Evidence map›Paper›PMID 39261226›Full record

Trial reportBritish journal of anaesthesia2024

Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.

Bradley A Fritz, Christopher R King, Mohamed Abdelhack, Yixin Chen, Alex Kronzer, Joanna Abraham, Sandhya Tripathi, Arbi Ben Abdallah, Thomas Kannampallil, Thaddeus P Budelier and 9 more

Registry-linked trialAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in British journal of anaesthesia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05042804 (Perioperative Outcome Risk Assessment With Computer Learning Enhancement), which is not on this map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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.

NCT05042804 nacompletednot on this map

Perioperative Outcome Risk Assessment With Computer Learning Enhancement

TypeinterventionalSponsorWashington University School of MedicineRan2021 to 2022Enrolled5,114ConditionsMorality, Acute Kidney InjuryArmsMachine learning models predicting postoperative death and acute kidney injury
3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Bradley A FritzDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA. Electronic address: bafritz@wustl.edu.
Christopher R KingDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Mohamed AbdelhackDepartment of Computer Science and Engineering, Washington University McKelvey School of Engineering, Saint Louis, MO, USA; Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Yixin ChenDepartment of Computer Science and Engineering, Washington University McKelvey School of Engineering, Saint Louis, MO, USA.
Alex KronzerDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Joanna AbrahamDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA; Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Saint Louis, MO, USA.
Sandhya TripathiDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Arbi Ben AbdallahDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Thomas KannampallilDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA; Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Saint Louis, MO, USA.
Thaddeus P BudelierDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Daniel HelstenDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Arianna Montes de OcaDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Divya MehtaDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Pratyush SonthaDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Omokhaye HigoDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Paul KerbyDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Stephen H GregoryDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
Troy S WildesDepartment of Anesthesiology, University of Nebraska Medical Center, Omaha, NE, USA.
Michael S AvidanDepartment of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.

Funding

TECTONICS (Telemedicine Control Tower for the OR: Navigating Information, Care and Safety) trialR01NR017916 · NINR · WASHINGTON UNIVERSITY · PI AVIDAN, MICHAEL SIMON · 2019 to 2023
$3.1M
NINR NIH HHS R01 NR017916
6 · The paper itself

Abstract

backgroundAnaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment.

methodsThis single-centre, prospective, randomised clinical trial enrolled surgical patients aged ≥18 yr. Anaesthesiologists and nurse anaesthetists providing remote telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury (AKI) within 7 days. The primary outcome was area under the receiver operating characteristic curve (AUROC) for clinician predictions of mortality and AKI, comparing AUROCs between assisted and unassisted assessments.

resultsWe analysed 5071 patients (mean [range] age: 58 [18-100] yr; 52% female) assessed by 89 clinicians. Of these, 98 (2.2%) patients died within 30 days of surgery and 450 (11.1%) patients sustained AKI. Clinician predictions agreed with the models more strongly in the assisted vs unassisted group (weighted kappa 0.75 vs 0.62 for death, mean difference: 0.13 [95% CI 0.10-0.17]; and 0.79 vs 0.54 for AKI, mean difference: 0.25 [95% CI 0.21-0.29]). Clinical prediction of death was similar between the assisted (AUROC 0.793) and unassisted (AUROC 0.780) groups (mean difference: 0.013 [95% CI -0.070 to 0.097]; P=0.76). Prediction of AKI had an AUROC of 0.734 in the assisted group vs 0.688 in the unassisted group (difference 0.046 [95% CI -0.003 to 0.091]; P=0.06).

conclusionsClinician performance was not improved by machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. CLINICAL

trial registrationNCT05042804.

Indexed as

Acute Kidney InjuryMachine LearningPostoperative ComplicationsAdolescentAdultAgedAged, 80 and overAnesthesiologistsFemaleHumansMaleMiddle AgedProspective StudiesRisk AssessmentTelemedicineYoung Adultacute kidney injuryanaesthesiology risk assessmentartificial intelligenceclinical trialmachine learningpostoperative complicationspostoperative death

Identifiers

PMID39261226
PMCPMC11488162

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Registered trials

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.