From the Department of Surgery (L.R.M.), Massachusetts General Hospital, Boston; Massachusetts Institute of Technology (D.B., H.T.B., K.G.), Cambridge; Interpretable AI (D.B., D.Z., J.D.); and Division of Trauma, Emergency Surgery, and Surgical Critical Care (M.E.H., M.E.M., G.C.V., H.M.A.K.), Massachusetts General Hospital, Boston, Massachusetts.
J Trauma Acute Care Surg. 2021 Jul 1;91(1):93-99. doi: 10.1097/TA.0000000000003158.
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.
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.
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.
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.
经典风险评估工具通常将患者的风险因素视为线性和可加的。临床实际情况表明，某些风险因素的存在可能会改变其他因素的影响；换句话说，风险建模不是线性的。我们旨在使用人工智能 (AI) 技术设计和验证创伤患者的非线性风险计算器。
在 2010 年至 2016 年美国外科医师学院创伤质量改进计划数据库的 80:20 推导/验证分割中，使用了一种新颖的、可解释的人工智能技术，称为最佳分类树 (OCT)。使用所有 18 岁及以上的钝器和穿透性创伤患者的人口统计学、急诊科生命体征、合并症和损伤特征（例如严重程度、机制）来开发、训练和验证 OCT 算法，以预测院内死亡率和并发症（例如急性肾损伤、急性呼吸窘迫综合征、深静脉血栓形成、肺栓塞、脓毒症）。创建了一个智能手机应用程序作为算法的交互和用户友好界面。使用 c 统计量方法测量性能。
共纳入 934053 例患者（747249 例推导；186804 例验证）。中位年龄为 51 岁，37%为女性，90.5%为钝器伤，损伤严重程度评分中位数为 11 分。开发了用于钝器和穿透性创伤的综合 OCT 算法，并创建了交互式智能手机应用程序 Trauma Outcome Predictor（TOP），其中一个问题的答案展开了下一个问题。Trauma Outcome Predictor 准确预测了穿透性损伤（c 统计量：推导 0.95，验证 0.94）和钝性损伤（c 统计量：推导 0.89，验证 0.88）的死亡率。预测并发症的验证 c 统计量范围在 0.69 到 0.84 之间。
我们建议将 TOP 作为一种基于人工智能的、可解释的、准确的、非线性风险计算器，用于预测创伤患者的结局。Trauma Outcome Predictor 可用于床边咨询严重创伤患者及其家属，并用于基准创伤护理质量。
