Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
BMJ Open. 2023 May 17;13(5):e073174. doi: 10.1136/bmjopen-2023-073174.
It is known that only a limited proportion of developed clinical prediction models (CPMs) are implemented and/or used in clinical practice. This may result in a large amount of research waste, even when considering that some CPMs may demonstrate poor performance. Cross-sectional estimates of the numbers of CPMs that have been developed, validated, evaluated for impact or utilized in practice, have been made in specific medical fields, but studies across multiple fields and studies following up the fate of CPMs are lacking.
We have conducted a systematic search for prediction model studies published between January 1995 and December 2020 using the Pubmed and Embase databases, applying a validated search strategy. Taking random samples for every calendar year, abstracts and articles were screened until a target of 100 CPM development studies were identified. Next, we will perform a forward citation search of the resulting CPM development article cohort to identify articles on external validation, impact assessment or implementation of those CPMs. We will also invite the authors of the development studies to complete an online survey to track implementation and clinical utilization of the CPMs.We will conduct a descriptive synthesis of the included studies, using data from the forward citation search and online survey to quantify the proportion of developed models that are validated, assessed for their impact, implemented and/or used in patient care. We will conduct time-to-event analysis using Kaplan-Meier plots.
No patient data are involved in the research. Most information will be extracted from published articles. We request written informed consent from the survey respondents. Results will be disseminated through publication in a peer-reviewed journal and presented at international conferences. OSF REGISTRATION: (https://osf.io/nj8s9).
已知只有有限比例的已开发临床预测模型（CPMs）在临床实践中得到实施和/或使用。这可能导致大量的研究浪费，即使考虑到一些 CPM 可能表现出较差的性能。在特定医学领域已经对已开发、验证、评估影响或在实践中使用的 CPM 数量进行了横断面估计，但缺乏跨多个领域的研究和对 CPM 命运的后续研究。
我们使用 Pubmed 和 Embase 数据库进行了系统搜索，检索了 1995 年 1 月至 2020 年 12 月期间发表的预测模型研究，应用了经过验证的搜索策略。对每一年的随机样本进行筛选，直到确定了 100 项 CPM 开发研究的摘要和文章。接下来，我们将对生成的 CPM 开发文章队列进行正向引用搜索，以确定关于这些 CPM 的外部验证、影响评估或实施的文章。我们还将邀请开发研究的作者完成在线调查，以跟踪 CPM 的实施和临床应用情况。我们将对纳入的研究进行描述性综合分析，使用来自正向引用搜索和在线调查的数据来量化已开发模型中经过验证、评估其影响、实施和/或用于患者护理的模型的比例。我们将使用 Kaplan-Meier 图进行时间事件分析。
研究不涉及患者数据。大多数信息将从已发表的文章中提取。我们将向调查受访者请求书面知情同意。研究结果将通过在同行评议期刊上发表和在国际会议上发表来传播。OSF 注册：（https://osf.io/nj8s9）。
