San Mateo Medical Center, 222 West 39th Avenue, San Mateo, CA 94403.
Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, 1912 Speedway, Austin, TX 78712.
J Bone Joint Surg Am. 2016 Jan 6;98(1):e2. doi: 10.2106/JBJS.N.01330.
Comparing outcomes across providers requires risk-adjustment models that account for differences in case mix. The burden of data collection from the clinical record can make risk-adjusted outcomes difficult to measure. The purpose of this study was to develop risk-adjustment models for hip fracture repair (HFR), total hip arthroplasty (THA), and total knee arthroplasty (TKA) that weigh adequacy of risk adjustment against data-collection burden.
We used data from the American College of Surgeons National Surgical Quality Improvement Program to create derivation cohorts for HFR (n = 7000), THA (n = 17,336), and TKA (n = 28,661). We developed logistic regression models for each procedure using age, sex, American Society of Anesthesiologists (ASA) physical status classification, comorbidities, laboratory values, and vital signs-based comorbidities as covariates, and validated the models with use of data from 2012.
The derivation models' C-statistics for mortality were 80%, 81%, 75%, and 92% and for adverse events were 68%, 68%, 60%, and 70% for HFR, THA, TKA, and combined procedure cohorts. Age, sex, and ASA classification accounted for a large share of the explained variation in mortality (50%, 58%, 70%, and 67%) and adverse events (43%, 45%, 46%, and 68%). For THA and TKA, these three variables were nearly as predictive as models utilizing all covariates. HFR model discrimination improved with the addition of comorbidities and laboratory values; among the important covariates were functional status, low albumin, high creatinine, disseminated cancer, dyspnea, and body mass index. Model performance was similar in validation cohorts.
Risk-adjustment models using data from health records demonstrated good discrimination and calibration for HFR, THA, and TKA. It is possible to provide adequate risk adjustment using only the most predictive variables commonly available within the clinical record. This finding helps to inform the trade-off between model performance and data-collection burden as well as the need to define priorities for data capture from electronic health records. These models can be used to make fair comparisons of outcome measures intended to characterize provider quality of care for value-based-purchasing and registry initiatives.
比较不同医疗机构的治疗结果需要使用能够反映病例组合差异的风险调整模型。从临床记录中收集数据的负担可能会使风险调整后的结果难以衡量。本研究旨在开发用于髋关节骨折修复（HFR）、全髋关节置换术（THA）和全膝关节置换术（TKA）的风险调整模型，这些模型在权衡风险调整的充分性与数据收集负担方面进行了考量。
我们使用美国外科医师学会国家外科质量改进计划（American College of Surgeons National Surgical Quality Improvement Program）的数据，为 HFR（n=7000）、THA（n=17336）和 TKA（n=28661）创建了推导队列。我们使用年龄、性别、美国麻醉医师协会（American Society of Anesthesiologists，ASA）身体状况分级、合并症、实验室值和基于生命体征的合并症作为协变量，为每个手术程序开发了逻辑回归模型，并使用 2012 年的数据对模型进行了验证。
推导模型的死亡率的 C 统计量分别为 80%、81%、75%和 92%，不良事件的 C 统计量分别为 68%、68%、60%和 70%，适用于 HFR、THA、TKA 和联合手术队列。年龄、性别和 ASA 分级在死亡率（50%、58%、70%和 67%）和不良事件（43%、45%、46%和 68%）的解释性差异中占了很大比例。对于 THA 和 TKA，这三个变量与使用所有协变量的模型几乎具有相同的预测能力。HFR 模型的区分度随着合并症和实验室值的增加而提高；重要的协变量包括功能状态、低白蛋白、高肌酐、转移性癌症、呼吸困难和体重指数。验证队列中的模型性能相似。
使用健康记录数据的风险调整模型对 HFR、THA 和 TKA 的区分度和校准度均较好。仅使用临床记录中最常见的、具有较强预测能力的变量，就可以进行充分的风险调整。这一发现有助于确定模型性能与数据收集负担之间的权衡，以及从电子健康记录中定义数据采集优先级的必要性。这些模型可用于对旨在描述提供者医疗质量的结果指标进行公平比较，以便为基于价值的采购和登记倡议提供依据。
