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Inspiratory Muscle Rehabilitation in Critically Ill Adults. A Systematic Review and Meta-Analysis

2018· review· en· W2794733568 on OpenAlexafffund
Stefannie Vorona, Umberto Sabatini, Sulaiman Al-Maqbali, Michele Bertoni, Martin Dres, Bernie Bissett, Frank van Haren, A. Daniel Martin, Cristian Urrea, Debbie Brace, Matteo Parotto, Margaret S. Herridge, Neill K. J. Adhikari, Eddy Fan, Luana Torres Monteiro Melo, W. Darlene Reid, Laurent Brochard, Niall D. Ferguson, Ewan C. Goligher

Bibliographic record

VenueAnnals of the American Thoracic Society · 2018
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreHealth Sciences CentreUniversity Health NetworkSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineInterquartile rangeIntensive care unitMechanical ventilationMeta-analysisRandomized controlled trialPhysical therapyCINAHLConfidence intervalIntensive careIntensive care medicineInternal medicinePsychological intervention

Abstract

fetched live from OpenAlex

Abstract Rationale Respiratory muscle weakness is common in critically ill patients; the role of targeted inspiratory muscle training (IMT) in intensive care unit rehabilitation strategies remains poorly defined. Objectives The primary objective of the present study was to describe the range and tolerability of published methods for IMT. The secondary objectives were to determine whether IMT improves respiratory muscle strength and clinical outcomes in critically ill patients. Methods We conducted a systematic review to identify randomized and nonrandomized studies of physical rehabilitation interventions intended to strengthen the respiratory muscles in critically ill adults. We searched the MEDLINE, Embase, HealthSTAR, CINAHL, and CENTRAL databases (inception to September Week 3, 2017) and conference proceedings (2012 to 2017). Data were independently extracted by two authors and collected on a standardized report form. Results A total of 28 studies (N = 1,185 patients) were included. IMT was initiated during early mechanical ventilation (8 studies), after patients proved difficult to wean (14 studies), or after extubation (3 studies), and 3 other studies did not report exact timing. Threshold loading was the most common technique; 13 studies employed strength training regimens, 11 studies employed endurance training regimens, and 4 could not be classified. IMT was feasible, and there were few adverse events during IMT sessions (nine studies; median, 0%; interquartile range, 0–0%). In randomized trials (n = 20), IMT improved maximal inspiratory pressure compared with control (15 trials; mean increase, 6 cm H2O; 95% confidence interval [CI], 5–8 cm H2O; pooled relative ratio of means, 1.19; 95% CI, 1.14–1.25) and maximal expiratory pressure (4 trials; mean increase, 9 cm H2O; 95% CI, 5–14 cm H2O). IMT was associated with a shorter duration of ventilation (nine trials; mean difference, 4.1 d; 95% CI, 0.8–7.4 d) and a shorter duration of weaning (eight trials; mean difference, 2.3 d; 95% CI, 0.7–4.0 d), but confidence in these pooled estimates was low owing to methodological limitations, including substantial statistical and methodological heterogeneity. Conclusions Most studies of IMT in critically ill patients have employed inspiratory threshold loading. IMT is feasible and well tolerated in critically ill patients and improves both inspiratory and expiratory muscle strength. The impact of IMT on clinical outcomes requires future confirmation.

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)0.026
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 score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0190.024
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.132
GPT teacher head0.450
Teacher spread0.318 · 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

Citations192
Published2018
Admission routes2
Has abstractyes

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