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 modelling is not linear. We aimed to use Artificial Intelligence (AI) technology to design and validate a nonlinear risk calculator for trauma patients.
A novel, interpretable AI technology called Optimal Classification Trees (OCTs) was used in an 80:20 derivation:validation split of the 2010-2016 ACS-TQIP database. Demographics, ED vital signs, comorbidities, and injury characteristics (e.g. severity, mechanism) of all blunt and penetrating trauma patients ≥ 18 years old 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.
A total of 934,053 patients were included (747,249 derivation; 186,804 validation). The median age was 51 years, 37% were female, 90.5% had blunt trauma, and the median ISS 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. TOP 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.
We suggest TOP as an AI-based, interpretable, accurate, and non-linear risk calculator for predicting outcome in trauma patients. TOP can prove useful for bedside counseling of critically injured trauma patients and their families, and for benchmarking the quality of trauma care.
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