Statistical analysis
malignant cancer, organ failure, CHD, deaths, dyslipidemia
ACUTE KIDNEY FAILURE, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, CONGESTIVE HEART FAILURE, ACUTE RESPIRATORY FAILURE, REGRESSION, DYSLIPIDEMIA
For continuous variables, normality was tested using the Shapiro-Wilk test. And depending on the type and distribution of the variable, the normal distribution was expressed as mean and standard deviation (SD) and differences between groups were assessed using the t-test, and the non-normal distribution was expressed as median and interquartile range (IQR) and differences between groups were assessed using the Kruskal-Wallis test. For categorical variables, expressed as counts and percentages, differences between groups were assessed using the ChiWe analyzed the cumulative rate of all-cause mortality in CHD patients within 90 days of ICU admission using Kaplan-Meier survival analysis to compare the cumulative distribution of deaths among patients in four-score subgroups for each indicator at admission. We also used the restricted cubic spline function (RCS) to explore the nonlinear relationship between these metrics and the study outcomes when used as continuous variables.To further assess the independent associations between the indicators and the primary endpoints, we used Logistic regression model and Cox proportional hazards model, and we used different models to adjust for potential confounders. Model 1: crude analysis without adjusting for any confounders; Model 2: adjustments including age, male, and race; Model 3: additional adjusting for confounders (heart rate, respiratory rate, saturation of peripheral oxygen, sequential organ failure assessment (SOFA), acute exacerbation of chronic obstructive pulmonary disease, congestive heart failure, malignant cancer, dyslipidemia, acute respiratory failure, acute kidney failure, potassium, aniongap, blood urea nitrogen, glucose, serum creatinine, hematocrit, hemoglobin, mean corpuscular volume, red blood cell, dialysis, vasopressor, invasive mechanical ventilation, coronary artery bypass graft, angiotensin-converting enzyme inhibitors or angiotensin receptor blocker, antiplatelet, statin, non-vitamin K antagonist oral anticoagulant, and Vitamin K antagonists) based on Model 2. Once the indicators were grouped based on quartiles, we chose G1 as the reference group and calculated the adjusted odds ratio (OR) or hazard ratio (HR) of the primary endpoints for the other groups in comparison to the reference group.Given the importance of mortality risk management in ICU patients with CHD, then we used these inflammatory indictors in conjunction with the widely utilized SOFA to construct a novel predictive model of ICU mortality in ICU patients with CHD. We first divided these inflammatory indicators into elevated value and non-elevated value groups based on their respective third quartiles. Subsequently, we divided the entire cohort into a training cohort and an internal validation cohort on a 8:2 basis. In the training corhort, we used univariate Logistic regression analyses followed by stepwise forward multivariate Logistic regression analyses to select the variables used to construct the novel predictive model, computed correlation coefficient and variance inflation factor (VIF) to detect covariance of the variables in the model, and used the Hosmer-Lemeshow test to assess the fit of the logistic regression models. Then, the area under the curve (AUC) of receiver operating characteristic (ROC) curves, plotting of calibration curves, decision curve analysis (DCA) (compared with SOFA) were used in both the internal and external validation cohorts, in addition, we computed the integrated discrimination improvement (IDI) to validate the variability of the predictive performance of the new model between the novel model and SOFA. Furthermore, we transformed the novel model obtained from the training cohort to nomogram and interactive network dynamic nomogram.We performed all statistical processing using SPSS (version 29), Stata (version 17), and R (version 4.2.3). In all analyses, a two-tailed 