Routine Laboratory Tests Predict 72-h Fatality in Patients With D-Dimer Levels ≥ 2 μg/mL: A Retrospective Cohort Study Comparing Statistical and Machine Learning Models

Background

Despite the high prognostic value of D-dimer in various clinical conditions, limited research has addressed short-term fatality prediction across disease categories. This study aimed to develop and compare models predicting 72-h fatality in patients with D-dimer levels ≥ 2 μg/mL, using laboratory variables. This timeframe was chosen based on its clinical relevance for early triage and intervention across multiple acute conditions.

Methods

We retrospectively analyzed data from 5158 patients (241 deaths within 72 h). The primary outcome was 72-h fatality; predictors included age, sex, and 40 routine hematologic, biochemical, and coagulation tests. Traditional multivariate logistic regression analysis (MLRA) was compared with four machine learning (ML) models: Prediction One, LightGBM, XGBoost, and CatBoost. External validation was performed using a separate dataset of 5550 patients (309 deaths). D-dimer levels were recorded in any clinical setting despite limited patient medical information.

Results

The 72-h fatality rate increased with increasing D-dimer levels (overall 4.67%). Major causes of death were intracranial disease (24.9%), malignancy (17.0%), and sepsis (8.3%). MLRA identified five key predictors: advanced age, low total protein and cholesterol levels, and elevated aspartate aminotransferase and D-dimer levels. Its performance (AUC 0.829, 95% CI 0.768–0.888; sensitivity 0.762; specificity 0.809) was exceeded by LightGBM (AUC 0.987; sensitivity 0.987; specificity 0.911), which outperformed Prediction One (0.814), XGBoost (0.981), and CatBoost (0.937).

Conclusion

ML models, particularly LightGBM, effectively identify high-risk patients using routine laboratory tests. The model enables timely decision-making and early risk stratification in patients with high D-dimer values, even when clinical information is limited.