Abstract
Background
This study aims to develop and validate a novel mortality prediction model to forecast the 90-day mortality risk for patients with ACS (Acute Coronary Syndrome) after discharge.
Methods
We selected 1359 patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database as our study cohort and collected 32 clinical indicators within the first 24 hours of their admission. By randomly assigning these patients to a training group and a validation group (with a ratio of 0.65:0.35), we used Least Absolute Shrinkage and Selection Operator (LASSO) regression and bidirectional stepwise logistic regression to identify 7 key variables. Based on these variables, we constructed a mortality prediction model. To evaluate the model's accuracy and reliability, we plotted the Receiver Operating Characteristic (ROC) curve, calculated the Area Under the Curve (AUC), sensitivity, and specificity, and performed calibration analysis, including plotting calibration curves, calculating Brier scores, and conducting Hosmer-Lemeshow goodness-of-fit tests. Additionally, through Decision Curve Analysis (DCA) and comparison with current clinical scoring systems, we further assessed the clinical utility of our model.
Results
Age, SOFA (Sepsis-related Organ Failure Assessment), APS III (Acute Physiology Score III), AG(Anion Gap), RR(Respiratory rate), INR(International normalized ratio), and BUN(Bun urea nitrogen) were identified as independent predictors of 90-day mortality risk. The model demonstrated good diagnostic performance in both the training and validation groups, with AUC values of 0.842 and 0.855, respectively. The Hosmer-Lemeshow test results indicated a good fit for both datasets, with P-values of 0.1626 and 0.4008. The Brier scores were 0.107 for the training set and 0.103 for the validation set, indicating the model's good predictive performance. Compared to existing scoring systems (SOFA, APSIII), DCA showed that our model could provide a higher net benefit in clinical applications.
Conclusion
We identified seven clinical indicators including age, SOFA, APSIII, AG, RR, INR, and BUN as independent prognostic factors for predicting the 90-day all-cause mortality in patients with ACS after discharge. This model can assist ICU physicians to quickly make preliminary clinical decisions for ACS patients in clinical practice.