Evidence map›Paper›PMID 35293969›Full record

ArticleJAMA surgery2022

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

Sidney T Le, Vincent X Liu, Patricia Kipnis, Jie Zhang, Peter D Peng, Elizabeth M Cespedes Feliciano

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. FI-CGA and eFI-CGA in Frailty Care: a Scoping Review.Canadian geriatrics journal : CGJ · 2026
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Sidney T LeDivision of Research, Kaiser Permanente Northern California, Oakland.
Vincent X LiuDivision of Research, Kaiser Permanente Northern California, Oakland.
Patricia KipnisDivision of Research, Kaiser Permanente Northern California, Oakland.
Jie ZhangDivision of Research, Kaiser Permanente Northern California, Oakland.
Peter D PengThe Permanente Medical Group, Oakland, California.
Elizabeth M Cespedes FelicianoDivision of Research, Kaiser Permanente Northern California, Oakland.

Funding

Identifying pre-sepsis opportunities for early, targeted intervention - Equipment SupplementR35GM128672 · NIGMS · KAISER FOUNDATION RESEARCH INSTITUTE · PI Vincent Liu · 2018 to 2026
$4.3M
Informatics approaches to assessing patient frailty in surgical careR01AG065334 · NIA · KAISER FOUNDATION RESEARCH INSTITUTE · PI FELICIANO, ELIZABETH MARJORIE CESPEDES · 2020 to 2024
$3.0M
NIA NIH HHS R01 AG065334NIGMS NIH HHS R35 GM128672
6 · The paper itself

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.

Indexed as

FrailtyAdultAgedCohort StudiesElectronicsFemaleFrail ElderlyHumansPostoperative ComplicationsRetrospective StudiesRisk AssessmentRisk Factors

Identifiers

PMID35293969
PMCPMC8928095

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.