Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients. Issue 1 (22nd July 2021)

• Record Type: Journal Article
• Authors: Maurer, Lydia R.; Bertsimas, Dimitris; Bouardi, Hamza Tazi; El Hechi, Majed; El Moheb, Mohamad; Giannoutsou, Katerina; Zhuo, Daisy; Dunn, Jack; Velmahos, George C.; Kaafarani, Haytham M.A.
• Is Part Of: Journal of trauma and acute care surgery. Volume 91:Issue 1(2021)
• Page Start: 93
• Page End: 99
• Publication Date: 2021-07-22
• Subjects: Artificial intelligence -- trauma surgery -- machine-learning -- optimal classification trees, risk prediction -- trauma outcomes; Surgical intensive care -- Periodicals; Surgical emergencies -- Periodicals; Wounds and injuries -- Surgery -- Periodicals; 617.026
• DOI: 10.1097/TA.0000000000003158
• Languages: English
• ISSNs: 2163-0755

Abstract

Abstract : BACKGROUND: Classic risk assessment tools often treat patients' risk factors as linear and additive. Clinical reality suggests that the presence of certain risk factors can alter the impact of other factors; in other words, risk modeling is not linear. We aimed to use artificial intelligence (AI) technology to design and validate a nonlinear risk calculator for trauma patients. METHODS: A novel, interpretable AI technology called Optimal Classification Trees (OCTs) was used in an 80:20 derivation/validation split of the 2010 to 2016 American College of Surgeons Trauma Quality Improvement Program database. Demographics, emergency department vital signs, comorbidities, and injury characteristics (e.g., severity, mechanism) of all blunt and penetrating trauma patients 18 years or older were used to develop, train then validate OCT algorithms to predict in-hospital mortality and complications (e.g., acute kidney injury, acute respiratory distress syndrome, deep vein thrombosis, pulmonary embolism, sepsis). A smartphone application was created as the algorithm's interactive and user-friendly interface. Performance was measured using the c-statistic methodology. RESULTS: A total of 934, 053 patients were included (747, 249 derivation; 186, 804 validation). The median age was 51 years, 37% were women, 90.5% had blunt trauma, and the median Injury Severity Score was 11. Comprehensive OCT algorithms were developed for blunt and penetrating trauma, and the interactive smartphone application, Trauma Outcome Predictor (TOP) was created, where the answer to one question unfolds the subsequent one. Trauma Outcome Predictor accurately predicted mortality in penetrating injury (c-statistics: 0.95 derivation, 0.94 validation) and blunt injury (c-statistics: 0.89 derivation, 0.88 validation). The validation c-statistics for predicting complications ranged between 0.69 and 0.84. CONCLUSION: We suggest TOP as an AI-based, interpretable, accurate, and nonlinear risk calculator for predicting outcome in trauma patients. Trauma Outcome Predictor can prove useful for bedside counseling of critically injured trauma patients and their families, and for benchmarking the quality of trauma care.