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未来高需求高成本医疗保健使用的预测模型:系统评价。

Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review.

机构信息

Section of Medical Decision Making, Department of Public Health, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, 3015, GD, Rotterdam, The Netherlands.

Department of Internal Medicine, Northwest Clinics, Alkmaar, The Netherlands.

出版信息

J Gen Intern Med. 2022 May;37(7):1763-1770. doi: 10.1007/s11606-021-07333-z. Epub 2022 Jan 11.

Abstract

BACKGROUND

In an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme's success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability.

METHODS

Ovid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST).

RESULTS

Of 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601-174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients' risk to become part of some top percentage of the cost distribution (top-1-20%) within a mean time horizon of 16 months (range 12-60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as 'high' in 40 studies (67%), mostly due to a 'high' risk of bias in the subdomain 'Analysis' (n = 37; 62%).

DISCUSSION

This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient's risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.

摘要

背景

为了提高医疗服务质量和成本效益,已经开发了各种针对高需求高费用(HNHC)患者的护理管理计划。早期识别有成为 HNHC 风险的患者(即病例发现)对于计划的成功至关重要。我们旨在系统地识别预测模型,以预测成年人未来 HNHC 医疗保健使用情况,描述其预测性能并评估其适用性。

方法

系统检索了 Ovid MEDLINE® All、EMBASE、CINAHL、Web of Science 和 Google Scholar 从成立到 2021 年 1 月 31 日的数据。通过预测模型风险偏倚评估工具(PROBAST)进行风险偏倚和方法学质量评估。

结果

在 5890 项研究中,有 60 项研究符合纳入标准。在这些研究中,使用了 313 个中位数开发队列大小为 20248 名患者(IQR 5601-174242)的独特模型。预测因子来自数据来源的组合,最常见的是索赔数据(n=37;62%)和患者调查数据(n=29;48%)。大多数研究(n=36;60%)估计患者在平均 16 个月(范围 12-60)内成为成本分布前 1-20%的一部分的风险。有 5 项研究(8%)预测了 HNHC 持续多年。45 项研究(76%)进行了模型验证。14 项研究(23%)报告了模型在校准和区分方面的性能。总体风险偏倚被评为“高”,有 40 项研究(67%),主要是由于“分析”子领域(n=37;62%)的“高”风险偏倚。

讨论

这是第一项系统评价(PROSPERO CRD42020164734),评估预测 HNHC 医疗保健使用情况的非专有预测模型。由于存在异质性,因此无法进行荟萃分析。大多数确定的模型估计了患者在随后一年中产生高医疗费用的风险。然而,HNHC 护理管理计划的病例发现策略最好由预测 HNHC 持续性的模型来指导。因此,未来的研究不仅应集中于验证和扩展现有模型,还应集中于评估其临床实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99bf/9130365/27a1088a8940/11606_2021_7333_Fig1_HTML.jpg

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