Department of Business Administration and Health Care Management, University of Cologne, Cologne, Germany
Department of Business Administration and Health Care Management, University of Cologne, Cologne, Germany.
BMJ Open. 2022 Mar 30;12(3):e055956. doi: 10.1136/bmjopen-2021-055956.
To summarise multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in paediatrics, describe their performance and completeness in reporting, and determine their potential for application in practice.
Systematic review.
CINAHL, Embase and PubMed up to 7 October 2021.
English or German language studies aiming to develop or validate a multivariable predictive model for 30-day paediatric UHRs related to all-cause, surgical conditions or general medical conditions were included.
Study characteristics, risk factors significant for predicting readmissions and information about performance measures (eg, c-statistic) were extracted. Reporting quality was addressed by the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' (TRIPOD) adherence form. The study quality was assessed by applying six domains of potential biases. Due to expected heterogeneity among the studies, the data were qualitatively synthesised.
Based on 28 studies, 37 predictive models were identified, which could potentially be used for determining individual 30-day UHR risk in paediatrics. The number of study participants ranged from 190 children to 1.4 million encounters. The two most common significant risk factors were comorbidity and (postoperative) length of stay. 23 models showed a c-statistic above 0.7 and are primarily applicable at discharge. The median TRIPOD adherence of the models was 59% (P-P, 55%-69%), ranging from a minimum of 33% to a maximum of 81%. Overall, the quality of many studies was moderate to low in all six domains.
Predictive models may be useful in identifying paediatric patients at increased risk of readmission. To support the application of predictive models, more attention should be placed on completeness in reporting, particularly for those items that may be relevant for implementation in practice.
总结儿科 30 天非计划性再入院(UHR)的多变量预测模型,描述其报告的性能和完整性,并确定其在实践中的应用潜力。
系统评价。
截至 2021 年 10 月 7 日,CINAHL、Embase 和 PubMed。
纳入旨在开发或验证与全因、手术或一般医疗条件相关的儿科 30 天 UHR 多变量预测模型的英文或德文研究。
提取研究特征、对再入院有预测意义的危险因素以及关于性能指标(如 c 统计量)的信息。采用“个体预后或诊断的多变量预测模型透明报告”(TRIPOD)依从性表格来解决报告质量问题。应用潜在偏倚的六个领域评估研究质量。由于研究之间预计存在异质性,因此对数据进行了定性综合。
基于 28 项研究,确定了 37 个预测模型,这些模型可能用于确定儿科患者 30 天 UHR 的个体风险。研究参与者人数从 190 名儿童到 140 万次就诊不等。两个最常见的重要危险因素是合并症和(术后)住院时间。23 个模型的 c 统计量大于 0.7,主要适用于出院时。模型的中位数 TRIPOD 依从性为 59%(P-P,55%-69%),范围从 33%到 81%不等。总体而言,在所有六个领域,许多研究的质量均为中等到低。
预测模型可用于识别再入院风险增加的儿科患者。为了支持预测模型的应用,应更加重视报告的完整性,特别是对于那些可能与实践应用相关的项目。