Division of Research, Kaiser Permanente Northern California, Oakland.
Department of Surgery, University of California San Francisco-East Bay, Oakland.
JAMA Surg. 2022 May 1;157(5):e220172. doi: 10.1001/jamasurg.2022.0172. Epub 2022 May 11.
Electronic frailty metrics have been developed for automated frailty assessment and include the Hospital Frailty Risk Score (HFRS), the Electronic Frailty Index (eFI), the 5-Factor Modified Frailty Index (mFI-5), and the Risk Analysis Index (RAI). Despite substantial differences in their construction, these 4 electronic frailty metrics have not been rigorously compared within a surgical population.
To characterize the associations between 4 electronic frailty metrics and to measure their predictive value for adverse surgical outcomes.
DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used electronic health record data from patients who underwent abdominal surgery from January 1, 2010, to December 31, 2020, at 20 medical centers within Kaiser Permanente Northern California (KPNC). Participants included adults older than 50 years who underwent abdominal surgical procedures at KPNC from 2010 to 2020 that were sampled for reporting to the National Surgical Quality Improvement Program.
Pearson correlation coefficients between electronic frailty metrics and area under the receiver operating characteristic curve (AUROC) of univariate models and multivariate preoperative risk models for 30-day mortality, readmission, and morbidity, which was defined as a composite of mortality and major postoperative complications.
Within the cohort of 37 186 patients, mean (SD) age, 67.9 (female, 19 127 [51.4%]), correlations between pairs of metrics ranged from 0.19 (95% CI, 0.18- 0.20) for mFI-5 and RAI 0.69 (95% CI, 0.68-0.70). Only 1085 of 37 186 (2.9%) were classified as frail based on all 4 metrics. In univariate models for morbidity, HFRS demonstrated higher predictive discrimination (AUROC, 0.71; 95% CI, 0.70-0.72) than eFI (AUROC, 0.64; 95% CI, 0.63-0.65), mFI-5 (AUROC, 0.58; 95% CI, 0.57-0.59), and RAI (AUROC, 0.57; 95% CI, 0.57-0.58). The predictive discrimination of multivariate models with age, sex, comorbidity burden, and procedure characteristics for all 3 adverse surgical outcomes improved by including HFRS into the models.
In this cohort study, the 4 electronic frailty metrics demonstrated heterogeneous correlation and classified distinct groups of surgical patients as frail. However, HFRS demonstrated the highest predictive value for adverse surgical outcomes.
电子衰弱指标已被开发用于自动衰弱评估，包括医院衰弱风险评分（HFRS）、电子衰弱指数（eFI）、五因素改良衰弱指数（mFI-5）和风险分析指数（RAI）。尽管它们在构建上存在很大差异，但这 4 种电子衰弱指标尚未在外科人群中进行严格比较。
描述 4 种电子衰弱指标之间的相关性，并衡量它们对不良手术结局的预测价值。
设计、地点和参与者：本回顾性队列研究使用了 2020 年 1 月 1 日至 12 月 31 日期间 Kaiser Permanente Northern California（KPNC）20 家医疗中心接受腹部手术的患者的电子健康记录数据。参与者包括年龄大于 50 岁的成年人，他们在 2010 年至 2020 年期间在 KPNC 接受腹部手术，并被抽样报告给国家手术质量改进计划。
Pearson 相关系数用于比较电子衰弱指标之间的相关性，以及用于 30 天死亡率、再入院和发病率的单变量模型和多变量术前风险模型的受试者工作特征曲线下面积（AUROC），发病率定义为死亡率和主要术后并发症的复合。
在 37186 例患者队列中，平均（SD）年龄为 67.9（女性 19127[51.4%]），指标之间的相关性范围从 mFI-5 和 RAI 0.19（95%CI，0.18-0.20）到 0.69（95%CI，0.68-0.70）。仅 37186 例中的 1085 例（2.9%）根据所有 4 项指标被归类为衰弱。在发病率的单变量模型中，HFRS 表现出更高的预测区分度（AUROC，0.71；95%CI，0.70-0.72），而 eFI（AUROC，0.64；95%CI，0.63-0.65）、mFI-5（AUROC，0.58；95%CI，0.57-0.59）和 RAI（AUROC，0.57；95%CI，0.57-0.58）。在包括 HFRS 在内的模型中，多变量模型中与年龄、性别、合并症负担和手术特征相关的所有 3 种不良手术结局的预测区分度均有所提高。
在本队列研究中，这 4 种电子衰弱指标表现出异质性相关性，并将不同的外科患者分组归类为衰弱。然而，HFRS 对不良手术结局的预测价值最高。
