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Mechanical Ventilation after Traumatic Spinal Cord Injury—A Multicentric Cohort Study-based Prediction Model for Weaning Success: The BICYCLE Score

2023· article· en· W4378373255 on OpenAlexaffabout
Annia Schreiber, Jacopo Garlasco, Martin Urner, Amanda McFarlan, Andrew Baker, Andrea Rigamonti, Jeffrey M. Singh, Demetrios J. Kutsogiannis, Laurent Brochard

Bibliographic record

VenueAnnals of the American Thoracic Society · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoUniversity of AlbertaSt. Michael's HospitalUniversity Health NetworkInstitute of Health Services and Policy Research
Fundersnot available
KeywordsMedicineSpinal cord injuryMechanical ventilationCohortCohort studyVentilation (architecture)WeaningPhysical medicine and rehabilitationAnesthesiaEmergency medicineSpinal cordInternal medicine

Abstract

fetched live from OpenAlex

Abstract Rationale Limited information exists about the epidemiology, outcomes, and predictors of weaning from mechanical ventilation in patients with spinal cord injury. Objectives Our aim was to investigate predictors of weaning outcomes for patients with traumatic spinal cord injury (tSCI) and develop and validate a prognostic model and score for weaning success. Methods This was a registry-based, multicentric cohort study including all adult patients with tSCI requiring mechanical ventilation (MV) and admitted to one of the intensive care units (ICUs) of the Trauma Registry at St. Michael’s Hospital (Toronto, ON, Canada) and the Canadian Rick Hansen Spinal Cord Injury Registry between 2005 and 2019. The primary outcome was weaning success from MV at ICU discharge. Secondary outcomes included weaning success at Days 14 and 28, time to liberation from MV accounting for competing risk of death, and ventilator-free days at 28 and 60 days. Associations between baseline characteristics and weaning success or time to liberation from MV were measured using multivariable logistic and competing risk regressions. A parsimonious model to predict weaning success and ICU discharge was developed and validated via bootstrap. A prediction score for weaning success at ICU discharge was derived, and its discriminative ability was assessed using receiver operating characteristic curve analysis and compared with the Injury Severity Score (ISS). Results Of 459 patients analyzed, 246 (53.6%), 302 (65.8%), and 331 (72.1%) were alive and free of MV at Day 14, Day 28, and ICU discharge, respectively; 54 (11.8%) died in the ICU. Median time to liberation from MV was 12 days. Factors associated with weaning success were Blunt injury (odds ratio [OR], 2.96; P = 0.010), ISS (OR, 0.98; P = 0.025), Complete syndrome (OR, 0.53; P = 0.009), age in Years (OR, 0.98; P = 0.003), and Cervical LEsion (OR, 0.60; P = 0.045). The BICYCLE score showed a greater area under the curve than the ISS (0.689 [95% confidence interval (CI), 0.631–0.743] vs. 0.537 [95% CI, 0.479–0.595]; P < 0.0001). Factors predicting weaning success also predicted time to liberation. Conclusions In a large multicentric cohort, 72% of patients with tSCI were weaned and discharged alive from the ICU. Readily available admission characteristics can reasonably predict weaning success and help prognostication.

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)0.009
metaresearch head score (Gemma)0.011
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 score0.022
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
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 head0.185
GPT teacher head0.484
Teacher spread0.299 · 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".

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Citations7
Published2023
Admission routes2
Has abstractyes

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