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Record W2971086845 · doi:10.1213/ane.0000000000004387

How Do Common Comorbidities Modify the Association of Frailty With Survival After Elective Noncardiac Surgery? A Population-Based Cohort Study

2019· article· en· W2971086845 on OpenAlexaffabout
Hui Yin, Carl van Walraven, Daniel I. McIsaac

Bibliographic record

VenueAnesthesia & Analgesia · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsInstitute for Clinical Evaluative SciencesOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineComorbidityHazard ratioDiabetes mellitusCOPDInternal medicineConfidence intervalCohortPopulationCohort studyProportional hazards modelRelative riskGerontologyPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Older people with frailty have decreased postoperative survival. Understanding how comorbidities modify the association between frailty and survival could improve risk stratification and guide development of interventions. Therefore, we evaluated whether the concurrent presence of common and high-risk comorbidities (dementia, chronic obstructive pulmonary disease [COPD], coronary artery disease [CAD], diabetes mellitus, heart failure [HF]) in conjunction with frailty might be associated with a larger decrease in postoperative survival after major elective surgery than would be expected based on the presence of the comorbidity and frailty on their own. METHODS: This cohort study used linked administrative data from Ontario, Canada to identify adults >65 years having elective noncardiac surgery from 2010 to 2015. Frailty was identified using a validated index; comorbidities were identified with validated codes. We evaluated the presence of effect modification (also called interaction) between frailty and each comorbidity on (1) the relative (or multiplicative) scale by assessing whether the risk of mortality when both frailty and the comorbidity were present was different than the product of the risks associated with each condition; and (2) the absolute risk difference (or additive) scale by assessing whether the risk of mortality when both frailty and the comorbidity were present was greater than the sum of the risks associated with each condition. RESULTS: 11,150 (9.7%) people with frailty died versus 7826 (2.8%) without frailty. After adjustment, frailty was associated with decreased survival (adjusted hazard ratio [HR] = 2.42; 95% confidence interval [CI], 2.31-2.54). On the relative (multiplicative) scale, only diabetes mellitus demonstrated significant effect modification (P value for interaction .03; reduced risk together). On the absolute risk difference (additive) scale, all comorbidities except for coronary disease demonstrated effect modification of the association of frailty with survival. Co-occurrence of dementia with frailty carried the greatest excess risk (Synergy Index [S; the excess risk from exposure to both risk factors compared to the sum of the risks from each factor in isolation] = 2.29; 95% CI, 1.32-10.80, the excess risk from exposure to both risk factors compared to the sum of the risks from each factor in isolation). CONCLUSIONS: Common comorbidities modify the association of frailty with postoperative survival; however, this effect was only apparent when analyses accounted for effect modification on the absolute risk difference, as opposed to relative scale. While the relative scale is more commonly used in biomedical research, smaller effects may be easier to detect on the risk difference scale. The concurrent presence of dementia, COPD, and HF with frailty were all associated with excess mortality on the absolute risk difference scale.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.330
Threshold uncertainty score0.655

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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".

Quick stats

Citations19
Published2019
Admission routes2
Has abstractyes

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