Hefny Ashraf F, Almansoori Taleb M, Smetanina Darya, Morozova Daria, Voitetskii Roman, Das Karuna M, Kashapov Aidar, Mansour Nirmin A, Fathi Mai A, Khogali Mohammed, Ljubisavljevic Milos, Statsenko Yauhen
Department of Surgery, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Front Surg. 2024 Oct 24;11:1462692. doi: 10.3389/fsurg.2024.1462692. eCollection 2024.
In blunt chest trauma, patient management is challenging because clinical guidelines miss tools for risk assessment. No clinical scale reliably measures the severity of cases and the chance of complications.
The objective of the study was to optimize the management of patients with blunt chest trauma by creating models prognosticating the transfer to the intensive care unit and in-hospital length of stay (LOS).
The study cohort consisted of 212 cases. We retrieved information on the cases from the hospital's trauma registry. After segmenting the lungs with Lung CT Analyzer, we performed volumetric feature extraction with data-characterization algorithms in PyRadiomics.
To predict whether the patient will require intensive care, we used the three groups of findings: ambulance, admission, and radiomics data. When trained on the ambulance data, the models exhibited a borderline performance. The metrics improved after we retrained the models on a combination of ambulance, laboratory, radiologic, and physical examination data (81.5% vs. 94.4% Sn). Radiomics data were the top-accurate predictors (96.3% Sn). Age, vital signs, anthropometrics, and first aid time were the best-performing features collected by the ambulance service. Laboratory findings, AIS scores for the lower extremity, abdomen, head, and thorax constituted the top-rank predictors received on admission to the hospital. The original first-order kurtosis had the highest predictive value among radiomics data. Top-informative radiomics features were derived from the right hemithorax because the right lung is larger. We constructed regression models that can adequately reflect the in-hospital LOS. When trained on different groups of data, the machine-learning regression models showed similar performance (MAE/ROV 8%). Anatomic scores for the body parts other than thorax and laboratory markers of hemorrhage had the highest predictive value. Hence, the number of injured body parts correlated with the case severity.
The study findings can be used to optimize the management of patients with a chest blunt injury as a specific case of monotrauma. The models we built may help physicians to stratify patients by risk of worsening and overcome the limitations of existing tools for risk assessment. High-quality AI models trained on radiomics data demonstrate superior performance.
在钝性胸部创伤中，患者管理具有挑战性，因为临床指南缺乏风险评估工具。没有临床量表能够可靠地衡量病例的严重程度和并发症发生的可能性。
本研究的目的是通过创建预测转入重症监护病房和住院时间（LOS）的模型，优化钝性胸部创伤患者的管理。
研究队列包括212例病例。我们从医院的创伤登记处获取病例信息。使用肺CT分析仪对肺部进行分割后，我们在PyRadiomics中使用数据特征化算法进行体积特征提取。
为了预测患者是否需要重症监护，我们使用了三组结果：救护车、入院和放射组学数据。当基于救护车数据进行训练时，模型表现出临界性能。在我们将模型重新训练于救护车、实验室、放射学和体格检查数据的组合后，指标有所改善（敏感度81.5%对94.4%）。放射组学数据是最准确的预测指标（敏感度96.3%）。年龄、生命体征、人体测量学和急救时间是救护服务收集的表现最佳的特征。实验室检查结果、下肢、腹部、头部和胸部的简明损伤定级（AIS）评分是入院时获得的顶级预测指标。在放射组学数据中，原始一阶峰度具有最高的预测价值。信息量最大的放射组学特征来自右半胸，因为右肺更大。我们构建了能够充分反映住院LOS的回归模型。当基于不同组数据进行训练时，机器学习回归模型表现出相似的性能（平均绝对误差/相对观察值8%）。胸部以外身体部位的解剖学评分和出血的实验室指标具有最高的预测价值。因此，受伤身体部位的数量与病例严重程度相关。
本研究结果可用于优化作为单一创伤特定病例的胸部钝性损伤患者的管理。我们构建的模型可能有助于医生根据病情恶化风险对患者进行分层，并克服现有风险评估工具的局限性。基于放射组学数据训练的高质量人工智能模型表现出卓越的性能。
