the analysis study samples from MIMIC-IV and eICU-CRD;

[0021] Figure 2 The restricted cubic spline is used to associate the RAR value with the short-term mortality model, where A is the MIMIC-V cohort with 28-day death, and D is the eICU-CRD cohort with 28-day death; B is the MIMIC-IV cohort with in-hospital death, and E is the eICU-CRD cohort with in-hospital death; C is the MIMIC-IV cohort with ICU death, and F is the eICU-CRD cohort with ICU death;

[0022] Figure 3 Figure 2 is the ROC curve analysis of RAR and short-term mortality, where A is the MIMIC-V cohort with 28-day death, and D is the eICU-CRD cohort with 28-day death; B is the MIMIC-IV cohort with in-hospital death, and E is the eICU-CRD cohort with in-hospital death; C is the MIMIC-IV cohort with ICU death, and F is the eICU-CRD cohort with ICU death;

[0023] Figure 4 Figures A and C are for the MIMIC-IV cohort, and Figures B and D are for the elCU-CRD cohort.

[0024] Figure 5 The Kaplan-Meier survival curves of RAR values ​​and 28-day mortality, hospital mortality, and ICU mortality are shown, where A is the 28-day survival rate of the MIMIC-V cohort, and D is the 28-day survival rate of the eICU-CRD cohort; B is the hospital survival rate of the MIMIC-IV cohort, and E is the hospital survival rate of the eICU-CRD cohort; C is the ICU survival rate of the MIMIC-IV cohort, and F is the ICU survival rate of the eICU-CRD cohort;

[0025] Figure 6 28-day mortality in the MIMIC-IV cohort subgroups adjusted for baseline data;

[0026] Figure 7In-hospital mortality in the MIMIC-IV cohort subgroups adjusted for baseline data;

[0027] Figure 8 ICU mortality in the MIMIC-IV cohort subgroups adjusted for baseline data;

[0028] Figure 9 28-day mortality in the eICU-CRD cohort subgroup adjusted for baseline data;

[0029] Figure 10 In-hospital mortality in the eICU-CRD cohort subgroup adjusted for baseline data;

[0030] Figure 11 ICU mortality in the eICU-CRD cohort subgroups adjusted for baseline data. DETAILED DESCRIPTION

[0031] The following is a detailed description of the specific embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0032] The endpoints of the ranges and any values ​​disclosed herein are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoints of each range, the endpoints of each range and individual point values, and the individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered to be specifically disclosed herein.

[0033] In a first aspect, the present invention provides a biomarker for predicting whether a patient with septic myocardial injury has a short-term mortality risk, wherein the biomarker is the ratio of red blood cell distribution width to albumin;

[0034] The short-term mortality risk refers to death within 28 days of illness, death in hospital, and death in the ICU.

[0035] In a second aspect, the present invention provides a use of a biomarker in preparing a kit for predicting whether a patient with septic myocardial injury has a short-term mortality risk, wherein the biomarker is the biomarker described in the first aspect.

[0036] In a third aspect, the present invention provides a kit for predicting whether a patient with septic myocardial injury has a short-term risk of death, characterized in that the kit includes a reagent for determining the biomarker for predicting whether a patient with septic myocardial injury has a short-term risk of death as described in the first aspect.

[0037] In a preferred embodiment of the present invention, the reagent is a reagent for detecting the ratio of red blood cell distribution width to albumin.

[0038] In a preferred embodiment of the present invention, the test sample of the kit includes one or two or more of venous whole blood, serum and plasma.

[0039] In a preferred embodiment of the present invention, the detection limit is set to the standard value. When the detected red blood cell distribution width to albumin ratio is higher than or equal to the standard value, it is judged as positive, indicating that the patient has a short-term risk of death; when the detected red blood cell distribution width to albumin ratio is lower than the standard value, it is judged as negative, indicating that the patient has no short-term risk of death.

[0040] In a fourth aspect, the present invention provides a system for predicting whether a patient with septic myocardial injury has a short-term risk of death, the system comprising a data analysis module; the data analysis module is used to analyze the detection values ​​of biomarkers, the biomarkers comprising one or two or more of red blood cell distribution width to albumin ratio, age, gender, weight, ethnicity, CAD, heart failure, atrial fibrillation, hypertension, diabetes, chronic kidney disease, chronic lung disease, liver disease, steroids, vasopressors, ventilation, renal replacement therapy, heart rate, body temperature, carbon dioxide partial pressure, oxygen partial pressure, cardiac troponin, lactate, white blood cells, neutrophils, lymphocytes, hematocrit, red blood cells, platelets, albumin, serum creatinine, bilirubin, urea nitrogen, glucose, alanine aminotransferase, and aspartate aminotransferase.

