Parkland Trauma Index of Mortality: Real-Time Predictive Model for Trauma Patients

Adam J Starr, Manjula Julka, Arun Nethi, John D. Watkins, Ryan W. Fairchild, Dustin Rinehart, Caroline Park, Ryan Dumas, Hayden N. Box, Michael W Cripps

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVE: Vital signs and laboratory values are used to guide decisions to use damage control techniques in lieu of early definitive fracture fixation. Previous models attempted to predict mortality risk but have limited utility. There is a need for a dynamic model that captures evolving physiologic changes during a trauma patient's hospital course. METHODS: The Parkland Trauma Index of Mortality (PTIM) is a machine learning algorithm that uses electronic medical record data to predict mortality within 48 hours during the first 3 days of hospitalization. It updates every hour, recalculating as physiology changes. The model was developed using 1935 trauma patient encounters from 2009 to 2014 and validated on 516 patient encounters from 2015 to 2016. Model performance was evaluated statistically. Data were collected retrospectively on its performance after 1 year of clinical use. RESULTS: In the validation data set, PTIM accurately predicted 52 of the sixty-three 12-hour time intervals within 48 hours of mortality, for sensitivity of 82.5% [95% confidence interval (CI), 73.1%-91.9%]. The specificity was 93.6% (95% CI, 92.5%-94.8%), and the positive predictive value (PPV) was 32.5% (95% CI, 25.2%-39.7%). PTIM predicted survival for 1608 time intervals and was incorrect only 11 times, yielding a negative predictive value of 99.3% (95% CI, 98.9%-99.7%). The area under the curve of the receiver operating characteristic curve was 0.94.During the first year of clinical use, when used in 776 patients, the last PTIM score accurately predicted 20 of the twenty-three 12-hour time intervals within 48 hours of mortality, for sensitivity of 86.9% (95% CI, 73%-100%). The specificity was 94.7% (95% CI, 93%-96%), and the positive predictive value was 33.3% (95% CI, 21.4%-45%). The model predicted survival for 716 time intervals and was incorrect 3 times, yielding a negative predictive value of 99.6% (95% CI, 99.1%-100%). The area under the curve of the receiver operating characteristic curve was 0.97. CONCLUSIONS: By adapting with the patient's physiologic response to trauma and relying on electronic medical record data alone, the PTIM overcomes many of the limitations of previous models. It may help inform decision-making for trauma patients early in their hospitalization. LEVEL OF EVIDENCE: Prognostic Level I. See Instructions for Authors for a complete description of levels of evidence.

Original languageEnglish (US)
Pages (from-to)280-286
Number of pages7
JournalJournal of orthopaedic trauma
Volume36
Issue number6
DOIs
StatePublished - Jun 1 2022

ASJC Scopus subject areas

  • Surgery
  • Orthopedics and Sports Medicine

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