Jing Cheng-Yang, Zhang Le, Feng Lin, Li Jia-Chen, Liang Li-Rong, Hu Jing, Liao Xing
Center for Evidence Based Chinese Medicine, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Department of Clinical Epidemiology, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Front Cardiovasc Med. 2024 Oct 17;11:1449058. doi: 10.3389/fcvm.2024.1449058. eCollection 2024.
This study aimed to synthesize the recommendations for prediction models in cardiovascular clinical practice guidelines (CPGs) and assess the methodological quality of the relevant primary modeling studies.
We performed a systematic literature search of all available cardiovascular CPGs published between 2018 and 2023 that presented specific recommendations (whether in support or non-support) for at least one multivariable clinical prediction model. For the guideline-recommended models, the assessment of the methodological quality of their primary modeling studies was conducted using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).
In total, 46 qualified cardiovascular CPGs were included, with 69 prediction models and 80 specific recommendations. Of the 80 specific recommendations, 74 supported 57 models (53 were fully recommended and 4 were conditionally recommended) in cardiovascular practice with moderate to strong strength. Most of the guideline-recommended models were focused on predicting prognosis outcomes (53/57, 93%) in primary and tertiary prevention, focusing primarily on long-term risk stratification and prognosis management. A total of 10 conditions and 7 types of target population were involved in the 57 models, while heart failure (14/57, 25%) and a general population with or without cardiovascular risk factor(s) (12/57, 21%) received the most attention from the guidelines. The assessment of the methodological quality of 57 primary studies on the development of the guideline-recommended models revealed that only 40% of the modeling studies had a low risk of bias (ROB). The causes of high ROB were mainly in the analysis and participant domains.
Global cardiovascular CPGs presented an unduly positive appraisal of the existing prediction models in terms of ROB, leading to stronger recommendations than were warranted. Future cardiovascular practice may benefit from well-established clinical prediction models with better methodological quality and extensive external validation.
本研究旨在综合心血管临床实践指南（CPG）中关于预测模型的建议，并评估相关基础建模研究的方法学质量。
我们对2018年至2023年期间发表的所有心血管CPG进行了系统的文献检索，这些CPG对至少一种多变量临床预测模型提出了具体建议（无论是支持还是不支持）。对于指南推荐的模型，使用预测模型偏倚风险评估工具（PROBAST）对其基础建模研究的方法学质量进行评估。
总共纳入了46份合格的心血管CPG，包含69个预测模型和80条具体建议。在这80条具体建议中，74条以中等到强的力度支持了心血管实践中的57个模型（53个被完全推荐，4个被有条件推荐）。大多数指南推荐的模型集中于预测一级和三级预防中的预后结局（53/57，93%），主要侧重于长期风险分层和预后管理。57个模型涉及总共10种情况和7类目标人群，其中心力衰竭（14/57，25%）以及有或无心血管危险因素的普通人群（12/57，21%）受到指南的关注最多。对57项关于指南推荐模型开发的基础研究的方法学质量评估显示，只有40%的建模研究偏倚风险较低。高偏倚风险的原因主要在分析和参与者领域。
全球心血管CPG在偏倚风险方面对现有预测模型给予了过度积极的评价，导致推荐力度超过了合理水平。未来的心血管实践可能会受益于方法学质量更好且经过广泛外部验证的成熟临床预测模型。
