School of Pharmacy, Pharmacy Australia Centre of Excellence, The University of Queensland, Brisbane, QLD, 4102, Australia.
Princess Alexandra Hospital, Metro South Health, 199 Ipswich Road, Woolloongabba, Brisbane, QLD, 4102, Australia.
Br J Clin Pharmacol. 2018 May;84(5):846-864. doi: 10.1111/bcp.13514. Epub 2018 Feb 22.
An emerging approach to reducing hospital adverse drug events is the use of predictive risk scores. The aim of this systematic review was to critically appraise models developed for predicting adverse drug event risk in inpatients.
Embase, PubMed, CINAHL and Scopus databases were used to identify studies of predictive risk models for hospitalized adult inpatients. Studies had to have used multivariable logistic regression for model development, resulting in a score or rule with two or more variables, to predict the likelihood of inpatient adverse drug events. The Checklist for the critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) was used to critically appraise eligible studies.
Eleven studies met the inclusion criteria and were included in the review. Ten described the development of a new model, whilst one study revalidated and updated an existing score. Studies used different definitions for outcome but were synonymous with or closely related to adverse drug events. Four studies undertook external validation, five internally validated and two studies did not validate their model. No studies evaluated impact of risk scores on patient outcomes.
Adverse drug event risk prediction is a complex endeavour but could help to improve patient safety and hospital resource management. Studies in this review had some limitations in their methods for model development, reporting and validation. Two studies, the BADRI and Trivalle's risk scores, used better model development and validation methods and reported reasonable performance, and so could be considered for further research.
减少医院药物不良事件的一种新兴方法是使用预测风险评分。本系统评价的目的是批判性评估用于预测住院患者药物不良事件风险的模型。
使用 Embase、PubMed、CINAHL 和 Scopus 数据库来确定针对住院成年患者的预测风险模型的研究。研究必须使用多变量逻辑回归来开发模型，从而得出具有两个或更多变量的评分或规则，以预测住院药物不良事件的可能性。使用预测模型研究的系统评价的批判性评估和数据提取清单（CHARMS）来批判性地评估合格的研究。
有 11 项研究符合纳入标准并被纳入综述。其中 10 项描述了新模型的开发，而 1 项研究对现有评分进行了重新验证和更新。研究使用不同的结局定义，但与药物不良事件同义或密切相关。四项研究进行了外部验证，五项进行了内部验证，两项研究未验证其模型。没有研究评估风险评分对患者结局的影响。
药物不良事件风险预测是一项复杂的工作，但可以帮助提高患者安全性和医院资源管理。本综述中的研究在模型开发、报告和验证方法方面存在一些局限性。有两项研究，即 BADRI 和 Trivalle 的风险评分，使用了更好的模型开发和验证方法，并报告了合理的性能，因此可以考虑进一步研究。
