Background:Health status outcomes are increasingly being promoted as measures of health care quality, given their importance to patients. In heart failure (HF), an American College of Cardiology/American Heart Association Task Force proposed using the proportion of patients with preserved health status as a quality measure but not as a performance measure because risk adjustment methods were not available.Methods:We built risk adjustment models for alive with preserved health status and for preserved health status alone in a prospective registry of outpatients with HF with reduced ejection fraction across 146 US centers between December 2015 and October 2017. Preserved health status was defined as not having a ≥5-point decrease in the Kansas City Cardiomyopathy Questionnaire Overall Summary score at 1 year. Using only patient-level characteristics, hierarchical multivariable logistic regression models were developed for 1-year outcomes and validated using data from 1 to 2 years. We examined model calibration, discrimination, and variability in sites’ unadjusted and adjusted rates.Results:Among 3932 participants (median age [interquartile range] 68 years [59–75], 29.7% female, 75.4% White), 2703 (68.7%) were alive with preserved health status, 902 (22.9%) were alive without preserved health status, and 327 (8.3%) had died by 1 year. The final risk adjustment model for alive with preserved health status included baseline Kansas City Cardiomyopathy Questionnaire Overall Summary, age, race, employment status, annual income, body mass index, depression, atrial fibrillation, renal function, number of hospitalizations in the past 1 year, and duration of HF (optimism-corrected C statistic=0.62 with excellent calibration). Similar results were observed when deaths were ignored. The risk standardized proportion of patients alive with preserved health status across the 146 sites ranged from 62% at the 10th percentile to 75% at the 90th percentile. Variability across sites was modest and changed minimally with risk adjustment.Conclusions:Through leveraging data from a large, outpatient, observational registry, we identified key factors to risk adjust sites’ proportions of patients with preserved health status. These data lay the foundation for building quality measures that quantify treatment outcomes from patients’ perspectives.

中文翻译：

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心力衰竭和射血分数降低患者保持健康状态的风险调整模型：CHAMP-HF 注册中心


背景：鉴于健康状况结果对患者的重要性，健康状况结果越来越多地被推广为医疗保健质量的衡量标准。在心力衰竭（HF）方面，美国心脏病学会/美国心脏协会工作组建议使用保持健康状态的患者比例作为质量衡量标准，而不是绩效衡量标准，因为没有风险调整方法。方法：我们建立了2015 年 12 月至 2017 年 10 月期间，在美国 146 个中心对射血分数降低的心力衰竭门诊患者进行前瞻性登记，建立了保持健康状态存活者和仅保持健康状态的风险调整模型。保持健康状态定义为不具有≥5-堪萨斯城心肌病问卷总体评分在一年内下降。仅使用患者层面的特征，针对 1 年结果开发了分层多变量逻辑回归模型，并使用 1 至 2 年的数据进行验证。我们检查了模型校准、区分度以及站点未调整率和调整率的变异性。结果：在 3932 名参与者中（中位年龄 [四分位距] 68 岁 [59–75]，29.7% 女性，75.4% 白人），2703 名参与者（68.7%） ）在健康状况良好的情况下存活，902 例（22.9%）在健康状况未保持的情况下存活，327 例（8.3%）在 1 年内死亡。健康状况良好的最终风险调整模型包括基线堪萨斯城心肌病问卷总体摘要、年龄、种族、就业状况、年收入、体重指数、抑郁、心房颤动、肾功能、过去 1 年住院次数和 HF 持续时间（乐观校正 C 统计量=0.62，具有出色的校准）。当忽略死亡时，也观察到类似的结果。 146 个地点健康状况良好的存活患者的风险标准化比例范围从第 10 个百分位数的 62% 到第 90 个百分位数的 75%。各研究中心之间的差异不大，并且随着风险调整的变化很小。结论：通过利用大型门诊观察登记处的数据，我们确定了风险调整研究中心保持健康状况的患者比例的关键因素。这些数据为建立从患者角度量化治疗结果的质量措施奠定了基础。