Association of Postoperative Undertriage to Hospital Wards With Mortality and Morbidity

Tyler J Loftus1,2, Matthew M Ruppert2,3, Tezcan Ozrazgat-Baslanti2,3

  • 1Department of Surgery, University of Florida Health, Gainesville.

JAMA Network Open
|November 10, 2021
PubMed

Insights

Patients undertriaged to hospital wards after surgery face higher mortality and morbidity. Machine learning models can identify these high-risk patients for better care allocation, improving postoperative outcomes.

Area of Science:

  • Medical Informatics
  • Surgical Outcomes Research
  • Critical Care Medicine

Background:

  • Undertriage of high-risk surgical patients to general wards instead of ICUs may lead to adverse outcomes.
  • Evidence linking undertriage to increased mortality and morbidity is limited.

Purpose of the Study:

  • To evaluate the association between postoperative undertriage and patient outcomes.
  • To compare mortality and morbidity in undertriaged patients versus risk-matched ICU admissions.

Main Methods:

  • A longitudinal, cross-sectional study using electronic health record data from a university hospital (2014-2020).
  • A validated machine-learning model identified undertriaged ward admissions based on risk for mortality or prolonged ICU stay.
  • A nearest neighbors algorithm matched undertriaged patients with ICU admissions for comparison.

Main Results:

  • Of 12,348 ward admissions, 10.6% were undertriaged. Undertriaged patients had increased hospital mortality (1.5% vs 0.7%), unplanned intubation (3.4% vs 2.0%), and acute kidney injury (26.1% vs 19.5%) compared to matched ICU controls.
  • Undertriaged patients experienced longer hospital stays (8.1 vs 6.0 days) but lower total costs.
  • Machine learning models demonstrated high accuracy (AUC 0.92) in predicting mortality and prolonged ICU stay.

Conclusions:

  • Postoperative undertriage to general hospital wards is associated with significantly increased mortality and morbidity.
  • Machine learning models can accurately identify undertriaged surgical patients in real-time using electronic health record data.
  • These findings highlight the need for improved risk stratification and care allocation for surgical patients to prevent adverse outcomes.
Abstract

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