ArticleInternational journal of general medicine2025
Construction and Validation of a Risk Prediction Model for Sepsis-Induced Myocardial Injury.
Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Sepsis patients face a high risk of myocardial injury, which increases the risk of death. Therefore, the rapid and accurate assessment of myocardial injury risk is crucial for improving prognosis. Objective: To construct and validate a risk prediction model for sepsis-induced myocardial injury (SMCI). Methods: Patients were randomly assigned to a training cohort and an internal validation cohort in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were used to identify independent predictors for the construction of a nomogram. The model's discrimination, calibration, and clinical applicability were evaluated using area under curve (AUC), Hosmer-Lemeshow tests, decision curve analysis (DCA) and clinical impact curve (CIC). Meanwhile, internal validation was conducted. Results: The study included 370 patients, with 262 in the training cohort and 108 in the validation cohort. 3 independent risk factors were identified, including Log myoglobin (Myo), Log B-type natriuretic peptide (BNP), and Log interleukin-6 (IL-6) and a nomogram incorporating these factors was constructed. The AUC in the training and validation cohorts was 0.856 and 0.853, respectively. The Hosmer-Lemeshow test indicated good calibration in both cohorts, while DCA and CIC demonstrated strong clinical applicability. Conclusion: The nomogram based on Log Myo, Log BNP, and Log IL-6 may serve as a practical tool for the early identification of high-risk patients by facilitating the rapid calculation of SMCI risk.
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