Evidence map›Paper›PMID 40550516›Full record

ArticleActa anaesthesiologica Scandinavica2025

External Validation of an Algorithm to Guide Opioid Administration at the End of Surgery-Protocol for an Observational Cohort Study of the OPIAID Algorithm.

Anders Peder Højer Karlsen, Pernille Bjersand Sunde, Ida Houtved Rasmussen, Caroline Folkersen, Trang Xuan Minh Tran, Markus Kien Trung Nguyen, Line Maria Elkjær, Troels Haxholdt Lunn, Christian S Meyhoff, Jonas Valbjørn Andersen and 2 more

Abstract read
In one paragraph

Article in Acta anaesthesiologica Scandinavica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Observational
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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.

Anders Peder Højer KarlsenDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.ORCID 0000-0002-0338-3751
Pernille Bjersand SundeDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.ORCID 0009-0008-8860-076X
Ida Houtved RasmussenDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.ORCID 0009-0007-7290-5254
Caroline FolkersenDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.
Trang Xuan Minh TranDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.ORCID 0000-0002-8491-3389
Markus Kien Trung NguyenDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.
Line Maria ElkjærDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.
Troels Haxholdt LunnDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.
Christian S MeyhoffDepartment of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.ORCID 0000-0002-4885-4609
Jonas Valbjørn AndersenIT-University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-3366-5573
Ole MathiesenDepartment of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
Markus Harboe OlsenCopenhagen Trial Unit, Centre for Clinical Intervention Research, The Capital Region, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.ORCID 0000-0003-0981-0723

Funding

Danmarks Frie Forskningsfond 4308-00002B
6 · The paper itself

Abstract

backgroundDespite advances in pain management, inadequate pain relief and opioid-related adverse events remain common challenges in perioperative care, often contributing to prolonged recovery and reduced quality of life. The perioperative opioid algorithms for individualized dosing (OPIAID) project aims to develop machine-learning algorithms tailored to provide patient-specific opioid dosing across the different phases of perioperative care. For each phase, eight models are trained on granular data from 1.1 million surgical procedures, including demographic and surgical details, vital signs, administered analgesics, pain, and opioid-related adverse events. The two most accurate models will proceed to external validation. The best-performing model will subsequently be tested as a decision support against current standard of care.

objectivesThis protocol describes the design and external validation of the intraoperative OPIAID algorithm, which suggests the end-of-surgery opioid dose intended for postoperative analgesia by approximating clinical performance and evaluating reliability, agreement, and calibration.

methodsIn this multicenter, TRIPOD+AI-adherent, prospective observational cohort study, we will collect data from a diverse surgical population of 656 adult patients undergoing elective or acute surgery under general anesthesia. All patients will require intraoperative opioid administration at the end of surgery for postoperative pain management and a subsequent stay in the post-anesthesia care unit. The cohort will be used to externally validate two machine-learning models through standardized measures of reliability, agreement, and calibration, and thereby designate the intraoperative OPIAID algorithm. Subsequently, the cohort will be used to approximate the clinical efficacy, safety and overall performance of the intraoperative OPIAID algorithm's recommended doses versus the clinician-administered doses. These comparisons will be based on each approach's proximity to a golden standard "optimal dose," which is calculated based on a predefined generic ruleset incorporating intraoperative opioid dosing, postoperative pain, opioid-related adverse events, and need for rescue opioid administrations.

conclusionThe intraoperative OPIAID algorithm is intended as a clinical decision aid for anesthesiologists and nurse anesthetists in providing adequate postoperative pain management.

Indexed as

AlgorithmsAnalgesics, OpioidPostoperative PainAdultCohort StudiesHumansMachine LearningObservational Studies as TopicPain ManagementPerioperative CareProspective StudiesReproducibility of ResultsAnalgesics, Opioidopioidspainpersonalized medicinepostoperative managementsurgery

Identifiers

PMID40550516
PMCPMC12185177

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Registered trials

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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.