Risk prediction models for postoperative delirium in elderly patients with fragility hip fracture: A systematic review and critical appraisal

医学 谵妄 接收机工作特性 检查表 过度拟合 髋部骨折 风险评估 预测建模 批判性评价 科克伦图书馆 数据提取 梅德林 痴呆 荟萃分析 重症监护医学 内科学 骨质疏松症 机器学习 计算机科学 心理学 人工神经网络 政治学 疾病 计算机安全 替代医学 法学 认知心理学 病理
作者
Bingqian Zhou,Wang Ai,Hong Cao
出处
期刊:International Journal of Orthopaedic and Trauma Nursing [Elsevier BV]
卷期号:52: 101077-101077 被引量:5
标识
DOI:10.1016/j.ijotn.2023.101077
摘要

Elderly patients with fragility hip fracture continue to experience a high rate of postoperative delirium (POD), which has a significant negative impact on prognosis and imposes a huge economic burden. A number of risk prediction models have been developed to detect POD early. However, the risk of bias and clinical applicability of the models remain unclear. The aim of this study was to systematically evaluate risk prediction models for POD.CNKI, WanFang DATA, Vip Database, SinoMed, PubMed, Web of Science, Embase, and the Cochrane Library were searched for studies published by July 2023. The literature was screened independently by two investigators. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST) were respectively used for data extraction, risk of bias, and applicability assessment.A total of 16 studies on the construction of POD risk prediction models were included. The area under the ROC curve of the models ranges from 0.670 to 0.957. The most common predictors of POD included age, history of dementia, history of delirium, ASA classification, preoperative waiting time, and preoperative albumin level. All models had a high risk of bias, mainly due to inadequate sample size, inappropriate handling of missing data, a lack of model performance evaluation, and overfitting of the models.Overall, risk prediction models for POD in fragility hip fracture patients are still in the development stage. The majority of POD prediction models have substantial bias risks, attributable primarily to poor reporting of analysis and evaluation of model performance. In future research, it is recommended to conduct validation of the models or develop localized prediction models with demonstrated high performance, with the aim of benefiting POD screening.PROSPERO CRD42023449153.
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