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Perioperative Factors Associated With Postoperative Delirium in Patients Undergoing Noncardiac Surgery

2023· review· en· W4387527926 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJAMA Network Open · 2023
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of OttawaWestern UniversityToronto Western HospitalUniversity of TorontoMcMaster UniversityUniversity of CalgaryImpact
FundersAustralian and New Zealand College of AnaesthetistsUniversity of OxfordEIT HealthCanadian Institutes of Health ResearchU.S. Department of Veterans Affairs
KeywordsMedicinePerioperativeDeliriumOdds ratioLogistic regressionCINAHLIncidence (geometry)Meta-analysisMEDLINEInternal medicineSurgeryEmergency medicineIntensive care medicinePsychological interventionPsychiatry

Abstract

fetched live from OpenAlex

Importance: Postoperative delirium (POD) is a common and serious complication after surgery. Various predisposing factors are associated with POD, but their magnitude and importance using an individual patient data (IPD) meta-analysis have not been assessed. Objective: To identify perioperative factors associated with POD and assess their relative prognostic value among adults undergoing noncardiac surgery. Data Sources: MEDLINE, EMBASE, and CINAHL from inception to May 2020. Study Selection: Studies were included that (1) enrolled adult patients undergoing noncardiac surgery, (2) assessed perioperative risk factors for POD, and (3) measured the incidence of delirium (measured using a validated approach). Data were analyzed in 2020. Data Extraction and Synthesis: Individual patient data were pooled from 21 studies and 1-stage meta-analysis was performed using multilevel mixed-effects logistic regression after a multivariable imputation via chained equations model to impute missing data. Main Outcomes and Measures: The end point of interest was POD diagnosed up to 10 days after a procedure. A wide range of perioperative risk factors was considered as potentially associated with POD. Results: A total of 192 studies met the eligibility criteria, and IPD were acquired from 21 studies that enrolled 8382 patients. Almost 1 in 5 patients developed POD (18%), and an increased risk of POD was associated with American Society of Anesthesiologists (ASA) status 4 (odds ratio [OR], 2.43; 95% CI, 1.42-4.14), older age (OR for 65-85 years, 2.67; 95% CI, 2.16-3.29; OR for >85 years, 6.24; 95% CI, 4.65-8.37), low body mass index (OR for body mass index <18.5, 2.25; 95% CI, 1.64-3.09), history of delirium (OR, 3.9; 95% CI, 2.69-5.66), preoperative cognitive impairment (OR, 3.99; 95% CI, 2.94-5.43), and preoperative C-reactive protein levels (OR for 5-10 mg/dL, 2.35; 95% CI, 1.59-3.50; OR for >10 mg/dL, 3.56; 95% CI, 2.46-5.17). Completing a college degree or higher was associated with a decreased likelihood of developing POD (OR 0.45; 95% CI, 0.28-0.72). Conclusions and Relevance: In this systematic review and meta-analysis of individual patient data, several important factors associated with POD were found that may help identify patients at high risk and may have utility in clinical practice to inform patients and caregivers about the expected risk of developing delirium after surgery. Future studies should explore strategies to reduce delirium after surgery.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.442
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.338
Teacher spread0.256 · 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