Evidence map›Paper›PMID 41549026›Full record

ArticleBritish journal of anaesthesia2026

Prospective validation and real-time implementation of an automated machine learning postoperative mortality prediction model.

Theodora Wingert, Tiffany Williams, Briana Syed, Brian Hill, Tristan Grogan, Andrew Young, Zarah Antongiorgi, Valiollah Salari, Alexandre Joosten, Ira Hofer and 3 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of anaesthesia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

13 authors.

Theodora WingertDepartment of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA. Electronic address: twingert@mednet.ucla.edu.
Tiffany WilliamsDepartment of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Briana SyedUniversity of Texas Medical Branch, John Sealy School of Medicine, Galveston, TX, USA.
Brian HillAge Bold, Inc., Los Angeles, CA, USA.
Tristan GroganDepartment of Medicine Statistics Core, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, USA.
Andrew YoungDepartment of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Zarah AntongiorgiDepartment of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Valiollah SalariDepartment of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Alexandre JoostenDepartment of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Ira HoferDepartment of Anesthesiology, Perioperative & Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Eran HalperinDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
Maxime CannessonDepartment of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Eilon GabelDepartment of Anesthesiology & Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.

Funding

UC BRAID Center for Accelerated Innovation COVID-19 diagnostic project developmentU54HL119893 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PALAZZOLO, MICHAEL J · 2013 to 2020
$18.3M
Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical PatientsR01HL144692 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANNESSON, MAXIME · 2019 to 2023
$3.5M
Machine learning of physiological variables to predict diagnose and treat cardiorespiratory instabilityR01GM117622 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI PINSKY, MICHAEL R · 2016 to 2019
$2.4M
Personalized Risk Prediction for Prevention and Early Detection of Postoperative Failure to RescueR01EB035028 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Maxime Cannesson · 2023 to 2026
$2.3M
Use of Machine Learning on Integrated Electronic Medical Record, Genetic andWaveform Data to Predict Perioperative Cardiorespiratory InstabilityK01HL150318 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HOFER, IRA · 2020 to 2024
$855k
NHLBI NIH HHS K01 HL150318NHLBI NIH HHS R01 HL144692NHLBI NIH HHS U54 HL119893NIBIB NIH HHS R01 EB035028NIGMS NIH HHS R01 GM117622
6 · The paper itself

Abstract

backgroundMachine learning prediction models require prospective validation to ensure implementation fidelity and feasibility. Our primary objective was to prospectively validate a previously reported postoperative mortality prediction model in inpatients undergoing surgery. Our secondary objective was to evaluate feasibility of a pilot clinical decision support tool.

methodsWe prospectively validated and implemented a random forest machine learning model trained to predict in-hospital mortality using data from a single academic medical centre. A reduced 32-feature model was implemented into the electronic health record (EHR) using a real-time data mart at the same institution. To assess model performance, the area under the receiver operating characteristic curve (AUROC), area under the curve precision-recall (AUCPR), and other performance measures were calculated. To assess feasibility, implementation workflow metrics were evaluated and a survey was administered to anaesthesiologists trained to use the pilot clinical decision support tool.

resultsThe AUROC for the prospectively implemented model was 0.874 (95% confidence interval [CI] 0.860-0.887), and the AUCPR was 0.111. By comparison, the AUROC for the 58-feature model was 0.925 (95% CI 0.900-0.947), and for ASA physical status the AUROC was 0.814 (95% CI 0.802-0.827) and the AUCPR was 0.103. The implementation demonstrated feasibility through real-time data updates, automated transfer of model outputs to the EHR, and provider survey entries.

conclusionsThis prospective validation and EHR implementation of a previously published random forest machine learning model predicting postoperative in-hospital mortality demonstrated acceptable real-world performance of the implemented model and feasibility of integrating such a system into clinical practice.

Indexed as

Hospital MortalityMachine LearningPostoperative ComplicationsAgedElectronic Health RecordsFeasibility StudiesFemaleHumansMaleMiddle AgedPilot ProjectsPrediction AlgorithmsPredictive Learning ModelsProspective StudiesRandom ForestROC Curveimplementationmachine learningmortalitypostoperative outcomesprospective validationrisk prediction

Identifiers

PMID41549026
PMCPMC12927408

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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.