Centre of Cardiovascular Research and Education in Therapeutics, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; School of Public Health, Mekelle University, Mekelle, Ethiopia.
Centre of Cardiovascular Research and Education in Therapeutics, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.
J Card Fail. 2017 Sep;23(9):680-687. doi: 10.1016/j.cardfail.2017.03.005. Epub 2017 Mar 21.
Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models.
EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was < 10 in 13 models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P < .001) and sample size (P = .007).
There is an abundance of HF risk prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution.
已经开发出许多预测心力衰竭（HF）事件风险的模型；然而，其方法学严谨性和报告的证据尚不清楚。本研究批判性地评价了事件性 HF 风险预测模型的方法学基础。
在 1990 年至 2016 年 6 月期间，通过 EMBASE 和 PubMed 检索了至少报告了 1 个用于 HF 预测的多变量模型的文章。提取了模型开发信息，包括研究设计、变量编码、缺失数据和预测因子选择。共纳入了 19 项研究，报告了 40 个风险预测模型。现有的模型具有可接受的判别能力（C 统计量＞0.70），尽管只有 6 个模型经过了外部验证。候选变量选择是基于 11 个模型的单变量筛选的统计学意义，而在 12 个模型中则不清楚。在 16 个模型中保留了连续预测因子，而在 16 个模型中不清楚如何处理连续变量。在报告缺失数据的 23 个模型中有 19 个排除了缺失值，并且在 13 个模型中每个变量的事件数＜10。仅有 2 个模型提供了推荐的回归方程。模型的判别能力在年龄方面（P＜0.001）和样本量方面（P=0.007）存在显著的异质性。
有大量 HF 风险预测模型具有足够的判别能力，尽管很少有经过外部验证。经常使用不推荐用于风险预测建模的方法，并且应该谨慎应用由此产生的算法。
