Evidence map›Paper›PMID 37830104›Full record

ArticleBJA open2023

Anaesthetist prediction of postoperative opioid use: a multicentre prospective cohort study.

Kieran Taylor, Mihika De Bruyne, Christine Li, Marcus Yip, Dominique Grant, Xinci Tang, Sarah Laing, Braden Preston, Kavinay Chand, Anurika De Silva and 2 more

Open access · goldAbstract read
In one paragraph

Article in BJA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 8 institutions in 1 country.

Kieran TaylorDepartment of Anaesthesia and Pain Management, Royal Melbourne Hospital, Melbourne, Australia.
Mihika De BruyneDepartment of Anaesthesia and Pain Management, Royal Melbourne Hospital, Melbourne, Australia.
Christine LiDepartment of Anaesthesia and Pain Medicine, Footscray Hospital, Melbourne, Australia.
Marcus YipDepartment of Anaesthesia, St Vincent's Hospital, Melbourne, Australia.
Dominique GrantDepartment of Anaesthesia, Austin Hospital, Melbourne, Australia.
Xinci TangDepartment of Anaesthesia, Northern Hospital, Melbourne, Australia.
Sarah LaingDepartment of Anaesthesia, Ballarat Base Hospital, Ballarat, Australia.
Braden PrestonDepartment of Anaesthesia, Northeast Health Wangaratta, Wangaratta, Australia.
Kavinay ChandDepartment of Anaesthesia, Goulburn Valley Health, Shepparton, Australia.
Anurika De SilvaCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Monash University, Melbourne, Australia.
Kate LeslieDepartment of Anaesthesia and Pain Management, Royal Melbourne Hospital, Melbourne, Australia.
Jai N DarvallDepartment of Anaesthesia and Pain Management, Royal Melbourne Hospital, Melbourne, Australia.
Monash University · AUThe University of Melbourne · AUAustin Hospital · AUBallarat Health Services · AUFootscray Hospital · AUGoulburn Valley Health · AUNorthern Hospital · AUSt Vincent's Hospital Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The Apfel simplified risk score includes four risk factors: female sex, non-smoking status, postoperative nausea and vomiting or motion sickness history, and postoperative opioid use. The score is calculated preoperatively, so postoperative opioid use must be predicted. We aimed to determine whether anaesthetists can predict patients' postoperative opioid use and dose. Methods: Specialist anaesthetists from eight hospitals preoperatively predicted opioid use and dose in the post-anaesthesia care unit (PACU) and for the first 24 h postoperatively, which was compared with actual opioid use and dose. Opioid doses were converted to oral morphine equivalents (MEQ). Correlations between predicted and actual opioid use and dose were analysed with Spearman's rho and linear regression. Results: A total of 487 anaesthetist-patient pairs were included. Anaesthetists overpredicted opioid use (398 [82%] predicted Conclusions: Specialist anaesthetists cannot accurately predict opioid use or dose in the PACU or the first 24 postoperative hours. The Apfel risk criterion for postoperative opioid use may be inaccurate in clinical practice.

Indexed as

anaesthesiaopioidpostoperative nausea and vomitingrisk predictionrisk score

Identifiers

PMID37830104
PMCPMC10565679
OpenAlexW4387417288

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.