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Record W2805950212 · doi:10.1371/journal.pone.0198669

Is body mass index associated with outcomes of mechanically ventilated adult patients in intensive critical units? A systematic review and meta-analysis

2018· review· en· W2805950212 on OpenAlexaboutno aff
Yonghua Zhao, Zhiqiang Li, Tao Yang, Meiping Wang

Bibliographic record

VenuePLoS ONE · 2018
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOverweightBody mass indexUnderweightMechanical ventilationOdds ratioObesityMeta-analysisMortality rateInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Obesity paradox refers to lower mortality in subjects with higher body mass index (BMI), and has been documented under a variety of condition. However, whether obesity paradox exists in adults requiring mechanical ventilation in intensive critical units (ICU) remains controversial. METHODS: MEDLINE, EMBASE, China Biology Medicine disc (CBM) and CINAHL electronic databases were searched from the earliest available date to July 2017, using the following search terms: "body weight", "body mass index", "overweight" or "obesity" and "ventilator", "mechanically ventilated", "mechanical ventilation", without language restriction. Subjects were divided into the following categories based on BMI (kg/m2): underweight, < 18.5 kg/m2; normal, 18.5-24.9 kg/m2; overweight, BMI 25-29.9 kg/m2; obese, 30-39.9 kg/m2; and severely obese > 40 kg/m2. The primary outcome was mortality, and included ICU mortality, hospital mortality, short-term mortality (<6 months), and long-term mortality (6 months or beyond). Secondary outcomes included duration of mechanical ventilation, length of stay (LOS) in ICU and hospital. A random-effects model was used for data analyses. Risk of bias was assessed using the Newcastle-Ottawa quality assessment scale. RESULTS: A total of 15,729 articles were screened. The final analysis included 23 articles (199,421 subjects). In comparison to non-obese patients, obese patients had lower ICU mortality (odds ratio (OR) 0.88, 95% CI 0.0.84-0.92, I2 = 0%), hospital mortality (OR 0.83, 95% CI 0.74-0.93, I2 = 52%), short-term mortality (OR 0.81, 95% CI 0.74-0.88, I2 = 0%) as well as long-term mortality (OR 0.69, 95% CI 0.60-0.79, I2 = 0%). In comparison to subjects with normal BMI, obese patients had lower ICU mortality (OR 0.88, 95% CI 0.82-0.93, I2 = 5%). Hospital mortality was lower in severely obese and obese subjects (OR 0.71, 95% CI 0.53-0.94, I2 = 74%, and OR 0.80, 95% CI 0.73-0.89, I2 = 30%). Short-term mortality was lower in overweight and obese subjects (OR 0.82, 95% CI 0.75-0.90, I2 = 0%, and, OR 0.75, 95% CI 0.66-0.84, I2 = 8%, respectively). Long-term mortality was lower in severely obese, obese and overweight subjects (OR 0.39, 95% CI 0.18-0.83, and OR 0.63, 95% CI 0.46-0.86, I2 = 56%, and OR 0.66, 95% CI 0.57-0.77, I2 = 0%). All 4 mortality measures were higher in underweight subjects than in subjects with normal BMI. Obese subjects had significantly longer duration on mechanical ventilation than non-obese group (mean difference (MD) 0.48, 95% CI 0.16-0.80, I2 = 37%), In comparison to subjects with normal BMI, severely obese BMI had significantly longer time in mechanical ventilation (MD 1.10, 95% CI 0.38-1.83, I2 = 47%). Hospital LOS did not differ between obese and non-obese patients (MD 0.05, 95% CI -0.52 to 0.50, I2 = 80%). Obese patients had longer ICU LOS than non-obese patients (MD 0.38, 95% CI 0.17-0.59, I2 = 70%). Hospital LOS and ICU LOS did not differ significantly in subjects with different BMI status. CONCLUSIONS: In ICU patients receiving mechanical ventilation, higher BMI is associated with lower mortality and longer duration on mechanical ventilation.

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)0.010
metaresearch head score (Gemma)0.029
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0190.028
Bibliometrics0.0060.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
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 head0.170
GPT teacher head0.343
Teacher spread0.173 · 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

Citations99
Published2018
Admission routes1
Has abstractyes

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