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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Drugs Outcomes Research and Policies
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1478342
This article is part of the Research Topic Clinical Pharmacist Service Promotes the Improvement of Medical Quality Volume II View all 20 articles

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

Provisionally accepted
Li Qin Li Qin Jieling Mao Jieling Mao Min Gao Min Gao Jingwen Xie Jingwen Xie Zhikun Liang Zhikun Liang Xiaoyan Li Xiaoyan Li *
  • The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

The final, formatted version of the article will be published soon.

    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; p < 0.01) and mortality prediction (ROC-AUC: 0.708; 95% CI: 0.646-0.768; p < 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.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.

    Keywords: Disseminated Intravascular Coagulation, Machine learning model, Intensive Care Unit, colorectal cancer, anticoagulation

    Received: 09 Aug 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Qin, Mao, Gao, Xie, Liang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Xiaoyan Li, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510610, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.