ArticleJAMA surgery2022
Comparison of Electronic Frailty Metrics for Prediction of Adverse Outcomes of Abdominal Surgery.
Article in JAMA surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Comparison of Electronic Health Data-Based Frailty Assessment Tools for Prediction of Adverse Outcomes of Patients With Chronic Kidney Disease.Nephrology (Carlton, Vic.) · 2026Article
- FI-CGA and eFI-CGA in Frailty Care: a Scoping Review.Canadian geriatrics journal : CGJ · 2026Review
- Age, Frailty, and Comorbidity as Predictors of Mortality and Failure to Rescue After Gastrointestinal Cancer Surgery: A National Retrospective Cohort Study.World journal of surgery · 2026Article
- Comparing Community-Level Social Determinants of Health With Patient Race in Total Hip Arthroplasty Outcomes.Arthritis care & research · 2025Article
- Inconsistent Associations of Modified Frailty Index-5 With Adverse Head and Neck Reconstruction Outcomes.The Laryngoscope · 2025Article
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- Accuracy of frailty instruments in predicting outcomes following perioperative cardiac arrest.Resuscitation · 2024Article
- Impact of frailty on perioperative outcomes following percutaneous nephrolithotomy in older persons: evidence from the US Nationwide Inpatient Sample.Urolithiasis · 2024Article
- Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and Current Procedural Terminology set embeddings.British journal of anaesthesia · 2024Article
- Hospice and palliative care utilization in 16 004 232 medicare claims: comparing trauma to surgical and medical inpatients.Trauma surgery & acute care open · 2024Article
- Training the next generation of delivery science researchers: 10-year experience of a post-doctoral research fellowship program within an integrated care system.Learning health systems · 2024Article
- Automated Electronic Frailty Index-Identified Frailty Status and Associated Postsurgical Adverse Events.JAMA network open · 2023Article
- Preoperative Frailty Risk in Cranioplasty Patients: Risk Analysis Index Predicts Adverse Outcomes.Plastic and reconstructive surgery. Global open · 2023Article
- Validating biologic age in selecting elderly patients with pancreatic cancer for surgical resection.Journal of surgical oncology · 2023Article
- Quantifying Frailty Requires a Conceptual Model Before a Statistical Model.JAMA surgery · 2022Article
- Identifying Frail Patients by Using Electronic Health Records in Primary Care: Current Status and Future Directions.Frontiers in public health · 2022Review
- A systematic review of predictive accuracy via c-statistic of preoperative frailty tests for extended length of stay, post-operative complications, and mortality.Saudi journal of anaesthesiaReview
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6 authors.
Funding
Abstract
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.
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