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Health·G Space·Evidence-backed gain·Published 2026-09-06

Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling

Abstract: Background Plasma D-dimer levels are independently associated with poor prognosis following aneurysmal subarachnoid hemorrhage (aSAH). However, the underlying mechanisms contributing to early D-dimer elevation remain unclear. This study aimed to evaluate the association between admission D-dimer levels and total bleeding volume (TBV) and to further explore their combined predictive power for functional outcomes using interpretable machine learning approaches. Methods We analyzed data from 473 patients with aSAH…

TRV-2026-0993Peer-reviewedPermanent record — cite & verify
Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling

"CT Scan of Dale Mahalko's brain-skull" by Dale Mahalko is licensed under CC BY-SA 3.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/3.0/.

The quick read

Researchers analyzed 473 patients with aneurysmal subarachnoid hemorrhage from the retrospective PROSAH-MPC cohort to test whether admission D-dimer levels and total bleeding volume together predict long-term function. They stratified patients by D-dimer quartiles, ran multivariable logistic regression for 12-month modified Rankin Scale outcomes, selected features with Boruta, and built seven machine learning models interpreted with SHAP.

The work matters because early risk stratification after aSAH remains difficult, and the models showed that combining a blood biomarker with imaging volume improves discrimination over single markers alone, with XGBoost reaching AUC 0.904. Uncertainty remains about generalizability beyond this retrospective cohort and about the biological mechanism linking early D-dimer elevation to bleeding volume and outcome.

Main points
  • Retrospective analysis of 473 patients with aSAH from the PROSAH-MPC cohort stratified by D-dimer quartiles.
  • Unfavorable 12-month mRS outcomes occurred in 125 patients (26.4%) and elevated D-dimer was associated with unfavorable outcomes with adjusted OR 1.08.
  • Boruta feature selection identified D-dimer, TBV, age, Hunt-Hess grade, and modified Fisher score as top predictors, with significant TBV and D-dimer interaction P = 0.032.
  • Seven machine learning models were developed and SHAP analysis confirmed D-dimer and TBV as major contributors to prediction.
Gain

Interpretable machine learning combining admission D-dimer and total bleeding volume improved prediction of 12-month functional outcome after aneurysmal subarachnoid hemorrhage, with XGBoost achieving AUC 0.904 and combined D-dimer+TBV+Hunt-Hess outperforming single markers, to inform early risk stratification.

The rundown

The study analyzed 473 aSAH patients from the retrospective PROSAH-MPC cohort, including clinical, radiological, and laboratory parameters, stratified by D-dimer quartiles and evaluated with multivariable logistic regression for 12-month mRS.

Feature selection via Boruta identified five top predictors, and seven machine learning models were trained; XGBoost had the highest AUC at 0.904, while the combined D-dimer + TBV + Hunt-Hess model reached AUC 0.867, with SHAP used to interpret feature contributions and a significant TBV-D-dimer interaction noted.

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