Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
PLoS One. 2024 Mar 11;19(3):e0294148. doi: 10.1371/journal.pone.0294148. eCollection 2024.
Our goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic.
A systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions.
From 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%).
Overall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.
我们的目标是回顾现有关于原发性高血压预测风险的文献，对其进行综合分析，并就该主题的未来工作提出建议。
在 PUBMED 和 Web of Science 数据库中对一般健康人群中用于预测原发性高血压的预后风险预测模型的研究进行了系统检索。使用预测模型风险偏倚评估工具（PROBAST）检查表评估研究质量。使用三级荟萃分析获得汇总 AUC/C 统计量估计值。在荟萃回归中，通过研究和队列特征来探索异质性。
从 5090 个命中结果中，我们发现了 53 项符合条件的研究，并将其中的 47 项纳入荟萃分析。只有四项研究被评估为结果具有低偏倚风险。很少有模型经过外部验证，只有 Framingham 风险模型经过了三次以上的验证。机器学习模型的汇总 AUC/C 统计量为 0.82（0.77-0.86），传统模型为 0.78（0.76-0.80），两组均存在高度异质性（I2>99%）。研究内的组内相关系数分别为 60%和 90%。随访时间（P=0.0405）对 ML 模型有意义，年龄（P=0.0271）对传统模型有意义，可解释异质性。Framingham 风险模型的验证存在高度异质性（I2>99%）。
总体而言，纳入研究的质量评估为较差。AUC/C 统计量大多为可接受或良好，机器学习模型的 AUC/C 统计量高于传统模型。高度异质性意味着新风险模型的性能存在较大差异。此外，Framingham 风险模型验证的高度异质性表明模型在新人群中的性能存在差异。为了使研究人员能够评估高血压风险模型，我们鼓励遵守现有的风险模型报告和开发指南，特别是报告适当的性能指标。此外，我们建议通过考虑合理的基线模型和对现有模型进行外部验证，更加关注模型的验证。因此，必须为外部研究人员提供开发的风险模型。
