MétaCan
Menu
Back to cohort
Record W4415700082 · doi:10.1213/ane.0000000000007821

Variation in Maximal Doses of Vasopressors Used for Treatment of Shock in Intensive Care Units in Alberta Canada: A Retrospective Cohort Study

2025· article· en· W4415700082 on OpenAlexaffabout
Andrea D. Hill, Bijan Teja, Lisa Burry, Ruxandra Pinto, Nicholas A. Bosch, Allan J. Walkey, Henry T. Stelfox, Martin Chapman, Damon C. Scales, Hannah Wunsch

Bibliographic record

VenueAnesthesia & Analgesia · 2025
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of AlbertaUniversity of TorontoSt. Michael's HospitalMount Sinai HospitalHealth Sciences CentreUniversity of CalgaryInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsRetrospective cohort studyIntensive careShock (circulatory)Cohort studyResuscitationCohort

Abstract

fetched live from OpenAlex

BACKGROUND: In the intensive care unit (ICU), few data are available to guide the maximum infusion rate for individual vasopressors or vasopressor combinations. Clinician preference, decisions to use multiple vasopressors, safety concerns and institutional policy may all affect maximal vasopressor dosing thresholds and potentially contribute to variation in clinical practice. We sought to describe clinical practice patterns in the maximum dosing of vasopressors used as part of the management of patients with shock. We expected to find variability between ICUs in maximum infusion rates for both individual vasopressors and the cumulative dose of vasopressor combinations. METHODS: Retrospective cohort study examining variations in maximum vasopressor doses among adult patients with shock, defined by receipt of one or more vasopressors for at least 6 hours, admitted to 21 ICUs in Alberta, Canada. We defined the maximum vasopressor dose as the highest infusion rate for ≥30 minutes for each individual vasopressor given to a patient, and the maximum total vasopressor dose as the highest combined dose for all vasopressors concurrently administered to a given patient (reported as norepinephrine-equivalents [NEQ]). For each individual vasopressor and overall, we defined a "high-dose" maximum as receipt of a dose above the 90th percentile of maximum doses across the entire patient cohort. We then applied these 90th percentile cutoffs to patients in each ICU. We used multi-level logistic regression to calculate the adjusted median odds ratio (aMOR) for receipt of high-dose maximum NEQ, as a measure of variation of maximum dosing between ICUs. RESULTS: Among 28,397 ICU admissions, the 90th percentile of unadjusted maximum NEQ dose reported administered to patients with shock was 0.7 µg/kg/min (full range 0.01-7.3 µg/kg/min; 1st to 99th percentile 0.03-2.0 µg/kg/min; 10th percentile 0.1 µg/kg/min). The percent of patients in each ICU receiving a high-dose maximum vasopressor (≥0.7 µg/kg/min) ranged from 3.6% to 32.1%. After adjustment for known patient factors, the aMOR (95% confidence interval [CI]) for receipt of a high-dose maximum vasopressors between different ICUs remained: 1.89 (1.50-2.40). Across the entire cohort, the 90th percentile cutoff for high-dose maximum norepinephrine was 0.5 µg/kg/min, for epinephrine 0.5 µg/kg/min, for phenylephrine 316.5 µg/min, for dopamine 15.0 µg/kg/min, and for vasopressin 3.6 units/h. Hospital survival was 31.6% for those who received high-dose vasopressors versus 79.7% for those who received lower doses ( P < .001). We found no clear dosing cutoff above which survival to hospital discharge was impossible. CONCLUSIONS: In Alberta CA, the maximum combined vasopressor dose for patients with shock varied between ICUs after adjustment for severity of illness. Some survival occurred even with high maximal doses. Further work is needed to understand the best approach to determining maximum doses for individual patients at high risk of death.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.025
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.031
GPT teacher head0.302
Teacher spread0.271 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueAnesthesia & AnalgesiaSame topicSepsis Diagnosis and TreatmentFrench-language works237,207