Table_1_The Disconnect Between Development and Intended Use of Clinical Prediction Models for Covid-19: A Systematic Review and Real-World Data Illustration.DOCX

BackgroundThe SARS-CoV-2 pandemic has boosted the appearance of clinical predictions models in medical literature. Many of these models aim to provide guidance for decision making on treatment initiation. Special consideration on how to account for post-baseline treatments is needed when developing such models. We examined how post-baseline treatment was handled in published Covid-19 clinical prediction models and we illustrated how much estimated risks may differ according to how treatment is handled.MethodsFirstly, we reviewed 33 Covid-19 prognostic models published in literature in the period up to 5 May 2020. We extracted: (1) the reported intended use of the model; (2) how treatment was incorporated during model development and (3) whether the chosen analysis strategy was in agreement with the intended use. Secondly, we used nationwide Dutch data on hospitalized patients who tested positive for SARS-CoV-2 in 2020 to illustrate how estimated mortality risks will differ when using four different analysis strategies to model ICU treatment.ResultsOf the 33 papers, 21 (64%) had misalignment between intended use and analysis strategy, 7 (21%) were unclear about the estimated risk and only 5 (15%) had clear alignment between intended use and analysis strategy. We showed with real data how different approaches to post-baseline treatment yield different estimated mortality risks, ranging between 33 and 46% for a 75 year-old patient with two medical conditions.ConclusionsMisalignment between intended use and analysis strategy is common in reported Covid-19 clinical prediction models. This can lead to considerable under or overestimation of intended risks.

背景：SARS-CoV-2大流行促进了临床预测模型在医学文献中的出现。众多模型旨在为治疗启动提供决策指导。在开发此类模型时，对基线后治疗如何进行考量需给予特别关注。本研究旨在探讨基线后治疗在已发表的Covid-19临床预测模型中的处理方式，并阐明根据治疗处理方式的不同，估算风险可能存在多大差异。方法：首先，我们对截至2020年5月5日发表的33篇Covid-19预后模型进行了文献综述。提取内容包括：（1）模型报告的预期用途；（2）模型开发过程中治疗方法的融入方式；（3）所选分析策略是否与预期用途相符。其次，我们利用2020年荷兰全国范围内住院患者的数据，以SARS-CoV-2检测阳性者为对象，展示了采用四种不同的分析策略对ICU治疗进行建模时，估算的死亡率风险如何产生差异。结果：在33篇论文中，有21篇（64%）报告的预期用途与分析策略存在不一致，7篇（21%）对估算风险不明确，仅有5篇（15%）在预期用途与分析策略之间具有清晰的对应关系。我们通过实际数据展示了不同的基线后治疗方法会导致估算的死亡率风险存在差异，对于一个患有两种疾病的75岁患者，这一风险差异介于33%至46%之间。结论：在报告的Covid-19临床预测模型中，预期用途与分析策略之间的不匹配现象较为普遍，这可能导致预期风险的显著低估或高估。