Machine learning models can predict cancer-associated disseminated intravascular coagulation in critically ill colorectal cancer patients.

BACKGROUND

Due to its complex pathogenesis, the assessment of cancer-associated disseminated intravascular coagulation (DIC) is challenging. We aimed to develop a machine learning (ML) model to predict overt DIC in critically ill colorectal cancer (CRC) patients using clinical features and laboratory indicators.

METHODS

This retrospective study enrolled consecutive CRC patients admitted to the intensive care unit from January 2018 to December 2023. Four ML algorithms were used to construct predictive models using 5-fold cross-validation. The models' performance in predicting overt DIC and 30-day mortality was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and Cox regression analysis. The performance of three established scoring systems, ISTH DIC-2001, ISTH DIC-2018, and JAAM DIC, was also assessed for survival prediction and served as benchmarks for model comparison.

RESULTS

A total of 2,766 patients were enrolled, with 699 (25.3%) diagnosed with overt DIC according to ISTH DIC-2001, 1,023 (36.9%) according to ISTH DIC-2018, and 662 (23.9%) according to JAAM DIC. The extreme gradient boosting (XGB) model outperformed others in DIC prediction (ROC-AUC: 0.848; 95% CI: 0.818-0.878; < 0.01) and mortality prediction (ROC-AUC: 0.708; 95% CI: 0.646-0.768; < 0.01). The three DIC scores predicted 30-day mortality with ROC-AUCs of 0.658 for ISTH DIC-2001, 0.692 for ISTH DIC-2018, and 0.673 for JAAM DIC.

CONCLUSION

The results indicate that ML models, particularly the XGB model, can serve as effective tools for predicting overt DIC in critically ill CRC patients. This offers a promising approach to improving clinical decision-making in this high-risk group.