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Record W7117259933 · doi:10.1002/alz70857_097144

Assessing the association between cognitive impairment and perioperative trajectory of frailty in older adults undergoing anesthesia and surgery: a multicentred cohort study

2025· article· en· W7117259933 on OpenAlexaffabout
Eric Ka J Cheuk, Ellene Yan, Yasmin Alhamdah, Sinead Campbell, David He, Frances Chung

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsMount Sinai HospitalToronto Western HospitalWomen's College HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPerioperativeCognitive impairmentCohortCohort studyAssociation (psychology)CognitionTrajectory

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive impairment (CI) and frailty can lead to increased vulnerability and reduced capacity to cope with anesthesia and surgery. Understanding the trajectory of frailty in those with versus without CI can help enhance perioperative care. This study aimed to (1) compare the prevalence and trajectory of frailty between patients with and without CI following surgery, (2) investigate patient-centered variables associated with preoperative frailty, and (3) examine clinical outcomes by baseline frailty. METHOD: Patients ≥65 years old undergoing elective non-cardiac surgery provided informed consent following ethics board approval. Preoperative CI was assessed using the Ascertain Dementia Eight-item Questionnaire (AD8) (≥2), Mini-Cog (≤2), and Montreal Cognitive Assessment (MoCA) (≤25). Frailty was measured by the 5-item FRAIL Questionnaire (prefrail: 1-2; frail: ≥3) preoperatively and at 30 and 90 days. Uni- and multivariable logistic regression was performed to determine patient-centered variables associated with preoperative frailty. Clinical outcomes at 30 and 90 days were collected from electronic medical records. RESULT: Of 381 participants, 13.6%, 12.6%, and 34.1% screened positive for CI on the AD8, Mini-Cog, and MoCA, respectively. Among the same assessments, 29.1%, 12.7%, and 40.0% CI patients were also frail preoperatively. In contrast to the Mini-Cog, patients with CI on the AD8 or MoCA were significantly frailer than those without CI at all timepoints (Figure 1). Regardless of cognitive assessment tool, frailty improved across all timepoints for both CI and no-CI patients, with both groups exhibiting a similar rate of improvement (Figure 1). In the multivariable analysis, females (3-fold), orthopedic surgery (6-fold), significant preoperative functional disability (5-fold), and high risk of obstructive sleep apnea (4-fold) were associated with higher odds of preoperative frailty, after adjusting for American Society of Anesthesiologists (ASA) class and pain (Table 1). Both frail and prefrail patients had significantly higher incidences of non-home discharge at 30 days, all-cause complications at 90 days, and composite adverse outcomes at 90 days (Table 2). CONCLUSION: Our findings demonstrated greater prevalence of frailty in CI patients, with frail and prefrail patients experiencing poorer postsurgical outcomes. Early detection of frailty and CI can inform tailored perioperative management strategies to enhance recovery, optimize patient-centered care and clinical outcomes.

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)0.004
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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.024
GPT teacher head0.309
Teacher spread0.286 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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