Automated Frailty Screening At-Scale for Pre-Operative Risk Stratification Using the Electronic Frailty Index

被引:48
作者
Callahan, Kathryn E. [1 ,2 ]
Clark, Clancy J. [2 ,3 ]
Edwards, Angela F. [4 ]
Harwood, Timothy N. [4 ]
Williamson, Jeff D. [1 ,2 ]
Moses, Adam W. [2 ,5 ]
Willard, James J. [2 ,6 ]
Cristiano, Joseph A. [5 ]
Meadows, Kellice [7 ]
Hurie, Justin [8 ]
High, Kevin P. [2 ,9 ]
Meredith, J. Wayne [7 ]
Pajewski, Nicholas M. [2 ,6 ]
机构
[1] Wake Forest Sch Med, Sect Gerontol & Geriatr Med, Dept Internal Med, Winston Salem, NC 27157 USA
[2] Wake Forest Sch Med, Ctr Hlth Care Innovat, Winston Salem, NC 27157 USA
[3] Wake Forest Sch Med, Sect Surg Oncol, Dept Gen Surg, Winston Salem, NC 27101 USA
[4] Wake Forest Sch Med, Dept Anesthesiol, Winston Salem, NC 27157 USA
[5] Wake Forest Sch Med, Dept Internal Med, Sect Gen Internal Med, Winston Salem, NC 27157 USA
[6] Wake Forest Sch Med, Dept Biostat & Data Sci, Div Publ Hlth Sci, Winston Salem, NC 27157 USA
[7] Wake Forest Sch Med, Dept Gen Surg, Winston Salem, NC 27157 USA
[8] Wake Forest Sch Med, Dept Gen Surg, Sect Vasc Surg, Winston Salem, NC 27157 USA
[9] Wake Forest Sch Med, Dept Internal Med, Sect Infect Dis, Winston Salem, NC 27157 USA
基金
美国国家卫生研究院;
关键词
frailty; preoperative assessment; healthcare utilization;
D O I
10.1111/jgs.17027
中图分类号
R592 [老年病学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 100203 ;
摘要
BACKGROUND: 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. OBJECTIVE: To evaluate the association of an automated measure of frailty integrated within the Electronic Health Record (EHR) with post-operative outcomes for nonemergency surgeries. DESIGN: Retrospective cohort study. SETTING: Academic Medical Center. PARTICIPANTS: 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. EXPOSURES: Frailty as measured by a 54-item electronic frailty index (eFI). OUTCOMES AND MEASUREMENTS: Inpatient length of stay, requirements for post-acute care, 30-day readmission, and 6-month all-cause mortality. RESULTS: 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. CONCLUSIONS: The eFI, an automated digital marker for frailty integrated within the EHR, can facilitate pre-operative frailty screening at scale.
引用
收藏
页码:1357 / 1362
页数:6
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