{"title":"老年脆性髋部骨折患者术后谵妄的风险预测模型:系统回顾与批判性评估","authors":"Bingqian Zhou , Ai Wang , Hong Cao","doi":"10.1016/j.ijotn.2023.101077","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Elderly patients with fragility hip fracture<span> 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.</span></p></div><div><h3>Methods</h3><p><span><span>CNKI, WanFang DATA, Vip Database, SinoMed, PubMed, Web of Science, Embase, and the </span>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 </span>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.</p></div><div><h3>Results</h3><p>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<span> 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.</span></p></div><div><h3>Conclusions</h3><p>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.</p></div><div><h3>Systematic review registration</h3><p>PROSPERO CRD42023449153.</p></div>","PeriodicalId":45099,"journal":{"name":"International Journal of Orthopaedic and Trauma Nursing","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Risk prediction models for postoperative delirium in elderly patients with fragility hip fracture: A systematic review and critical appraisal\",\"authors\":\"Bingqian Zhou , Ai Wang , Hong Cao\",\"doi\":\"10.1016/j.ijotn.2023.101077\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Elderly patients with fragility hip fracture<span> 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.</span></p></div><div><h3>Methods</h3><p><span><span>CNKI, WanFang DATA, Vip Database, SinoMed, PubMed, Web of Science, Embase, and the </span>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 </span>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.</p></div><div><h3>Results</h3><p>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<span> 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.</span></p></div><div><h3>Conclusions</h3><p>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.</p></div><div><h3>Systematic review registration</h3><p>PROSPERO CRD42023449153.</p></div>\",\"PeriodicalId\":45099,\"journal\":{\"name\":\"International Journal of Orthopaedic and Trauma Nursing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Orthopaedic and Trauma Nursing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1878124123000813\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Orthopaedic and Trauma Nursing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1878124123000813","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 0
摘要
背景老年脆性髋部骨折患者术后谵妄(POD)的发生率仍然很高,这对预后有很大的负面影响,并造成巨大的经济负担。目前已开发出许多风险预测模型来早期检测 POD。然而,这些模型的偏倚风险和临床适用性仍不明确。本研究旨在系统评估 POD 的风险预测模型。方法检索 CNKI、万方数据、维普数据库、SinoMed、PubMed、Web of Science、Embase 和 Cochrane 图书馆 2023 年 7 月前发表的研究。文献由两名研究人员独立筛选。数据提取、偏倚风险和适用性评估分别采用了预测模型研究系统性综述批判性评估和数据提取清单(CHARMS)和预测模型偏倚风险评估工具(PROBAST)。模型的 ROC 曲线下面积从 0.670 到 0.957 不等。最常见的 POD 预测因素包括年龄、痴呆史、谵妄史、ASA 分级、术前等待时间和术前白蛋白水平。所有模型的偏倚风险都很高,主要原因是样本量不足、缺失数据处理不当、缺乏模型性能评估以及模型过度拟合。大多数 POD 预测模型都存在很大的偏倚风险,主要原因是分析报告和模型性能评估不完善。在未来的研究中,建议对模型进行验证,或开发已证明性能较高的局部预测模型,以促进 POD 筛查。
Risk prediction models for postoperative delirium in elderly patients with fragility hip fracture: A systematic review and critical appraisal
Background
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.
Methods
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.
Results
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.
Conclusions
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.