Development and Cross-Database External Validation of 24-Hour Landmark Models for ICU Mortality After Intracerebral Hemorrhage: Incremental Value of Routine Coagulation Markers
- 1Department of Health Science, California State University, Long Beach, Long Beach, CA, USA
Abstract
BackgroundIntracerebral hemorrhage (ICH) is associated with high early mortality. Although routine coagulation markers are clinically relevant, their incremental value for mortality prediction beyond neurologic and physiologic information is uncertain. We developed and externally validated models using first-24-hour ICU data and evaluated the added predictive value of routine coagulation measurements.
MethodsWe used MIMIC-IV for model development and eICU-CRD for external validation. The development cohort included 2,669 adults with nontraumatic ICH and 388 ICU deaths. Logistic regression, random forest, and XGBoost were evaluated using a patient-level 70:30 holdout split and 5-fold stratified cross-validation. External validation used a high-specificity primary eICU cohort (396 patients; 39 deaths) and a broader secondary hemorrhagicstroke/likely-ICH cohort (1,555 patients; 168 deaths). Performance was assessed using AUROC, AUPRC, Brier score, calibration, and paired bootstrap comparisons. GCS-only and age-plus-GCS models served as clinical benchmarks. Coagulation-only, clinical-only, and combined XGBoost models were compared to assess incremental value.
ResultsIn the internal holdout cohort, AUROCs were 0.870 for logistic regression, 0.859 for random forest, and 0.867 for XGBoost. In the primary external cohort, corresponding AUROCs were 0.804, 0.839, and 0.817; AUPRCs were 0.342, 0.419, and 0.406; and Brier scores were 0.080, 0.072, and 0.074. In the broader cohort, AUROCs were 0.826, 0.848, and 0.838. Random forest showed the numerically strongest external performance, but pairwise AUROC differences among multivariable models were not clearly supported. GCS-only achieved AUROCs of 0.780 and 0.813 in the primary and broader cohorts. Coagulationonly XGBoost showed weak discrimination (AUROC 0.598 internally, 0.598 primary external, and 0.566 broader external). Adding coagulation variables to the clinical model produced AUROC differences of 0.014 (95% CI -0.009 to 0.037) and 0.003 (95% CI -0.007 to 0.013) in the two external cohorts.
ConclusionsMultivariable models based on first-24-hour ICU data retained useful discrimination across external cohorts and provided modest gains over simple GCS-based benchmarks. Routine coagulation markers alone were weak predictors and did not provide clearly supported incremental discrimination beyond broader clinical information. Cross-database transportability should be interpreted together with phenotype alignment and calibration.
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BibTeX
@misc{pathak2026development,
title = {{Development and Cross-Database External Validation of 24-Hour Landmark Models for ICU Mortality After Intracerebral Hemorrhage: Incremental Value of Routine Coagulation Markers}},
author = {S. Pathak and Y. Si and J. Sanjaya and M. Haghi and N. T. Kudrot and G. Placencia and K. Alaei and M. Pishgar},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.11.26365237},
url = {https://www.medrxiv.org/content/10.64898/2026.10.11.26365237v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Development and Cross-Database External Validation of 24-Hour Landmark Models for ICU Mortality After Intracerebral Hemorrhage: Incremental Value of Routine Coagulation Markers
AU - S. Pathak
AU - Y. Si
AU - J. Sanjaya
AU - M. Haghi
AU - N. T. Kudrot
AU - G. Placencia
AU - K. Alaei
AU - M. Pishgar
PY - 2026
DA - 2026/10/11
PB - medRxiv
DO - 10.64898/2026.10.11.26365237
UR - https://www.medrxiv.org/content/10.64898/2026.10.11.26365237v1
AB - BackgroundIntracerebral hemorrhage (ICH) is associated with high early mortality. Although routine coagulation markers are clinically relevant, their incremental value for mortality prediction beyond neurologic and physiologic information is uncertain. We developed and externally validated models using first-24-hour ICU data and evaluated the added predictive value of routine coagulation measurements. MethodsWe used MIMIC-IV for model development and eICU-CRD for external validation. The development cohort included 2,669 adults with nontraumatic ICH and 388 ICU deaths. Logistic regression, random forest, and XGBoost were evaluated using a patient-level 70:30 holdout split and 5-fold stratified cross-validation. External validation used a high-specificity primary eICU cohort (396 patients; 39 deaths) and a broader secondary hemorrhagicstroke/likely-ICH cohort (1,555 patients; 168 deaths). Performance was assessed using AUROC, AUPRC, Brier score, calibration, and paired bootstrap comparisons. GCS-only and age-plus-GCS models served as clinical benchmarks. Coagulation-only, clinical-only, and combined XGBoost models were compared to assess incremental value. ResultsIn the internal holdout cohort, AUROCs were 0.870 for logistic regression, 0.859 for random forest, and 0.867 for XGBoost. In the primary external cohort, corresponding AUROCs were 0.804, 0.839, and 0.817; AUPRCs were 0.342, 0.419, and 0.406; and Brier scores were 0.080, 0.072, and 0.074. In the broader cohort, AUROCs were 0.826, 0.848, and 0.838. Random forest showed the numerically strongest external performance, but pairwise AUROC differences among multivariable models were not clearly supported. GCS-only achieved AUROCs of 0.780 and 0.813 in the primary and broader cohorts. Coagulationonly XGBoost showed weak discrimination (AUROC 0.598 internally, 0.598 primary external, and 0.566 broader external). Adding coagulation variables to the clinical model produced AUROC differences of 0.014 (95% CI -0.009 to 0.037) and 0.003 (95% CI -0.007 to 0.013) in the two external cohorts. ConclusionsMultivariable models based on first-24-hour ICU data retained useful discrimination across external cohorts and provided modest gains over simple GCS-based benchmarks. Routine coagulation markers alone were weak predictors and did not provide clearly supported incremental discrimination beyond broader clinical information. Cross-database transportability should be interpreted together with phenotype alignment and calibration.
ER -