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Record W4296482276 · doi:10.1097/sla.0000000000005720

Domains of Frailty Predict Loss of Independence in Older Adults After Noncardiac Surgery

2022· article· en· W4296482276 on OpenAlexaboutno aff
Lee A. Goeddel, Zachary C. Murphy, Oluwafemi P. Owodunni, Tina Esfandiary, Demetria Campbell, Joanne Shay, Olive Tang, Karen Bandeen‐Roche, Susan L. Gearhart, Charles H. Brown

Bibliographic record

VenueAnnals of Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institutes of HealthJohns Hopkins University
KeywordsMedicineActivities of daily livingRetrospective cohort studyOdds ratioCohortPhysical therapyGerontologyPediatricsInternal medicine

Abstract

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IMPORTANCE: Preoperative frailty has been consistently associated with death, severe complications, and loss of independence (LOI) after surgery. LOI is an important patient-centered outcome, but it is unclear which domains of frailty are most strongly associated with LOI. Such information would be important to target individual geriatric domains for optimization. OBJECTIVE: To assess whether impairment in individual domains of the Edmonton Frail Scale (EFS) can predict LOI in older adults after noncardiac surgery. DESIGN: Retrospective Cohort Study. SETTING: One Academic Hospital. PARTICIPANTS: Patients aged 65 or older who were living independently and evaluated with the EFS during a preoperative visit to the Center for Preoperative Optimization at the Johns Hopkins Hospital between June 2018 and January 2020. MAIN OUTCOME: LOI defined as discharge to increased level of care outside of the home with new mobility deficit or functional dependence. New mobility deficit and functional dependence were extracted from chart review of the standardized occupational therapy and physical therapy assessment performed before discharge. RESULTS: A total of 3497 patients were analyzed. Age (mean±SD) was 73.4±6.2 years, and 1579 (45.2%) were female. The median total EFS score was 3 (range 0-16), and 725/3497 (27%) were considered frail (EFS≥6). The frequencies of impairment in each EFS domain were functional performance (33.5% moderately impaired, 11% severely impaired), history of hospital readmission (42%), poor self-described health status (37%), and abnormal cognition (17.1% moderately impaired, 13.8% severely impaired). Overall, 235/3497 (6.7%) patients experienced LOI. Total EFS score was associated with LOI (odds ratio: 1.37, 95% CI, 1.30-1.45, P <0.001) in a model adjusted for age, sex, body mass index, American Society of Anesthesiologists rating, congestive heart failure, valvular heart disease, hypertension diagnosis, chronic lung disease, diabetes, renal failure, liver disease, weight loss, anemia, and depression. Using a nested log likelihood approach, the domains of functional performance, functional dependence, social support, health status, and urinary incontinence improved the base multivariable model. In cross-validation, total EFS improved the prediction of LOI with the final model achieving an area under the curve of 0.840. Functional performance was the single domain that most improved outcome prediction, but together with functional dependence, social support, and urinary incontinence, the model resulted in an area under the curve of 0.838. CONCLUSION AND RELEVANCE: Among domains measured by the EFS before a wide range of noncardiac surgeries in older adults, functional performance, functional dependence, social support, and urinary incontinence were independently associated with and improved the prediction of LOI. Clinical initiatives to mitigate LOI may consider screening with the EFS and targeting abnormalities within these domains.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.093
GPT teacher head0.313
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations11
Published2022
Admission routes1
Has abstractyes

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