He Yang, Liu Ning, Hao Sicheng, Xu Mimei, Zeng Yingchun
Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Asia Pac J Oncol Nurs. 2025 Jul 19;12:100760. doi: 10.1016/j.apjon.2025.100760. eCollection 2025 Dec.
Delirium in cancer patients presents a significant clinical challenge, often leading to increased mortality, prolonged hospital stays, and higher healthcare costs. This study aimed to develop an interpretable and generalizable machine learning (ML) model for early prediction of mortality risk in cancer patients with delirium.
A retrospective cohort study design was employed, utilizing data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Five ML models were subsequently constructed and evaluated.
A total of 1893 cancer patients with delirium were included in the analysis, of whom 685 (36.2%) died within 28 days who were admitted to the intensive care unit at Beth Israel Deaconess Medical Center between 2008 and 2022. The Category Boosting (CatBoost) algorithm outperformed other ML models, achieving the highest area under the curve (AUC) on both training and validation datasets. Its robustness was supported by a bias-corrected performance curve closely aligned with the ideal line and the greatest net benefit in decision curve analysis across all threshold probabilities (0-1). The top five predictors of 28-day mortality were high Glasgow Coma Scale and Acute Physiology and Chronic Health Evaluation II scores, use of antibiotics, propofol, and vasopressors.
This study developed an optimal and explainable ML model for predicting 28-day mortality in cancer patients with delirium. The CatBoost algorithm demonstrated stable and robust performance, and interpretability analysis highlighted key predictors. These findings may aid early clinical decision-making and targeted interventions for this high-risk population.
癌症患者的谵妄是一项重大的临床挑战，常常导致死亡率增加、住院时间延长和医疗成本升高。本研究旨在开发一种可解释且具有普遍性的机器学习（ML）模型，用于早期预测谵妄癌症患者的死亡风险。
采用回顾性队列研究设计，利用重症监护医学信息集市IV（MIMIC-IV）数据库中的数据。随后构建并评估了五个ML模型。
共有1893例谵妄癌症患者纳入分析，其中685例（36.2%）在28天内死亡，这些患者于2008年至2022年期间入住贝斯以色列女执事医疗中心重症监护病房。类别提升（CatBoost）算法优于其他ML模型，在训练集和验证集上均获得了最高的曲线下面积（AUC）。其稳健性得到了偏差校正性能曲线的支持，该曲线与理想线紧密对齐，并且在所有阈值概率（0-1）的决策曲线分析中具有最大的净效益。28天死亡率的前五个预测因素是高格拉斯哥昏迷量表和急性生理与慢性健康状况评估II评分、使用抗生素、丙泊酚和血管加压药。
本研究开发了一种用于预测谵妄癌症患者28天死亡率的最优且可解释的ML模型。CatBoost算法表现出稳定且稳健的性能，可解释性分析突出了关键预测因素。这些发现可能有助于针对这一高危人群进行早期临床决策和靶向干预。
