Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Beijing University of Chinese Medicine, Beijing, China.
PLoS One. 2023 Jul 31;18(7):e0283307. doi: 10.1371/journal.pone.0283307. eCollection 2023.
A considerable number of risk models, which predict outcomes in mortality and readmission rates, have been developed for patients with acute heart failure (AHF) to help stratify patients by risk level, improve decision making, and save medical resources. However, some models exist in a clinically useful manner such as risk scores or online calculators, while others are not, providing only limited information that prevents clinicians and patients from using them. The reported performance of some models varied greatly when predicting at multiple time points and being validated in different cohorts, which causes model users uncertainty about the predictive accuracy of these models. The foregoing leads to users facing difficulties in the selection of prediction models, and even sometimes being reluctant to utilize models. Therefore, a systematic review to assess the performance at multiple time points, applicability, and clinical impact of extant prediction models for mortality and readmission in AHF patients is essential. It may facilitate the selection of models for clinical implementation.
Four databases will be searched from their inception onwards. Multivariable prognostic models for mortality and/or readmission in AHF patients will be eligible for review. Characteristics and the clinical impact of included models will be summarized qualitatively and quantitatively, and models with clinical utility will be compared with those without. Predictive performance measures of included models with an analogous clinical outcome appraised repeatedly, will be compared and synthesized by a meta-analysis. Meta-analysis of validation studies for a common prediction model at the same time point will also be performed. We will also provide an overview of critical appraisal of the risk of bias, applicability, and reporting transparency of included studies using the PROBAST tool and TRIPOD statement.
PROSPERO registration number CRD42021256416.
已经开发出许多预测急性心力衰竭(AHF)患者死亡率和再入院率的风险模型,以帮助根据风险水平对患者进行分层,改善决策,并节省医疗资源。然而,有些模型以有用的方式存在,如风险评分或在线计算器,而有些则不然,仅提供有限的信息,使临床医生和患者无法使用。一些模型在预测多个时间点和在不同队列中验证时的性能差异很大,这使得模型使用者对这些模型的预测准确性不确定。上述情况导致模型使用者在选择预测模型时面临困难,甚至有时不愿意使用模型。因此,对现有用于预测 AHF 患者死亡率和再入院率的预测模型在多个时间点的性能、适用性和临床影响进行系统评价是至关重要的。这可能有助于选择用于临床实施的模型。
将从四个数据库的创建开始进行搜索。将有资格对多变量预后模型进行审查,这些模型用于预测 AHF 患者的死亡率和/或再入院率。将定性和定量地总结纳入模型的特征和临床影响,并将具有临床实用性的模型与不具有临床实用性的模型进行比较。通过荟萃分析比较和综合具有类似临床结局的纳入模型的预测性能指标。还将同时对同一时间点的通用预测模型的验证研究进行荟萃分析。我们还将使用 PROBAST 工具和 TRIPOD 声明对纳入研究的偏倚风险、适用性和报告透明度进行批判性评估概述。
PROSPERO 注册号 CRD42021256416。