Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Kaier Gu, Saisai Lu
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引用次数: 0

Abstract

Background: The incidence of intensive care unit (ICU) admissions and the corresponding mortality rates among cancer patients are both high. However, the existing scoring systems all lack specificity. This research seeks to establish and validate a prediction model for early forecasting of in-hospital mortality in critically ill cancer patients.

Methods: A retrospective analysis was conducted utilizing data from cancer patients obtained from the eICU and MIMIC-IV databases. The least absolute shrinkage and selection operator method was employed to screen predictive factors and construct six machine learning (ML) models. These models were mainly compared in terms of their predictive performance through area under the curve (AUC) and underwent external validation. Nomograms were developed using multivariate logistic regression (LR) analysis findings. The Shapley Additive exPlanations (SHAP) method was employed to explain the variables within the ML models.

Results: Twelve predictive factors were chosen to develop the ML models. Among these models, the LR model and the eXtreme gradient boosting (XGB) model demonstrated the optimal efficacy. In the external validation cohort, their AUC values reached 0.751 [95% confidence interval (CI): 0.735 - 0.768] and 0.737 (95% CI: 0.720 - 0.754), respectively. Moreover, nomograms and SHAP were employed to explain the variables. Additionally, a user-friendly web-based calculator tool was created.

Conclusions: The LR and XGB models were successfully developed to predict in-hospital mortality in critically ill cancer patients. Their robust predictive ability was demonstrated in the external validation cohorts. This model can assist physicians in clinical decision-making and timely intervention.

Clinical trial number: Not applicable.

危重癌症患者住院死亡率的基于机器学习的预测工具和nomogram:回顾性队列的开发和外部验证
背景:癌症患者的重症监护病房(ICU)入院率和相应的死亡率都很高。然而,现有的评分系统都缺乏特异性。本研究旨在建立并验证癌症危重病人住院死亡率的早期预测模型。方法:回顾性分析从eICU和MIMIC-IV数据库中获得的癌症患者数据。采用最小绝对收缩法和选择算子法筛选预测因子,构建6个机器学习模型。主要通过曲线下面积(area under the curve, AUC)对模型的预测性能进行比较,并进行外部验证。使用多变量逻辑回归(LR)分析结果绘制nomogram。采用Shapley加性解释(SHAP)方法来解释ML模型中的变量。结果:选取12个预测因素建立ML模型。其中LR模型和极限梯度增强(eXtreme gradient boosting, XGB)模型效果最佳。在外部验证队列中,它们的AUC值分别达到0.751[95%可信区间(CI): 0.735 ~ 0.768]和0.737 (95% CI: 0.720 ~ 0.754)。此外,还采用了nomogram和SHAP来解释变量。此外,还创建了一个用户友好的基于web的计算器工具。结论:成功地建立了LR和XGB模型来预测危重癌症患者的住院死亡率。在外部验证队列中证明了它们强大的预测能力。该模型可以辅助医生进行临床决策和及时干预。临床试验号:不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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