[0041] In a preferred embodiment of the present invention, the biomarkers include red blood cell distribution width to albumin ratio, age, sex, weight, ethnicity, CAD, heart failure, atrial fibrillation, hypertension, diabetes, chronic kidney disease, chronic lung disease, liver disease, steroids, vasopressors, ventilation, renal replacement therapy, heart rate, body temperature, carbon dioxide partial pressure, oxygen partial pressure, cardiac troponin, lactate, white blood cells, neutrophils, lymphocytes, hematocrit, red blood cells, platelets, albumin, serum creatinine, bilirubin, urea nitrogen, glucose, alanine aminotransferase and aspartate aminotransferase.

[0042] In a preferred embodiment of the present invention, the data analysis module calculates the predicted value for predicting whether a patient with septic myocardial injury has a risk of death by substituting the detection value of the biomarker into a regression equation, thereby evaluating the risk of death in patients with septic myocardial injury.

[0043] The present invention will be described in detail below by way of examples. In the following examples, the drugs and pharmaceuticals are all conventional commercial products.

[0044] 1. Data Source

[0045] This study employed a retrospective cohort study design, and we used the following two publicly accessible datasets: (1) the Medical Information Mart for Intensive Care IV / MIMIC-IV v3.1 dataset (2008–2019) and (2) the eICU-CRD dataset (2014–2015).

[0046] MIMIC-IV 3.1 contains non-personalized data on 73,181 ICU admissions for 50,906 unique patients at Beth Israel Deaconess Medical Center between 2008 and 2019 (single-center dataset).

[0047] eICU-CRD is also a de-identified database containing 200,859 ICU admissions for 139,367 unique patients admitted to 335 ICUs across 208 hospitals in the United States (a multicenter dataset). Importantly, the eICU-CRD dataset remains completely independent of the MIMIC-IV dataset, as there is no shared hospital participation between the MIMIC and eICU datasets. To access the database, the present invention team completed the Collaborative Institutional Training Initiative (CITI) course and passed the "Conflict of Interest" and "Data or Sample Invention" exams (ID: 66254112). Permission was obtained to extract data from both datasets. Given that all identifying information has been removed, our invention is deemed exempt from institutional research committee ethical review. Patients or the public were not involved in the design, implementation, reporting, or dissemination plans of our invention.

[0048] 2. Study Population

[0049] The present invention includes 769 patients diagnosed with septic myocardial injury in the MIMIC-IV database from 2008 to 2019 and 721 patients diagnosed with septic myocardial injury in the eICU-CRD database from 2014 to 2015. The specific process is as follows Figure 1 .

[0050] 1. Inclusion criteria:

[0051] (1) Patients diagnosed with sepsis according to the Sepsis 3.0 diagnostic criteria, with infection and Sequential Organ Failure Assessment (SOFA) scores ≥ 2, and cTnT levels higher than the upper limit of normal reference value (cTnT>0.01 ng / ml);

[0052] (2) Patient age ≥18 years;

[0053] (3) ICU stay >24 h;

[0054] (4) Completed echocardiography examination; no myocardial damage caused by other heart diseases.

[0055] 2. Exclusion criteria:

[0056] (1) Patient age <18 years;

[0057] (2) ICU stay <24 h;

[0058] (3) Important laboratory test results such as RDW, ALB, and cTNT are missing, or other related covariates are missing by more than 20%.

[0059] 3. Covariate Collection

[0060] In the present invention, we use query language (SQL) and Navicat Premium to extract data from the MIMIC-IV (3.1) database and the eICU-CRD database.

