Association of Postoperative Undertriage to Hospital Wards With Mortality and Morbidity
Authors: Tyler J. Loftus, Matthew M. Ruppert, Tezcan Ozrazgat-Baslanti, Jeremy A. Balch, Philip A. Efron, Patrick J. Tighe, William R. Hogan, Parisa Rashidi, Gilbert R. Upchurch, Azra Bihorac
Journal: JAMA Network Open (2021)

<h3>Importance</h3> Undertriaging patients who are at increased risk for postoperative complications after surgical procedures to low-acuity hospital wards (ie, floors) rather than highly vigilant intensive care units (ICUs) may be associated with risk of unrecognized decompensation and worse patient outcomes, but evidence for these associations is lacking. <h3>Objective</h3> To test the hypothesis that postoperative undertriage is associated with increased mortality and morbidity compared with risk-matched ICU admission. <h3>Design, Setting, and Participants</h3> This longitudinal cross-sectional study was conducted using data from the University of Florida Integrated Data Repository on admissions to a university hospital. Included patients were individuals aged 18 years or older who were admitted after a surgical procedure from June 1, 2014, to August 20, 2020. Data were analyzed from April through August 2021. <h3>Exposures</h3> Ward admissions were considered undertriaged if their estimated risk for hospital mortality or prolonged ICU stay (ie, ≥48 hours) was in the top quartile among all inpatient surgical procedures according to a validated machine-learning model using preoperative and intraoperative electronic health record features available at surgical procedure end time. A nearest neighbors algorithm was used to identify a risk-matched control group of ICU admissions. <h3>Main Outcomes and Measures</h3> The primary outcomes of hospital mortality and morbidity were compared among appropriately triaged ward admissions, undertriaged wards admissions, and a risk-matched control group of ICU admissions. <h3>Results</h3> Among 12 348 postoperative ward admissions, 11 042 admissions (89.4%) were appropriately triaged (5927 [53.7%] women; median [IQR] age, 59 [44-70] years) and 1306 admissions (10.6%) were undertriaged and matched with a control group of 2452 ICU admissions. The undertriaged group, compared with the control group, had increased median [IQR] age (64 [54-74] years vs 62 [50-73] years;<i>P</i> = .001) and increased proportions of women (649 [49.7%] women vs 1080 [44.0%] women;<i>P</i> &lt; .001) and admitted patients with do not resuscitate orders before first surgical procedure (53 admissions [4.1%] vs 27 admissions [1.1%]);<i>P</i> &lt; .001); 207 admissions that were undertriaged (15.8%) had subsequent ICU admission. In the validation cohort, hospital mortality and prolonged ICU stay estimations had areas under the receiver operating characteristic curve of 0.92 (95% CI, 0.91-0.93) and 0.92 (95% CI, 0.92-0.92), respectively. The undertriaged group, compared with the control group, had similar incidence of prolonged mechanical ventilation (32 admissions [2.5%] vs 53 admissions [2.2%];<i>P</i> = .60), decreased median (IQR) total costs for admission ($26 900 [$18 400-$42 300] vs $32 700 [$22 700-$48 500];<i>P</i> &lt; .001), increased median (IQR) hospital length of stay (8.1 [5.1-13.6] days vs 6.0 [3.3-9.3] days,<i>P</i> &lt; .001), and increased incidence of hospital mortality (19 admissions [1.5%] vs 17 admissions [0.7%];<i>P</i> = .04), discharge to hospice (23 admissions [1.8%] vs 14 admissions [0.6%];<i>P</i> &lt; .001), unplanned intubation (45 admissions [3.4%] vs 49 admissions [2.0%];<i>P</i> = .01), and acute kidney injury (341 admissions [26.1%] vs 477 admissions [19.5%];<i>P</i> &lt; .001). <h3>Conclusions and Relevance</h3> This study found that admitted patients at increased risk for postoperative complications who were undertriaged to hospital wards had increased mortality and morbidity compared with a risk-matched control group of admissions to ICUs. Postoperative undertriage was identifiable using automated preoperative and intraoperative data as features in real-time machine-learning models.