Validation of the artificial intelligence-based trauma outcomes predictor (TOP) in patients 65 years and older

Abstract

Background: The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient.

Methods: All patients aged 65 years and older in the American College of Surgeons-Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years.

Results: A total of 260,505 patients were included. Median age was 77 (71-84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively).

Conclusion: The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.

Graphical abstract

Figure 1

The optimal classification tree used by Trauma Outcome Predictor (TOP) to predict inpatient mortality, with a magnification on one of the terminal nodes.

Figure 2

Example screen shot of the Trauma Outcome Predictor (TOP) application in predicting mortality after blunt injury. TOP is interactive, and the answer to a question dictates the next question. In this case, the value of the Glasgow Coma Scale on emergency department presentation takes the algorithm in a different direction.

Figure 3

Trauma Outcome Predictor (TOP) model area under the receiver operator characteristic (ROC) curve; for (A) blunt injury mortality and (B) penetrating injury mortality.

Figure 4

Trauma Outcome Predictor (TOP) model area under the receiver operator characteristic (ROC) curve; for (A) blunt injury morbidity and (B) penetrating injury morbidity.

Figure 5

Radar plots depicting the performance (c-statistic) of Trauma Outcome Predictor (TOP) in predicting 9 individual complications in blunt (B) and penetrating (P) trauma patients. The axis extends from the outermost circle, with associated c-statistic (area under the curve) of 1, and each concentric circle moving inward signifies an interval of 0.2.