[0061] We first extracted baseline characteristics, including age, sex, and weight;

[0062] Then, data from the 24 hours before ICU admission were extracted, including age, sex, and body mass index, and other relevant data such as laboratory test results, clinical outcomes, and comorbidity information were obtained;

[0063] We extracted laboratory test data including neutrophil percentage, lymphocyte percentage, hematocrit, serum creatinine (Scr), red blood cell count (RBC), platelet count (PLT), partial pressure of carbon dioxide (PaCO2), partial pressure of arterial oxygen (PaO2), prothrombin time, international normalized ratio (INR), total bilirubin, blood urea nitrogen, creatine kinase MB isoenzyme (CK-MB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum glucose, and cardiac troponin T (cTnT), as well as the use of steroids (glucocorticoids);

[0064] Related comorbidities: hypertension, diabetes, chronic kidney disease, chronic lung disease, liver disease;

[0065] Sepsis severity scores, including the systemic inflammatory response syndrome (SIRS) score, the quick sequential organ failure assessment (qSOFA) score, the acute physiology score III (APS III), and the simplified acute physiology score II (SAPS II), as well as clinical treatment-related information, including the use of vasopressors, ventilator use, and renal replacement therapy (RRT).

[0066] Statistical Analysis

[0067] All statistical analyses were performed using R software and Python;

[0068] A two-sided P value <0.05 was considered statistically significant;

[0069] Normally distributed variables were expressed as mean ± standard deviation (X ± S);

[0070] Comparisons between groups were performed using the Student's t test;

[0071] Skewed distribution variables were expressed as median (quartile) M (P25, P75);

[0072] Comparisons between groups were performed using the Mann-Whitney U test;

[0073] Categorical variables were expressed as number of cases (%), and comparisons between groups were performed using the chi-square test;

[0074] We used the “Mice” package for multiple imputation for data with less than 20% missing values ​​and excluded data with more than 20% missing values.

[0075] We divided each database into two groups: Low and High according to the median RAR;

[0076] Person correlation analysis was used to study the association between RAR values ​​and SOFA scores and APS III scores;

[0077] The ability of RAR values ​​to predict short-term mortality was compared by using the area under the receiver operating characteristic (ROC) curve (AUC). The cutoff values, accuracy, sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, positive predictive accuracy, negative predictive accuracy, overall predictive accuracy, and KAPPA value of RAR for predicting the occurrence of outcome events were compared in each database to evaluate the predictive performance.

[0078] To evaluate the relationship between RAR values ​​and all-cause mortality in the ICU, hospital, and 28 days, we performed regression analysis using RAR values ​​as continuous variables and categorical variables (Low group and High group);

[0079] In the regression model, we constructed 3 groups of models:

[0080] Original model: only RAR was incorporated;

[0081] Model 1 adjusted for age, sex, weight, and ethnicity;

[0082] Model 2 was based on model 1 and further adjusted for CAD, heart failure, atrial fibrillation, hypertension, diabetes mellitus, chronic kidney disease, chronic lung disease, liver disease, steroids, vasopressors, ventilation, renal replacement therapy, heart rate, body temperature, partial pressure of carbon dioxide, partial pressure of oxygen, cardiac troponin, lactate, white blood cells, neutrophils, lymphocytes, hematocrit, red blood cells, platelets, albumin, serum creatinine, bilirubin, urea nitrogen, glucose, alanine aminotransferase, aspartate aminotransferase, and multivariable-adjusted hazard ratios.

[0083] We also set four knots (25%, 50%, 75%, and 95%) in the restricted cubic spline to investigate the association between RAR values ​​and short-term mortality.

[0084] Kaplan-Meier survival curves and Cox regression models were used to calculate hazard ratios (HRs) and 95% CIs.

[0085] Subgroup analyses were performed to evaluate the association between RAR values ​​and 28-day mortality, hospital mortality, and ICU mortality in different subgroups, including age (<65 years, ≥65 years), sex (male, female), race (black, white, and other), and the presence or absence of medical conditions (CAD, heart failure, atrial fibrillation, hypertension, diabetes, CKD, chronic lung disease, and liver disease).

[0086] 5. Experimental Results

[0087] 1. Basic characteristics of the research object

[0088] In the MIMIC-IV cohort, hospital mortality was 13.3%, ICU mortality was 12.87%, and 28-day mortality was 11.83%.

[0089] In the eICU-CRD cohort, hospital mortality was 25.52%, ICU mortality was 16.5%, and 28-day mortality was 24.97%.

[0090] The two databases included similar amounts of data and had similar numbers of deaths.

[0091] Table 1

[0092]

[0093]

[0094]

[0095]

[0096] As shown in Table 1, patients with high RAR values ​​were more likely to develop CAD, hypertension, liver disease, kidney damage, higher SOFA scores and APS III scores, higher arterial oxygen tension, white blood cell count, neutrophil count, lymphocyte count, red blood cell count, hematocrit and platelet count, serum albumin, bilirubin, blood glucose, ALT and AST levels, which was statistically significant (P<0.05).

