Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, North Carolina.
JAMA Netw Open. 2023 Nov 1;6(11):e2341915. doi: 10.1001/jamanetworkopen.2023.41915.
Electronic frailty index (eFI) is an automated electronic health record (EHR)-based tool that uses a combination of clinical encounters, diagnosis codes, laboratory workups, medications, and Medicare annual wellness visit data as markers of frailty status. The association of eFI with postanesthesia adverse outcomes has not been evaluated.
To examine the association of frailty, calculated as eFI at the time of the surgical procedure and categorized as fit, prefrail, or frail, with adverse events after elective noncardiac surgery.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study was conducted at a tertiary care academic medical center in Winston-Salem, North Carolina. The cohort included patients 55 years or older who underwent noncardiac surgery of at least 1 hour in duration between October 1, 2017, and June 30, 2021.
Frailty calculated by the eFI tool. Preoperative eFI scores were calculated based on available data 1 day prior to the procedure and categorized as fit (eFI score: ≤0.10), prefrail (eFI score: >0.10 to ≤0.21), or frail (eFI score: >0.21).
The primary outcome was a composite of the following 8 adverse component events: 90-item Patient Safety Indicators (PSI 90) score, hospital-acquired conditions, in-hospital mortality, 30-day mortality, 30-day readmission, 30-day emergency department visit after surgery, transfer to a skilled nursing facility after surgery, or unexpected intensive care unit admission after surgery. Secondary outcomes were each of the component events of the composite.
Of the 33 449 patients (median [IQR] age, 67 [61-74] years; 17 618 females [52.7%]) included, 11 563 (34.6%) were classified as fit, 15 928 (47.6%) as prefrail, and 5958 (17.8%) as frail. Using logistic regression models that were adjusted for age, sex, race and ethnicity, and comorbidity burden, patients with prefrail (odds ratio [OR], 1.24; 95% CI, 1.18-1.30; P < .001) and frail (OR, 1.71; 95% CI, 1.58-1.82; P < .001) statuses were more likely to experience postoperative adverse events compared with patients with a fit status. Subsequent adjustment for all other potential confounders or covariates did not alter this association. For every increase in eFI of 0.03 units, the odds of a composite of postoperative adverse events increased by 1.06 (95% CI, 1.03-1.13; P < .001).
This cohort study found that frailty, as measured by an automatically calculated index integrated within the EHR, was associated with increased risk of adverse events after noncardiac surgery. Deployment of eFI tools may support screening and possible risk modification, especially in patients who undergo high-risk surgery.
电子虚弱指数 (eFI) 是一种基于电子病历 (EHR) 的自动化工具，它使用临床就诊、诊断代码、实验室检查、药物和医疗保险年度健康检查数据的组合作为虚弱状态的标志物。eFI 与麻醉后不良结局的关联尚未得到评估。
研究虚弱状况与非心脏手术后不良事件的关联，虚弱状况通过手术时的 eFI 计算得出，并分为健康、虚弱前期或虚弱。
设计、地点和参与者：这项队列研究在北卡罗来纳州温斯顿-塞勒姆的一家三级护理学术医疗中心进行。队列纳入了 2017 年 10 月 1 日至 2021 年 6 月 30 日期间接受至少 1 小时非心脏手术的 55 岁及以上患者。
由 eFI 工具计算的虚弱程度。术前 eFI 评分根据术前 1 天的可用数据计算得出，并分为健康（eFI 评分：≤0.10）、虚弱前期（eFI 评分：>0.10 至 ≤0.21）或虚弱（eFI 评分：>0.21）。
主要结局是以下 8 种不良事件综合而成的结果：90 项患者安全指标（PSI 90）评分、医院获得性疾病、院内死亡率、30 天死亡率、30 天再入院率、术后 30 天急诊就诊、术后转入康复护理院或术后意外转入重症监护病房。次要结局是复合结果的每个组成部分事件。
在纳入的 33449 名患者中（中位数[IQR]年龄，67[61-74]岁；17618 名女性[52.7%]），11563 名（34.6%）被归类为健康，15928 名（47.6%）为虚弱前期，5958 名（17.8%）为虚弱。使用调整年龄、性别、种族和民族以及合并症负担的逻辑回归模型，虚弱前期（比值比[OR]，1.24；95%CI，1.18-1.30；P<.001）和虚弱（OR，1.71；95%CI，1.58-1.82；P<.001）状态的患者与健康状态的患者相比，更有可能经历术后不良事件。随后调整所有其他潜在的混杂因素或协变量并没有改变这种关联。eFI 每增加 0.03 单位，复合术后不良事件的几率就会增加 1.06（95%CI，1.03-1.13；P<.001）。
这项队列研究发现，通过 EHR 中自动计算的指数衡量的虚弱程度与非心脏手术后不良事件的风险增加有关。部署 eFI 工具可能支持筛查和可能的风险调整，特别是在接受高风险手术的患者中。
