Recommendations for preoperative management of frailty from the Society for Perioperative Assessment and Quality Improvement (SPAQI)

Álvarez-Nebreda, M.L.; Bentov, Nathalie; Urman, Richard D.; Setia, Sabeena; Huang, Joe C.; Pfeifer, Kurt; Bennett, Katherine A.; Ong, Thuan D.; Richman, Deborah C.; Gollapudi, Divya; Rooke, G. Alec; Javedan, Houman

doi:10.1016/j.pcorm.2017.11.011

Cited by 4 publications

(1 citation statement)

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“…railty is a state of decreased physiologic reserve and multiple system impairments that increases vulnerability to stressors, such as surgery. National institutions and specialty societies have strongly recommended preoperative assessment for frailty as a critical component of surgical care, [1][2][3][4][5] as frailty is a powerful determinant of complications, nonhome discharge, and new disability. 6-10 However, implementation of frailty assessments has been limited, in part owing to time-consuming frailty assessment methods that require direct clinician evaluation.…”

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confidence: 99%

Comparison of Electronic Frailty Metrics for Prediction of Adverse Outcomes of Abdominal Surgery

Liu

Kipnis

et al. 2022

JAMA Surg

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IMPORTANCE Electronic frailty metrics have been developed for automated frailty assessment and include the Hospital Frailty Risk Score (HFRS), the Electronic Frailty Index (eFI), the 5-Factor Modified Frailty Index (mFI-5), and the Risk Analysis Index (RAI). Despite substantial differences in their construction, these 4 electronic frailty metrics have not been rigorously compared within a surgical population.OBJECTIVE To characterize the associations between 4 electronic frailty metrics and to measure their predictive value for adverse surgical outcomes. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study used electronic health record data from patients who underwent abdominal surgery from January 1, 2010, to December 31, 2020, at 20 medical centers within Kaiser Permanente Northern California (KPNC). Participants included adults older than 50 years who underwent abdominal surgical procedures at KPNC from 2010 to 2020 that were sampled for reporting to the National Surgical Quality Improvement Program. MAIN OUTCOMES AND MEASURESPearson correlation coefficients between electronic frailty metrics and area under the receiver operating characteristic curve (AUROC) of univariate models and multivariate preoperative risk models for 30-day mortality, readmission, and morbidity, which was defined as a composite of mortality and major postoperative complications. RESULTSWithin the cohort of 37 186 patients, mean (SD) age, 67.9 (female, 19 127 [51.4%]), correlations between pairs of metrics ranged from 0.19 (95% CI, 0.18-0.20) for mFI-5 and RAI 0.69 (95% CI, 0.68-0.70). Only 1085 of 37 186 (2.9%) were classified as frail based on all 4 metrics. In univariate models for morbidity, HFRS demonstrated higher predictive discrimination (AUROC, 0.71; 95% CI, 0.70-0.72) than eFI (AUROC, 0.64; 95% CI, 0.63-0.65), mFI-5 (AUROC, 0.58; 95% CI, 0.57-0.59), and RAI (AUROC, 0.57; 95% CI, 0.57-0.58). The predictive discrimination of multivariate models with age, sex, comorbidity burden, and procedure characteristics for all 3 adverse surgical outcomes improved by including HFRS into the models. CONCLUSIONS AND RELEVANCEIn this cohort study, the 4 electronic frailty metrics demonstrated heterogeneous correlation and classified distinct groups of surgical patients as frail. However, HFRS demonstrated the highest predictive value for adverse surgical outcomes.

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confidence: 99%