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

ABSTRACT:

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,

SUBMITTER: Qin L 

PROVIDER: S-EPMC11614659 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature