Automated Electronic Frailty Index–Identified Frailty Status and Associated Postsurgical Adverse Events

Author:

Khanna Ashish K.123,Motamedi Vida134,Bouldin Bethany13,Harwood Timothy1,Pajewski Nicholas M.5,Saha Amit K.12,Segal Scott12

Affiliation:

1. Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina

2. Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, North Carolina

3. Outcomes Research Consortium, Cleveland, Ohio

4. Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee

5. Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina

Abstract

ImportanceElectronic frailty index (eFI) is an automated electronic health record (EHR)–based tool that uses a combination of clinical encounters, diagnosis codes, laboratory workups, medications, and Medicare annual wellness visit data as markers of frailty status. The association of eFI with postanesthesia adverse outcomes has not been evaluated.ObjectiveTo examine the association of frailty, calculated as eFI at the time of the surgical procedure and categorized as fit, prefrail, or frail, with adverse events after elective noncardiac surgery.Design, Setting, and ParticipantsThis cohort study was conducted at a tertiary care academic medical center in Winston-Salem, North Carolina. The cohort included patients 55 years or older who underwent noncardiac surgery of at least 1 hour in duration between October 1, 2017, and June 30, 2021.ExposureFrailty calculated by the eFI tool. Preoperative eFI scores were calculated based on available data 1 day prior to the procedure and categorized as fit (eFI score: ≤0.10), prefrail (eFI score: >0.10 to ≤0.21), or frail (eFI score: >0.21).Main Outcomes and MeasuresThe primary outcome was a composite of the following 8 adverse component events: 90-item Patient Safety Indicators (PSI 90) score, hospital-acquired conditions, in-hospital mortality, 30-day mortality, 30-day readmission, 30-day emergency department visit after surgery, transfer to a skilled nursing facility after surgery, or unexpected intensive care unit admission after surgery. Secondary outcomes were each of the component events of the composite.ResultsOf the 33 449 patients (median [IQR] age, 67 [61-74] years; 17 618 females [52.7%]) included, 11 563 (34.6%) were classified as fit, 15 928 (47.6%) as prefrail, and 5958 (17.8%) as frail. Using logistic regression models that were adjusted for age, sex, race and ethnicity, and comorbidity burden, patients with prefrail (odds ratio [OR], 1.24; 95% CI, 1.18-1.30; P < .001) and frail (OR, 1.71; 95% CI, 1.58-1.82; P < .001) statuses were more likely to experience postoperative adverse events compared with patients with a fit status. Subsequent adjustment for all other potential confounders or covariates did not alter this association. For every increase in eFI of 0.03 units, the odds of a composite of postoperative adverse events increased by 1.06 (95% CI, 1.03-1.13; P < .001).Conclusions and RelevanceThis cohort study found that frailty, as measured by an automatically calculated index integrated within the EHR, was associated with increased risk of adverse events after noncardiac surgery. Deployment of eFI tools may support screening and possible risk modification, especially in patients who undergo high-risk surgery.

Publisher

American Medical Association (AMA)

Subject

General Medicine

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