Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Aug 31, 2025
Date Accepted: Jan 8, 2026
Complete blood count–derived inflammation indices to predict 3-year all-cause mortality in critically ill patients with diabetes and acute myocardial infarction: a retrospective cohort study with single-center external validation
ABSTRACT
Background:
Background Inflammation plays a pivotal role in the progression of diabetes and its cardiovascular complications, particularly acute myocardial infarction (AMI). Patients with AMI often face high mortality and morbidity, making accurate prognosis crucial for clinical decision-making and outcome improvment.
Objective:
Objective The objective of our study was to develop and validate a stacked predictive model using inflammation-related indices to predict 3-year all-cause mortality in severe diabetic patients with acute myocardial infarction (AMI), aiming to improve patient prognosis.
Methods:
Methods:
This study included 833 severe diabetic AMI patients from the MIMIC-IV database (training/test cohort) and 166 cases from Zhongnan Hospital, China (external validation cohort with 3-year follow-up). Five inflammation-related indices (LMR, NLR, NPR, PLR, PIV) were analyzed for their association with mortality using Cox proportional hazards models. Kaplan-Meier curves and restricted cubic spline (RCS) analysis explored survival probabilities and dose-response relationships. A stacked predictive model incorporating these indices was constructed, and its performance was evaluated using the Area Under the Curve (AUC).
Results:
Results LMR was a protective factor (HR = 0.44, 95% CI 0.25-0.77, P < 0.001), while NLR (HR = 1.78, 95% CI 1.19-2.65, P = 0.004) and PIV (HR = 1.59, 95% CI 1.09-2.30, P = 0.014) were associated with increased mortality risk. Kaplan-Meier analysis showed mortality increased with decreasing LMR quartiles and increasing NLR, NPR, PLR, and PIV quartiles. RCS confirmed decreasing LMR and increasing NLR, NPR, PLR, and PIV were associated with higher adverse event risk. The predictive model achieved an AUC of 0.803 in internal testing and 0.781 in the external validation cohort. The online prediction tool is available at: https://vigbly.shinyapps.io/shiny/.
Conclusions:
Conclusion The stacked predictive model shows potential as a valuable tool for predicting 3-year outcomes in diabetic AMI patients, with good clinical applicability, though further validation in larger multi-center studies is necessary.
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