[0097] Table 2

[0098]

[0099]

[0100]

[0101]

[0102] As shown in Table 2, patients with higher RAR values ​​were more likely to have chronic lung disease, liver disease, weight gain, elevated steroids, need for vasopressors, elevated SOFA and APS III scores, elevated body temperature, increased arterial oxygen tension, and elevated lymphocytes, hematocrit, and red blood cell count. Compared with those with lower RAR values, they were more likely to experience in-hospital mortality, ICU death, and death within 28 days. The P value was < 0.05, indicating statistical significance. The relationship between these three outcomes was generally consistent with that in the MIMIC-IV database.

[0103] 2. The predictive value of RAR value for death in patients with septic myocardial injury

[0104] The present invention evaluated the predictive performance of RAR value for ICU, in-hospital and 28-day all-cause mortality by ROC curve analysis (Supplementary Figure 1).

[0105] Table 3

[0106]

[0107]

[0108] The data in Table 3 show that in the MIMIC-IV cohort, the AUCs of RAR for 28-day mortality, in-hospital mortality, and ICU mortality were all close to 0.63, indicating a certain predictive value. The corresponding cutoff values ​​were 4.45, 4.485, and 4.485, respectively. The sensitivity and specificity for 28-day mortality were both 0.615, the sensitivity for in-hospital mortality was 0.584 and the specificity was 0.62, and the sensitivity for ICU mortality was 0.586 and the specificity was 0.62. The PPVs were 0.177, 0.191, and 0.188, respectively. The NPVs were relatively high, at 0.923, 0.909, and 0.911, respectively. The Kappa values ​​were 0.111, 0.112, and 0.111, respectively. In the eICU-CRD cohort, the AUCs of the three types of death also approached 0.63, with cutoff values ​​of 5.275. The sensitivity of 28-day death was close to 0.66 and the specificity was close to 0.59. The sensitivity of in-hospital death may be close to 0.58 and the specificity is close to 0.62. The sensitivity of ICU death may be close to 0.59 and the specificity is close to 0.62. The PPV of 28-day death in this cohort was 0.348, the NPV was 0.839, and the Kappa value was 0.191. The PPVs of in-hospital death and ICU death were 0.354 and 0.249, respectively, the NPVs were 0.834 and 0.91, respectively, and the Kappa values ​​were 0.192 and 0.164, respectively.

[0109] At the same time, we studied the correlation between RAR values ​​and SOFA scores and APS III scores using the Pearson method, as shown in Figure 4 As shown in Figure 3, we observed positive correlations between RAR values ​​and SOFA scores and APS III scores in both cohorts (p < 0.001).

[0110] 3. Correlation analysis between RAR value and short-term mortality risk in patients with septic myocardial injury

[0111] The Kaplam-Meier survival curve showed that in the MIMIC-IV cohort, there were no significant differences in 28-day death (HR: 0.459, 95% CI: 0.295-0.713, p < 0.001), hospital death (HR: 0.632, 95% CI: (0.418-0.954), p = 0.027), and ICU death (HR: 0.754, 95% CI: 0.496-1:145, p = 0.184). In the eICU-CRD cohort, there were no statistically significant differences in ICU mortality (HR: 0.522, 95% CI: 0.381-0.714, p < 0.001), hospital death (HR: 0.534, 95% CI: 0.392-0.727, p < 0.001), and ICU death (HR: 0.406, 95% CI: 0.269-0.611, p < 0.001). Figure 5 shown.

[0112] At the same time, the present invention also evaluated the relationship between RAR value and 28-day mortality, hospital mortality and ICU mortality through a multivariate Cox regression model.

[0113] Table 4

[0114]

[0115]

[0116] As shown in Table 4, the RAR value predicts the occurrence of 28-day death, hospital death, and ICU death in the MIMIC-IV and eICU-CRD cohorts. In the MIMIC-IV cohort, the HR (95% CI) for 28-day mortality in the high RAR group was 2.32 (1.23-4.37), the HR (95% CI) for ICU mortality was 2.13 (1.04-4.38), and the HR (95% CI) for in-hospital mortality was 1.79 (0.97-3.29). In the eICU-CRD cohort, the HR (95% CI) for 28-day mortality was 1.98 (1.39-2.82), ICU mortality was 2.14 (1.35-3.38), and in-hospital mortality was 1.96 (1.38-2.78). In the MIMIC-IV cohort, the association between grouped RAR and in-hospital mortality was not statistically significant. In both databases, continuous RAR was positively associated with 28-day mortality, in-hospital mortality, and ICU mortality, and the differences were statistically significant.

