Key result
Frail patients identified by an automated electronic frailty index had higher rates of 30-day readmission (HR 2.46; 95% CI 1.72-3.52) and 6-month mortality (HR 2.86; 95% CI 1.84-4.44) than fit patients.
Why the study?
Current preoperative frailty screening tools require additional data collection or objective assessments, adding expense and limiting large-scale implementation.
Does an automated electronic frailty index (eFI) predict post-operative outcomes in older adults undergoing nonemergency surgery?
Cohort (n=4,831)
No
Does an automated electronic frailty index (eFI) predict post-operative outcomes in older adults undergoing nonemergency surgery?
Hazard Ratio: 2.86 (95% CI 1.84–4.44)
An automated electronic frailty index integrated into the EHR can effectively stratify post-operative risk for older adults undergoing nonemergency surgery.
Electronic frailty index stratifies post-discharge risk in older adults; hypothesis-generating and requires prospective validation before guiding care.
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.
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Callahan et al. (2021) conducted a cohort in Nonemergency surgery (n=4,831). Electronic frailty index (eFI) vs. Fit patients was evaluated on 6-month all-cause mortality (HR 2.86, 95% CI 1.84-4.44). Frail patients identified by an automated electronic frailty index had higher rates of 30-day readmission (HR 2.46; 95% CI 1.72-3.52) and 6-month mortality (HR 2.86; 95% CI 1.84-4.44) than fit patients.
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