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 Participants: This 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 Measures: Pearson 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. Results: Within 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 Relevance: In 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.
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 Participants: This 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 Measures: Pearson 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. Results: Within 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 Relevance: In 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.
Authors: Daniel E Hall; Shipra Arya; Kendra K Schmid; Casey Blaser; Mark A Carlson; Travis L Bailey; Georgia Purviance; Tammy Bockman; Thomas G Lynch; Jason Johanning Journal: JAMA Surg Date: 2017-02-01 Impact factor: 14.766
Authors: Shipra Arya; Patrick Varley; Ada Youk; Jeffrey D Borrebach; Sebastian Perez; Nader N Massarweh; Jason M Johanning; Daniel E Hall Journal: Ann Surg Date: 2020-12 Impact factor: 12.969
Authors: E Dent; J E Morley; A J Cruz-Jentoft; L Woodhouse; L Rodríguez-Mañas; L P Fried; J Woo; I Aprahamian; A Sanford; J Lundy; F Landi; J Beilby; F C Martin; J M Bauer; L Ferrucci; R A Merchant; B Dong; H Arai; E O Hoogendijk; C W Won; A Abbatecola; T Cederholm; T Strandberg; L M Gutiérrez Robledo; L Flicker; S Bhasin; M Aubertin-Leheudre; H A Bischoff-Ferrari; J M Guralnik; J Muscedere; M Pahor; J Ruiz; A M Negm; J Y Reginster; D L Waters; B Vellas Journal: J Nutr Health Aging Date: 2019 Impact factor: 4.075