[0117] 4. Linear relationship between RAR value and short-term all-cause mortality

[0118] To further investigate the relationship between RAR values ​​and short-term all-cause mortality, we used adjusted restricted cubic splines, and the results are shown in Figure 2 .

[0119] like Figure 2 The restricted cubic spline model is shown in the figure, with the horizontal axis being RAR and the vertical axis being the risk ratio. Figure 2 A- Figure 2 The red solid curves in F show the changes in the hazard ratio of 28-day death, hospitalization death, and ICU death corresponding to the changes in RAR. It can be seen that after adjusting for age, sex, weight, and ethnicity, there is a nonlinear relationship between the RAR value and short-term all-cause mortality (including 28-day death, hospitalization death, and ICU death) in both the MIMIC-IV cohort and the eICU-CRD cohort (P<0.05).

[0120] 5. Subgroup Analysis

[0121] To further explore the correlation between the RAR index and hospital mortality, ICU mortality, and 28-day mortality in different subgroups, this paper conducted a stratified analysis based on factors such as age, gender, race, CAD, heart failure, atrial fibrillation, hypertension, diabetes, chronic kidney disease, chronic lung disease, and liver disease. In the MIMIC-IV cohort and the eICU-CRD cohort, except for the missing data on liver disease in the eICU-CRD cohort, the relationship between the RAR value and 28-day all-cause mortality, hospital mortality, and ICU mortality showed a stable and consistent trend in each subgroup. There was no significant interaction between the factors studied. The specific results are shown in Figure 6-11 .

[0122] like Figures 6-11 As can be seen from the content, the present invention found that there was an independent association between RAR value and short-term all-cause mortality after constructing a cox regression model and adjusting for multiple confounding factors. For example, the hazard ratios (HR) of 28-day mortality, ICU mortality, and in-hospital mortality in the MIMIC-IV cohort were 1.27, 1.48, and 1.35, respectively; the corresponding HRs in the eICU-CRD cohort were 1.10, 1.09, and 1.10, respectively. The risk of death in each cohort increased further with high RAR levels (although there was no statistically significant difference in in-hospital mortality with high RAR levels in the eICU-CRD cohort), the overall trend showed that RAR value had an important impact on mortality. It is worth noting that there is no linear relationship between RAR value and death, only an overall relationship. There are differences in the incidence of each outcome indicator in the two databases, but from the overall trend, the three outcomes of in-hospital death, ICU death, and 28-day death in both databases show a certain positive correlation trend with changes in factors related to the severity of the patient's condition.

[0123] The present invention investigates the prognostic value of the red blood cell distribution width to albumin ratio (RAR) in patients with septic myocardial injury. Through retrospective analysis of two large databases, MIMIC-IV and eICU-CRD, the inventors found that the RAR value is closely related to the patient's short-term all-cause mortality and is stable and consistent in different subgroups. Figure 2 Restricted cubic spline analysis also showed that the risk of short-term mortality increased with increasing RAR levels, and both relationships were nonlinear. Therefore, RAR may be a novel and highly promising biomarker that can be used in clinical practice to identify individuals at high risk of mortality in patients with septic myocardial injury.

[0124] The present invention shows that the RAR value has a certain predictive value in predicting the short-term death of patients with septic myocardial injury. Figure 3 As shown in the content, in the MIMIC-IV cohort, the area under the curve (AUC) of RAR for 28-day death, hospital death, and ICU death was close to 0.63, and the AUCs of the three types of death in the eICU-CRD cohort were also close to 0.63, indicating that the RAR value has a certain predictive value for short-term death.

