Machine Learning for Prediction of Postoperative Delirium in Adult Patients: A Systematic Review and Meta-analysis.

IF 3.2 4区 医学 Q2 PHARMACOLOGY & PHARMACY
Clinical therapeutics Pub Date : 2024-12-01 Epub Date: 2024-10-11 DOI:10.1016/j.clinthera.2024.09.013
Hao Chen, Dongdong Yu, Jing Zhang, Jianli Li
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引用次数: 0

Abstract

Purpose: This meta-analysis aimed to evaluate the performance of machine learning (ML) models in predicting postoperative delirium (POD) and to provide guidance for clinical application.

Methods: PubMed, Embase, Cochrane Library, and Web of Science databases were searched from inception to April 29, 2024. Studies reported ML models for predicting POD in adult patients were included. Data extraction and risk of bias assessment were performed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis - AI (TRIPOD-AI) and Prediction model Risk Of Bias ASsessment Tool (PROBAST) tools. Meta-analysis with the area under the curve (AUC) was performed using MedCalc software.

Findings: A total of 23 studies were included after screening. Age (n = 20, 86.95%) and Random Forest (RF) (n = 24, 17.27%) were the most frequently used feature and ML algorithm, respectively. The meta-analysis showed an overall AUC of 0.792. The ensemble models (AUC = 0.805) showed better predictive performance than single models (AUC = 0.782). Additionally, considerable variations in AUC were found among different ML algorithms, with AdaBoost (AB) demonstrating good performance with AUC of 0.870. Notably, the generalizability of these models was uncertain due to limitations in external validation and bias assessment.

Implications: The performance of ensemble models were higher than single models, and the AB algorithms demonstrated better performance, compared with other algorithms. However, further research was needed to enhance the generalizability and transparency of ML models.

预测成人患者术后谵妄的机器学习:系统回顾与元分析》。
目的:本荟萃分析旨在评估机器学习(ML)模型在预测术后谵妄(POD)方面的性能,并为临床应用提供指导:方法:检索了 PubMed、Embase、Cochrane Library 和 Web of Science 数据库中从开始到 2024 年 4 月 29 日的内容。纳入了报告成人患者 POD 预测 ML 模型的研究。数据提取和偏倚风险评估使用 "个人预后或诊断多变量预测模型透明报告-AI(TRIPOD-AI)"和 "预测模型偏倚风险评估工具(PROBAST)"工具进行。使用 MedCalc 软件对曲线下面积(AUC)进行了元分析:经过筛选,共纳入 23 项研究。年龄(n = 20,86.95%)和随机森林(RF)(n = 24,17.27%)分别是最常用的特征和 ML 算法。荟萃分析显示,总体 AUC 为 0.792。集合模型(AUC = 0.805)比单一模型(AUC = 0.782)显示出更好的预测性能。此外,不同 ML 算法的 AUC 也有很大差异,AdaBoost(AB)的 AUC 为 0.870,表现出色。值得注意的是,由于外部验证和偏差评估的局限性,这些模型的普适性并不确定:启示:集合模型的性能高于单一模型,与其他算法相比,AB 算法的性能更好。然而,要提高 ML 模型的普遍性和透明度,还需要进一步研究。
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来源期刊
Clinical therapeutics
Clinical therapeutics 医学-药学
CiteScore
6.00
自引率
3.10%
发文量
154
审稿时长
9 weeks
期刊介绍: Clinical Therapeutics provides peer-reviewed, rapid publication of recent developments in drug and other therapies as well as in diagnostics, pharmacoeconomics, health policy, treatment outcomes, and innovations in drug and biologics research. In addition Clinical Therapeutics features updates on specific topics collated by expert Topic Editors. Clinical Therapeutics is read by a large international audience of scientists and clinicians in a variety of research, academic, and clinical practice settings. Articles are indexed by all major biomedical abstracting databases.
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