Automated Frailty Screening At-Scale for Pre-Operative Risk Stratification Using the Electronic Frailty Index.
Automated Frailty Screening At-Scale for Pre-Operative Risk Stratification Using the Electronic Frailty Index.
复制标题
DOI：
10.1111/jgs.17027
复制
发表时间：
2021-05
影响因子：
6.3
通讯作者：
Pajewski NM
中科院分区：
文献类型：
--
作者：
Callahan KE;Clark CJ;Edwards AF;Harwood TN;Williamson JD;Moses AW;Willard JJ;Cristiano JA;Meadows K;Hurie J;High KP;Meredith JW;Pajewski NM
虚弱与老年人的许多术后不良结局相关。目前的术前虚弱筛查工具需要额外的数据收集或客观评估，增加了费用并限制了大规模实施。评价电子健康记录（EHR）中集成的虚弱自动测量与非急诊手术的术后结局的相关性。回顾性队列研究。学术医疗中心。在2017年10月8日至2019年6月1日期间接受非急诊手术且住院时间≥ 24小时的65岁或以上患者。通过54项电子虚弱指数（eFI）测量的虚弱程度。住院时间、急性期后护理要求、30天再入院率和6个月全因死亡率。在4，831名独特患者（2，281名女性（47.3%）;平均（SD）年龄为73.2（5.9）岁）中，4，143名（85.7%）有足够的EHR数据来计算eFI，其中15.1%被归类为虚弱（eFI > 0.21），50.9%被归类为虚弱前（0.10 < eFI ≤ 0.21）。对于所有结局，通常有一个风险等级，eFI评分较高。例如，根据年龄、性别、人种/种族和美国麻醉医师协会分类进行调整，并考虑服务线的变异性，与健康患者相比，根据eFI确定为虚弱的患者对急性期后护理的需求更大（比值比（OR）= 1.68; 95%置信区间（CI）= 1.36-2.08），30天再入院率较高（风险比（HR）= 2.46; 95%CI = 1.72-3.52）和较高的全因死亡率（HR = 2.86; 95%CI = 1.84-4.44）。eFI是一种集成在EHR中的自动化虚弱数字标记，可以促进大规模的术前虚弱筛查。
Frailty is associated with numerous post-operative adverse outcomes in older adults. Current pre-operative frailty screening tools require additional data collection or objective assessments, adding expense and limiting large-scale implementation. To evaluate the association of an automated measure of frailty integrated within the Electronic Health Record (EHR) with post-operative outcomes for nonemergency surgeries. Retrospective cohort study. Academic Medical Center. Patients 65 years or older that underwent nonemergency surgery with an inpatient stay 24 hours or more between October 8th, 2017 and June 1st, 2019. Frailty as measured by a 54-item electronic frailty index (eFI). Inpatient length of stay, requirements for post-acute care, 30-day readmission, and 6-month all-cause mortality. Of 4,831 unique patients (2,281 females (47.3%); mean (SD) age, 73.2 (5.9) years), 4,143 (85.7%) had sufficient EHR data to calculate the eFI, with 15.1% categorized as frail (eFI > 0.21) and 50.9% pre-frail (0.10 < eFI ≤ 0.21). For all outcomes, there was a generally a gradation of risk with higher eFI scores. For example, adjusting for age, sex, race/ethnicity, and American Society of Anesthesiologists class, and accounting for variability by service line, patients identified as frail based on the eFI, compared to fit patients, had greater needs for post-acute care (odds ratio (OR) = 1.68; 95% confidence interval (CI) = 1.36–2.08), higher rates of 30-day readmission (hazard ratio (HR) = 2.46; 95%CI = 1.72–3.52) and higher all-cause mortality (HR = 2.86; 95%CI = 1.84–4.44) over 6 months’ follow-up. The eFI, an automated digital marker for frailty integrated within the EHR, can facilitate pre-operative frailty screening at scale.
登录
查看更多内容
影响因子：
11.4
作者：
Tyrrell, Daniel J.;Bharadwaj, Manish S.;Jorgensen, Matthew J.;Register, Thomas C.;Molina, Anthony J. A.
通讯作者：
Molina, Anthony J. A.
影响因子：
13.8
作者：
Rothenberg, Kara A.;Stern, Jordan R.;Arya, Shipra
通讯作者：
Arya, Shipra
影响因子：
5.9
作者：
Lansbury, Lynn N.;Roberts, Helen Clare;Sayer, Avan A.
通讯作者：
Sayer, Avan A.
影响因子：
6.7
作者：
Clegg, Andrew;Bates, Chris;Marshall, Tom
通讯作者：
Marshall, Tom
影响因子：
16.9
作者：
Seib, Carolyn D.;Rochefort, Holly;Finlayson, Emily
通讯作者：
Finlayson, Emily