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6: ASSOCIATION BETWEEN OPIOID ADMINISTRATION DURING MECHANICAL VENTILATION AND SUBSEQUENT OPIOID USE

2023· article· en· W4389743108 on OpenAlexaff
Justin M. Rucci, Laura C. Myers, Nicholas A. Bosch, Lauren Soltesz, S. Reza Jafarzadeh, Ycar Devis, Cynthia I. Campbell, Jennifer P. Stevens, Hannah Wunsch, Vincent Liu, Allan J. Walkey

Bibliographic record

VenueCritical Care Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineOpioidAdministration (probate law)Mechanical ventilationAnesthesiaPsychoanalysisInternal medicineLaw

Abstract

fetched live from OpenAlex

Introduction: An analgesia-first sedation strategy during mechanical ventilation (MV) is a guideline-based intensive care unit (ICU) practice, resulting in many critically ill patients receiving opioids. Healthcare opioid exposure in non-ICU settings is associated with subsequent outpatient opioid use, but the long-term impact of opioid administration during MV is uncharacterized. We hypothesized that higher opioid doses during MV would result in increased opioid use after hospital discharge. Methods: Our retrospective cohort study included adult patients in 21 Kaiser Permanente Northern California medical ICUs (2012-2019) who received MV ≥24 hours for acute respiratory failure, survived to discharge, and did not have prior comfort-focused care/hospice referral. The primary exposure was tercile of median daily fentanyl equivalents (MDFE) administered during the first 21 MV days. Patients who did not receive opioids were the reference group. The primary outcome was a filled opioid prescription (Rx) in the year after discharge modeled using a time to event analysis with death as a competing risk. Secondary outcomes included 1) an opioid Rx filled in 30 days, 2) an opioid Rx filled in 1 year and 3) persistent opioid use over 1 year, all modeled as binary outcomes using logistic regression. Models were adjusted for patient demographics; social determinants of health; Charlson comorbidities; opioid Rx, chronic pain, or opioid-related diagnoses in the prior year; principal diagnosis; length of stay; and code status. Results: We included 6,746 patients; 2,942 (43.6%) experienced the primary outcome. The MDFE distribution was a median 200µcg (Interquartile range 40µcg, 1000µcg) with Tercile 1 0-67µcg, Tercile 2 >67-700µcg and Tercile 3 >700µcg. Compared to patients who received no opioid, higher MDFE was associated with more opioid Rxs in the year after discharge: Tercile 1 Hazard Ratio (HR) 1.01 (95% Confidence Interval 0.87-1.18), Tercile 2 HR 1.23 (1.06-1.43), Tercile 3 HR 1.31 (1.13-1.53). Results were similar for secondary outcomes. Conclusions: Administering higher opioid doses during MV is associated with increased post-discharge opioid use, including persistent use. Given the harms of the ongoing opioid epidemic, future studies should evaluate the risks and benefits of opioid-sparing strategies during MV.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.032
GPT teacher head0.337
Teacher spread0.305 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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