Automated Frailty Screening At-Scale for Pre-Operative Risk Stratification Using the Electronic Frailty Index
| Autor: | Joseph A. Cristiano, J. Wayne Meredith, Clancy J. Clark, Angela F. Edwards, Justin B. Hurie, Kellice Meadows, Timothy N. Harwood, James J Willard, Nicholas M. Pajewski, Kevin P. High, Jeff D Williamson, Adam Moses, Kathryn E. Callahan |
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| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Frail Elderly Frailty Index Patient Readmission Risk Assessment Article 03 medical and health sciences 0302 clinical medicine Risk Factors Medicine Electronic Health Records Health Status Indicators Humans Mass Screening 030212 general & internal medicine Postoperative Period Geriatric Assessment Aged Proportional Hazards Models Retrospective Studies Aged 80 and over Frailty business.industry Hazard ratio Retrospective cohort study Odds ratio Patient Acceptance of Health Care Pre operative Confidence interval Hospitalization Systems Integration 030220 oncology & carcinogenesis Scale (social sciences) Emergency medicine Risk stratification Preoperative Period Female Geriatrics and Gerontology business |
| Zdroj: | J Am Geriatr Soc |
| ISSN: | 1532-5415 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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