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Record W4387964894 · doi:10.1186/s13741-023-00345-9

The impact of preoperative malnutrition on postoperative delirium: a systematic review and meta-analysis

2023· review· en· W4387964894 on OpenAlexaboutno aff
Bo Dong, Jing Wang, Li Pan, Jianli Li, Meinv Liu, Huanhuan Zhang

Bibliographic record

VenuePerioperative Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
FundersHebei Provincial Key Research Projects
KeywordsMalnutritionMedicineMeta-analysisDeliriumCochrane LibraryOdds ratioSubgroup analysisProspective cohort studyAnthropometryCohort studyInternal medicineSurgeryIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Postoperative delirium (POD) is a common postoperative complication, characterized by disturbance of attention, perception, and consciousness within 1 week after surgery, and linked to cognitive decline, increased mortality, and other serious surgical outcomes. Early identification and treatment of risk factors for POD could reduce the occurrence of delirium and the related poor outcomes. Malnutrition as a possible precipitating factor, defined as the poor anthropometric, functional, and clinical outcomes of nutrient deficiency, has been investigated. However, the evidence is controversial. The goal of this systematic review and meta-analysis was to comprehensively assess the correlation between preoperative malnutrition and POD. METHODS: PubMed, Embase, Cochrane Library, and Web of Science were used to search prospective cohort articles that explored the correlation between preoperative malnutrition and POD from inception until September 30, 2022. Two researchers independently conducted the literature selection and data extraction. The quality of the literature was evaluated according to the Newcastle-Ottawa scale (NOS). Odds ratios (ORs) and 95% confidence intervals (CIs) for POD associated with malnutrition relative to normal nutritional status were calculated. RESULTS: Seven prospective cohort studies qualified for the meta-analysis, which included 2701 patients. The pooled prevalence of preoperative malnutrition was 15.1% (408/2701), and POD occurred in 428 patients (15.8%). The NOS score was above 7 points in all 7 studies. Our results demonstrated that the pooled OR for malnutrition and POD was 2.32 (95% CI 1.62-3.32) based on a random-effects model. Our subgroup analysis suggested that the relationship between malnutrition and POD was significant in adults following noncardiac surgery (OR = 3.04, 95% CI, 1.99-4.62, P < 0.001), while there was no statistical significance in adults following cardiac surgery (OR = 1.76, 95% CI, 0.96-3.22, P = 0.07). Additionally, in the subgroup analysis based on different malnutrition assessment tools (MNA-SF versus others), a significant association was found in the MNA-SF group (OR = 3.04, 95% CI, 1.99-4.62, P < 0.001), but not in the others group (OR = 1.76, 95% CI, 0.96-3.22, P = 0.07). Other subgroup analyses showed that this association was not significantly affected by evaluation instruments for POD, location of the study, or quality of the article (all P < 0.05). CONCLUSIONS: Based on the currently available evidence, our results suggested that preoperative malnutrition was independently associated with POD in adult surgical patients.

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)0.012
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.030
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.038
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.126
GPT teacher head0.433
Teacher spread0.306 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations34
Published2023
Admission routes1
Has abstractyes

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