Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): An external validation study

Davy van de Sande, Michel E van Genderen*, Cornelis Verhoef, Joost Huiskens, Diederik Gommers, Edwin van Unen, Renske A Schasfoort, Judith Schepers, Jasper van Bommel, Dirk J Grünhagen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

BACKGROUND: In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital discharge after the second postoperative day. Despite strong model performance (area under the receiver operating characteristics curve of 0.88) in an academic surgical population, it remains unknown whether these findings can be translated to other hospitals and surgical populations. We therefore aimed to determine the generalizability of the previously developed machine learning concept.

METHODS: We externally validated the machine learning concept in gastrointestinal and oncology surgery patients admitted to 3 nonacademic hospitals in The Netherlands between January 2017 and June 2021, who remained admitted 2 days after surgery. Primary outcome was the ability to predict hospital interventions after the second postoperative day, which were defined as unplanned reoperations, radiological interventions, and/or intravenous antibiotics administration. Four forest models were locally trained and evaluated with respect to area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS: All models were trained on 1,693 epsiodes, of which 731 (29.9%) required a hospital intervention and demonstrated strong performance (area under the receiver operating characteristics curve only varied 4%). The best model achieved an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [0.81-0.85]), sensitivity of 77.9% (0.67-0.87), specificity of 79.2% (0.72-0.85), positive predictive value of 61.6% (0.54-0.69), and negative predictive value of 89.3% (0.85-0.93).

CONCLUSION: This study showed that a previously developed machine learning concept can predict safe discharge in different surgical populations and hospital settings (academic versus nonacademic) by training a model on local patient data. Given its high accuracy, integration of the machine learning concept into the clinical workflow could expedite surgical discharge and aid hospitals in addressing capacity challenges by reducing avoidable bed-days.

Original languageEnglish
Pages (from-to)663-669
Number of pages7
JournalSurgery (United States)
Volume172
Issue number2
Early online date4 May 2022
DOIs
Publication statusPublished - Aug 2022

Bibliographical note

Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.

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