ArticleBMC anesthesiology2023
Predictors and nomogram of in-hospital mortality in sepsis-induced myocardial injury: a retrospective cohort study.
Article in BMC anesthesiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Early high-sensitivity troponin elevation and short-term mortality in sepsis: a systematic review with meta-analysis.Critical care (London, England) · 2025Pooled it
- A machine learning model for in-hospital mortality prediction in patients with sepsis-related myocardial injury.PLOS digital health · 2026Article
- Machine learning-based prediction of sepsis-induced myocardial injury: external validation and SHAP interpretation.BMC infectious diseases · 2026Article
- Machine Learning-Based Prediction of 28-Day Mortality in Patients with Sepsis-Induced Cardiomyopathy: An Integrated Analysis of Inflammatory Biomarkers.Journal of inflammation research · 2026Article
- Albumin administration and 28-day mortality in sepsis-induced myocardial injury: a propensity score-matched analysis.Frontiers in pharmacology · 2026Article
- Identification of Prognostic Factors for Sepsis or Septic Shock Patients with Cardiac Dysfunction.Journal of cardiovascular translational research · 2025Article
- Association between statins use and mortality in critically ill patients with sepsis included myocardial injury: a retrospective cohort study.European journal of medical research · 2025Article
- Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury.Scientific reports · 2025Article
- A CD36-based prediction model for sepsis-induced myocardial injury.International journal of cardiology. Heart & vasculature · 2025Article
- Prognostic analysis of sepsis-induced myocardial injury patients using propensity score matching and doubly robust analysis with machine learning-based risk prediction model development.Frontiers in medicine · 2025Article
- The predictive value of SOFA and APSIII scores for 28-day mortality risk in SIMI: a cohort study based on the MIMIC-IV database.Frontiers in cellular and infection microbiology · 2025Article
- Construction and Validation of a Risk Prediction Model for Sepsis-Induced Myocardial Injury.International journal of general medicine · 2025Article
- Myocardial Injury as a Harbinger of Multi-organ Failure in Septic Shock: A Comprehensive Review.Cureus · 2024Review
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis-induced myocardial injury (SIMI) is a common organ dysfunction and is associated with higher mortality in patients with sepsis. We aim to construct a nomogram prediction model to assess the 28-day mortality in patients with SIMI. .
methodWe retrospectively extracted data from Medical Information Mart for Intensive Care (MIMIC-IV) open-source clinical database. SIMI was defined by Troponin T (higher than the 99th percentile of upper reference limit value) and patients with cardiovascular disease were excluded. A prediction model was constructed in the training cohort by backward stepwise Cox proportional hazards regression model. The concordance index (C-index), area under the receiver operating characteristics curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting and decision-curve analysis (DCA) were used to evaluate the nomogram.
results1312 patients with sepsis were included in this study and 1037 (79%) of them presented with SIMI. The multivariate Cox regression analysis in all septic patients revealed that SIMI was independently associated with 28-day mortality of septic patients. The risk factors of diabetes, Apache II score, mechanical ventilation, vasoactive support, Troponin T and creatinine were included in the model and a nomogram was constructed based on the model. The C-index, AUC, NRI, IDI, calibration plotting and DCA showed that the performance of the nomogram was better than the single SOFA score and Troponin T.
conclusionSIMI is related to the 28-day mortality of septic patients. The nomogram is a well-performed tool to predict accurately the 28-day mortality in patients with SIMI.
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