Evidence map›Paper›PMID 33469933›Full record

ArticleJournal of the American Geriatrics Society2021

Automated Frailty Screening At-Scale for Pre-Operative Risk Stratification Using the Electronic Frailty Index.

Kathryn E Callahan, Clancy J Clark, Angela F Edwards, Timothy N Harwood, Jeff D Williamson, Adam W Moses, James J Willard, Joseph A Cristiano, Kellice Meadows, Justin Hurie and 3 more

Abstract readEvaluation Study
In one paragraph

Article in Journal of the American Geriatrics Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
–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

38 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Frailty Screening Using the Risk Analysis Index: A User Guide.Joint Commission journal on quality and patient safety · 2025
    Pooled it
  2. Relative impact of multidomain lifestyle interventions on deficit accumulation frailty over 24 months in the U.S. POINTER trial.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Trial
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  6. Review
  7. External validation of an AI-based preoperative frailty index using real-world data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Article
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  17. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Kathryn E CallahanSection on Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.ORCID 0000-0001-6405-3062
Clancy J ClarkCenter for Health Care Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Angela F EdwardsDepartment of Anesthesiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Timothy N HarwoodDepartment of Anesthesiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Jeff D WilliamsonSection on Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Adam W MosesCenter for Health Care Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
James J WillardCenter for Health Care Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Joseph A CristianoSection on General Internal Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Kellice MeadowsDepartment of General Surgery, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Justin HurieSection on Vascular Surgery, Department of General Surgery, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Kevin P HighCenter for Health Care Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
J Wayne MeredithDepartment of General Surgery, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Nicholas M PajewskiCenter for Health Care Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.ORCID 0000-0002-4447-6196

Funding

Wake Forest Claude D. Pepper OAIC - RenewalP30AG021332 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Denise Kathryn Houston · 2002 to 2026
$36.4M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Identifying Frailty in Primary Care: Implementation of an Electronic Medical Record-based Frailty IndexK76AG059986 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI CALLAHAN, KATHRYN · 2018 to 2022
$1.2M
NCATS NIH HHS UL1 TR001420NCATS NIH HHS UL1TR001420NIA NIH HHS K76 AG059986NIA NIH HHS K76-AG059986NIA NIH HHS P30 AG021332NIA NIH HHS P30-AG21332
6 · The paper itself

Abstract

backgroundFrailty is associated with numerous post-operative adverse outcomes in older adults. Current pre-operative frailty screening tools require additional data collection or objective assessments, adding expense and limiting large-scale implementation.

objectiveTo evaluate the association of an automated measure of frailty integrated within the Electronic Health Record (EHR) with post-operative outcomes for nonemergency surgeries.

designRetrospective cohort study.

settingAcademic Medical Center.

participantsPatients 65 years or older that underwent nonemergency surgery with an inpatient stay 24 hours or more between October 8th, 2017 and June 1st, 2019. EXPOSURES: Frailty as measured by a 54-item electronic frailty index (eFI). OUTCOMES AND MEASUREMENTS: Inpatient length of stay, requirements for post-acute care, 30-day readmission, and 6-month all-cause mortality.

resultsOf 4,831 unique patients (2,281 females (47.3%); mean (SD) age, 73.2 (5.9) years), 4,143 (85.7%) had sufficient EHR data to calculate the eFI, with 15.1% categorized as frail (eFI > 0.21) and 50.9% pre-frail (0.10 < eFI ≤ 0.21). For all outcomes, there was a generally a gradation of risk with higher eFI scores. For example, adjusting for age, sex, race/ethnicity, and American Society of Anesthesiologists class, and accounting for variability by service line, patients identified as frail based on the eFI, compared to fit patients, had greater needs for post-acute care (odds ratio (OR) = 1.68; 95% confidence interval (CI) = 1.36-2.08), higher rates of 30-day readmission (hazard ratio (HR) = 2.46; 95%CI = 1.72-3.52) and higher all-cause mortality (HR = 2.86; 95%CI = 1.84-4.44) over 6 months' follow-up.

conclusionsThe eFI, an automated digital marker for frailty integrated within the EHR, can facilitate pre-operative frailty screening at scale.

Indexed as

Health Status IndicatorsAgedAged, 80 and overElectronic Health RecordsFemaleFrail ElderlyFrailtyGeriatric AssessmentHospitalizationHumansMaleMass ScreeningPatient Acceptance of Health CarePatient ReadmissionPostoperative PeriodPreoperative Periodfrailtyhealthcare utilizationpreoperative assessment

Identifiers

PMID33469933
PMCPMC8127394

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.