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Automated Frailty Screening At‐Scale for Pre‐Operative Risk Stratification Using the Electronic Frailty Index
Author(s) -
Callahan Kathryn E.,
Clark Clancy J.,
Edwards Angela F.,
Harwood Timothy N.,
Williamson Jeff D.,
Moses Adam W.,
Willard James J.,
Cristiano Joseph A.,
Meadows Kellice,
Hurie Justin,
High Kevin P.,
Meredith J. Wayne,
Pajewski Nicholas M.
Publication year - 2021
Publication title -
journal of the american geriatrics society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.992
H-Index - 232
eISSN - 1532-5415
pISSN - 0002-8614
DOI - 10.1111/jgs.17027
Subject(s) - medicine , hazard ratio , confidence interval , frailty index , odds ratio , retrospective cohort study , emergency medicine , cohort , gerontology
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.