Objective: There are several efforts to re-learn the 2013 ACC/AHA pooled cohort equations (PCE) for patients with specific comorbidities and geographic locations. With over 363 customized risk models in the literature, we aim to evaluate such revised models to determine if the performance improvements translate to gains in clinical utility. Methods: We re-train a baseline PCE using the ACC/AHA PCE variables and revise it to incorporate subject-level geographic location and comorbidity information. We apply fixed effects, random effects, and extreme gradient boosting models to handle the correlation and heterogeneity induced by locations. Models are trained using 2,464,522 claims records from Optum Clinformatics Data Mart and validated in the hold-out set (N=1,056,224). We evaluate models' performance overall and across subgroups defined by the presence or absence of chronic kidney disease (CKD) or rheumatoid arthritis (RA) and geographic locations. We evaluate models' expected net benefit using decision curve analysis and models' statistical properties using several discrimination and calibration metrics. Results: The baseline PCE is miscalibrated overall, in patients with CKD or RA, and locations with small populations. Our revised models improved both the overall (GND P-value=0.41) and subgroup calibration but only enhanced net benefit in the underrepresented subgroups. The gains are larger in the subgroups with comorbidities and heterogeneous across geographic locations. Conclusions: Revising the PCE with comorbidity and location information significantly enhanced models' calibration; however, such improvements do not necessarily translate to clinical gains. Thus, we recommend future works to quantify the consequences from using risk calculators to guide clinical decisions.
翻译:目标:为重新阅读2013 ACC/AHA 集合组合组合组合方程式(PCE),我们做出了一些努力,重新阅读2013 ACC/AHA 组合组合组合组合方程式(PCE),针对特定发病和地理位置的患者。在文献中有超过363个定制的风险模型,我们的目标是评价这些经过修订的模型,以确定性能改进是否转化为临床效用的增益。方法:我们利用ACC/AHA PCE变量重新培训基线PCE,并对其进行修订,以纳入主题级别地理位置和腐蚀性信息。我们应用固定效应、随机效应和极端梯度加速模型模型来处理由地点引发的关联性和异异异性。模型经过培训,使用来自Optum Clinformatical 数据站点的2 464 522个索赔记录进行了培训,并在暂停使用数据集(N=1,056,224)。我们用长期肾脏病(CD)或循环(RACE)的增量计算结果评估模型的总体性结果。我们仅从长期肾脏(RA)或亚值计算结果。我们的数据比值(C)比值(C)比值(Crationalalalalalalal)和亚值(仅使用决定)和亚值的改进了我们的数据分析),我们用决定和亚值(我们的数据分析)比值(我们的数据分析)比值分析)比值(不必要)比值分析)比值(比值(我们的数据分析)比值和子(C)。