EARLY PREDICTIVE VALUE OF PERIPHERAL INFLAMMATORY INDEX COMBINED WITH HIGH-SENSITIVITY TROPONIN T FOR SEPSIS-INDUCED CARDIOMYOPATHY

Inflammatory and Troponon T in cardiomyopaty from Sepsis

Background: Sepsis-induced cardiomyopathy (SICM) is a common and critical complication of sepsis, posing significant challenges for early identification. Inflammatory and immune markers may provide complementary information regarding myocardial injury biomarkers.

Methods: Of 319 patients firstly diagnosed as sepsis between June 2022 and January 2025 were enrolled. Complete blood counts and high-sensitivity cardiac troponin T (hs-cTnT) were collected within 0–6 hours after diagnosis. SICM was determined based on clinical and imaging data. The peripheral blood inflammatory indices (PBII) was calculated from blood counts, including NLR, SII, SIRI, PLR, and PIV. Variable robustness was assessed using LASSO logistic regression combined with bootstrap resampling. Univariate and multivariate logistic regression analyses were then performed to construct predictive models, with model performance evaluated via ROC curves, calibration curves, and decision curves.

Results: This study involved 319 patients with sepsis, among whom 115 (36.1%) developed SICM. Compared with non-SICM patients, SICM patients exhibited significantly elevated high-sensitivity troponin T levels, indicating more pronounced myocardial injury. Peripheral inflammatory indices were also elevated overall, with the most significant differences observed in SII and NLR. SIRI and PLR were also notably elevated, while PIV showed limited variation (all P<0.05). Regarding predictive performance, the baseline model (hs-cTnT) achieved an AUC of 0.825; incorporating SII or NLR sequentially further enhanced model discrimination (respective AUCs of 0.856 and 0.860), demonstrating good consistency and stable clinical net benefit in calibration and decision curve analyses.

Conclusion: SII and NLR, as readily available peripheral inflammatory markers, greatly enhance the early predictive performance of SICM when combined with hs-cTnT. This combined strategy improves the accuracy of clinical risk stratification and offers a reference for individualized management of sepsis patients.