Cardiac troponin T and 28-day mortality in traumatic brain injury: a retrospective cohort study and machine learning prediction model.
- 1Department of Neurocritical Care, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Abstract
Background
Traumatic brain injury (TBI) frequently induces cardiac dysfunction via the brain-heart axis, yet the prognostic value of cardiac troponin T (cTnT) in TBI remains incompletely characterized. This study investigated thye association between cTnT levels and 28-day all-cause mortality in TBI patients and developed a machine learning-based prediction model.Methods
This retrospective cohort study included 837 adult TBI patients from the MIMIC-III/IV databases (development cohort) and 121 patients from a single Chinese center (external validation cohort). The first cTnT measurement within 24 h of ICU admission was analyzed. Restricted cubic spline (RCS) regression was used to explore nonlinear relationships. cTnT was categorized into quartiles (Q1 ≤ 0.03, Q2 0.03-0.06, Q3 0.06-0.17, Q4 > 0.17 ng/mL). A two-step Boruta plus LASSO feature selection identified 13 robust predictors. Five machine learning algorithms (Random Forest, BART, GBM, CTREE, SVM) were trained. The primary outcome was 28-day all-cause mortality.Results
Among 837 patients, 217 (25.9%) died within 28 days. RCS revealed a significant nonlinear association (P for nonlinearity < 0.001). In multivariable Cox regression adjusted for the 12 LASSO-selected covariates (excluding cTnT quartile itself), patients in Q3 and Q4 had nearly twofold higher mortality risks compared with Q1 (HR = 2.01, 95% CI: 1.37-2.95, p < 0.001; and HR = 1.96, 95% CI: 1.31-2.93, p = 0.001, respectively). Random Forest achieved the best discriminative performance, with an internal AUC of 0.783 and an external validation AUC of 0.706 (95% CI: 0.584-0.828). The model also demonstrated good calibration (Brier score = 0.155) and favorable clinical utility across clinically relevant threshold probabilities as shown by decision curve analysis.Conclusion
Elevated cTnT is independently associated with 28-day mortality in TBI patients, with a potential risk threshold of ≥0.06 ng/mL. The Random Forest model integrating cTnT with clinical variables provides moderate mortality prediction. cTnT may serve as an integrative marker of systemic stress and multi-organ dysfunction in TBI.
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BibTeX
@article{zhang2026cardiac,
title = {{Cardiac troponin T and 28-day mortality in traumatic brain injury: a retrospective cohort study and machine learning prediction model.}},
author = {Liwen Zhang and Wenwen Hu and Wenjie He and Feiyu Huang and Jing Zhang},
journal = {Frontiers in neurology},
year = {2026},
month = oct,
doi = {10.3389/fneur.2026.1926632},
url = {https://europepmc.org/article/MED/42908636},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cardiac troponin T and 28-day mortality in traumatic brain injury: a retrospective cohort study and machine learning prediction model.
AU - Liwen Zhang
AU - Wenwen Hu
AU - Wenjie He
AU - Feiyu Huang
AU - Jing Zhang
PY - 2026
DA - 2026/10/07
JO - Frontiers in neurology
DO - 10.3389/fneur.2026.1926632
UR - https://europepmc.org/article/MED/42908636
AB - Background Traumatic brain injury (TBI) frequently induces cardiac dysfunction via the brain-heart axis, yet the prognostic value of cardiac troponin T (cTnT) in TBI remains incompletely characterized. This study investigated thye association between cTnT levels and 28-day all-cause mortality in TBI patients and developed a machine learning-based prediction model.Methods This retrospective cohort study included 837 adult TBI patients from the MIMIC-III/IV databases (development cohort) and 121 patients from a single Chinese center (external validation cohort). The first cTnT measurement within 24 h of ICU admission was analyzed. Restricted cubic spline (RCS) regression was used to explore nonlinear relationships. cTnT was categorized into quartiles (Q1 ≤ 0.03, Q2 0.03-0.06, Q3 0.06-0.17, Q4 > 0.17 ng/mL). A two-step Boruta plus LASSO feature selection identified 13 robust predictors. Five machine learning algorithms (Random Forest, BART, GBM, CTREE, SVM) were trained. The primary outcome was 28-day all-cause mortality.Results Among 837 patients, 217 (25.9%) died within 28 days. RCS revealed a significant nonlinear association (P for nonlinearity < 0.001). In multivariable Cox regression adjusted for the 12 LASSO-selected covariates (excluding cTnT quartile itself), patients in Q3 and Q4 had nearly twofold higher mortality risks compared with Q1 (HR = 2.01, 95% CI: 1.37-2.95, p < 0.001; and HR = 1.96, 95% CI: 1.31-2.93, p = 0.001, respectively). Random Forest achieved the best discriminative performance, with an internal AUC of 0.783 and an external validation AUC of 0.706 (95% CI: 0.584-0.828). The model also demonstrated good calibration (Brier score = 0.155) and favorable clinical utility across clinically relevant threshold probabilities as shown by decision curve analysis.Conclusion Elevated cTnT is independently associated with 28-day mortality in TBI patients, with a potential risk threshold of ≥0.06 ng/mL. The Random Forest model integrating cTnT with clinical variables provides moderate mortality prediction. cTnT may serve as an integrative marker of systemic stress and multi-organ dysfunction in TBI.
SN - 1664-2295
ER -