[0125] The biological mechanisms linking RAR and mortality risk remain unclear. However, they may be related to chronic inflammation and nutritional status. Research suggests that these two factors play a key role in mortality and the development of various chronic diseases. Specifically, inflammation plays a key role in changes in RAR values, triggering a series of physiological changes. During inflammation, the body releases multiple inflammatory factors, such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). These inflammatory factors can affect red blood cell production and metabolism through various pathways, leading to elevated RDW. TNF-α inhibits renal production of erythropoietin (EPO), a key hormone that promotes red blood cell production. Reduced EPO secretion can lead to insufficient red blood cell production. These inflammatory factors also inhibit albumin synthesis in the liver and promote albumin breakdown, resulting in lower serum albumin levels. It is precisely this dual effect of inflammation on red blood cells and albumin that leads to elevated RAR values. Changes in RAR values ​​are more than just simple changes in two indicators; they reflect the overall state of inflammation and nutritional imbalance within the body. An elevated RAR value often portends a worse prognosis for patients, as it reflects the severity of the imbalance between erythropoiesis and metabolism, as well as liver protein synthesis and metabolism, caused by the persistent effects of chronic inflammation. This imbalance can further impact cardiac function recovery, immune regulation, and other aspects, ultimately increasing the risk of death.

[0126] This study has significant advantages. It uses a retrospective cohort study design, draws on two large independent databases, and has a large sample size, which enhances the reliability and representativeness of the results. Confounding factors were fully considered during the analysis, and adjustments were made through model construction, making the results more scientific.

[0127] In summary, this study demonstrates that RAR has predictive value for short-term mortality in patients with septic myocardial injury, potentially influencing prognosis by reflecting inflammation and nutritional status. The risk of mortality increases proportionally with increasing RAR values. Our study suggests that RAR can be used as a predictor of mortality in patients with SMI, aiding risk stratification and prognosis prediction.

[0128] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the scope of protection of the present invention.

[0129] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0130] In addition, the various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A biomarker for predicting whether a patient with septic myocardial injury has a short-term mortality risk, characterized in that: The biomarker is the ratio of red blood cell distribution width to albumin; wherein, The short-term mortality risk refers to 28-day mortality, hospital mortality, and ICU mortality.

2. Use of a biomarker in the preparation of a kit for predicting whether a patient with septic myocardial injury has a short-term mortality risk, characterized in that: The biomarker is the biomarker according to claim 1.

3. A kit for predicting whether a patient with septic myocardial injury has a short-term mortality risk, characterized in that: The kit comprises reagents for determining the biomarkers for predicting whether a patient with septic myocardial injury has a short-term mortality risk as claimed in claim 1.

4. The kit according to claim 3, wherein The reagent is a reagent for detecting the ratio of red blood cell distribution width to albumin.

5. The kit according to claim 3, characterized in that The test samples of the kit include one or two or more of venous whole blood, serum and plasma.

6. The kit according to claim 3, characterized in that The detection limit is set to the standard value. When the detected red blood cell distribution width to albumin ratio is higher than or equal to the standard value, it is judged as positive, indicating that the patient has a short-term risk of death; when the detected red blood cell distribution width to albumin ratio is lower than the standard value, it is judged as negative, indicating that the patient has no short-term risk of death.

7. A system for predicting whether a patient with septic myocardial injury has a short-term mortality risk, characterized in that: The system includes a data analysis module; the data analysis module is used to analyze the detection values ​​of biomarkers, and the biomarkers include one or two or more of red blood cell distribution width to albumin ratio, age, gender, weight, ethnicity, CAD, heart failure, atrial fibrillation, hypertension, diabetes, chronic kidney disease, chronic lung disease, liver disease, steroids, vasopressors, ventilation, renal replacement therapy, heart rate, body temperature, carbon dioxide partial pressure, oxygen partial pressure, cardiac troponin, lactate, white blood cells, neutrophils, lymphocytes, hematocrit, red blood cells, platelets, albumin, serum creatinine, bilirubin, urea nitrogen, glucose, alanine aminotransferase and aspartate aminotransferase.

8. The system according to claim 7, characterized in that The biomarkers include red blood cell distribution width to albumin ratio, age, sex, weight, ethnicity, CAD, heart failure, atrial fibrillation, hypertension, diabetes, chronic kidney disease, chronic lung disease, liver disease, steroids, vasopressors, ventilation, renal replacement therapy, heart rate, body temperature, partial pressure of carbon dioxide, partial pressure of oxygen, cardiac troponin, lactate, white blood cells, neutrophils, lymphocytes, hematocrit, red blood cells, platelets, albumin, serum creatinine, bilirubin, urea nitrogen, glucose, alanine aminotransferase, and aspartate aminotransferase.

9. The system according to claim 7, wherein: The data analysis module calculates the predicted value for predicting whether the patient with septic myocardial injury has a risk of death by substituting the detection value of the biomarker into the regression equation, thereby evaluating the risk of death of the patient with septic myocardial injury.

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