Kawai Yasuyuki, Yamamoto Koji, Tsuruta Keisuke, Miyazaki Keita, Asai Hideki, Fukushima Hidetada
Department of Emergency and Critical Care Medicine, Nara Medical University, Shijo-cho, 840, Kashihara City, Nara, 634-8522, Japan.
BMC Emerg Med. 2025 May 13;25(1):78. doi: 10.1186/s12873-025-01233-9.
Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care upon arrival are crucial for improving survival. In Japan, helicopter emergency medical services (HEMS) complement general emergency medical services (GEMS) by providing on-site care, reducing transport times, and aiding facility selection. Vital signs at hospital arrival determine initial management, but existing models are poor at predicting them, especially in patients receiving continuous interventions from both GEMS and HEMS. Therefore, we developed a machine-learning model to accurately predict the actual values of vital signs at hospital arrival using limited patient characteristic data and prehospital vital signs.
Using data from the Japanese Society for Aeromedical Services registry, we retrospectively analyzed data from patients aged ≥18 years transported by HEMS between April 2020 and March 2022. Patients with cardiac arrest during transport, missing vital signs, and data inconsistencies were excluded. The predictive model used prehospital vital signs from GEMS and HEMS contact times, demographic characteristics, and intervention information. The primary outcome was the actual values of vital signs measured at hospital arrival. After data preprocessing, we constructed a deep neural network multi-output regression model using Bayesian optimization. Model performance was assessed by comparing the predicted values with the actual hospital arrival measurements using mean absolute error, R² score, residual standard deviation, and Spearman's correlation coefficient. Additionally, the NN model's performance was compared with alternative methods, namely HEMS contact values and change-based predictions derived solely from prehospital data.
The study included 10,478 patients (median age 70 years; 69% male). The model achieved mean absolute errors of 7.1 bpm for heart rate, 15.7 mmHg for systolic blood pressure, 10.8 mmHg for diastolic blood pressure, 2.9 breaths/min for respiratory rate, and 0.62 points for Glasgow Coma Scale score. The Spearman's correlation coefficients ranged from 0.54 to 0.86. The model outperformed other methods, especially for R² scores and residual standard deviations, demonstrating its superior ability to predict actual vital signs values.
The multi-output regression model accurately predicted the actual values of vital signs measured at hospital arrival using limited prehospital information, demonstrating the effectiveness of advanced modeling techniques.
危重症患者病情可能迅速恶化；因此，及时的院前干预以及到达医院后无缝过渡到院内治疗对于提高生存率至关重要。在日本，直升机紧急医疗服务（HEMS）通过提供现场护理、缩短转运时间和协助选择医疗机构来补充一般紧急医疗服务（GEMS）。到达医院时的生命体征决定初始治疗，但现有模型在预测这些生命体征方面效果不佳，尤其是在同时接受GEMS和HEMS持续干预的患者中。因此，我们开发了一种机器学习模型，以使用有限的患者特征数据和院前生命体征准确预测到达医院时生命体征的实际值。
利用日本航空医疗服务协会登记处的数据，我们回顾性分析了2020年4月至2022年3月期间由HEMS转运的年龄≥18岁患者的数据。排除转运期间心脏骤停、生命体征缺失和数据不一致的患者。预测模型使用来自GEMS和HEMS接触时间的院前生命体征、人口统计学特征和干预信息。主要结局是到达医院时测量的生命体征实际值。经过数据预处理后，我们使用贝叶斯优化构建了一个深度神经网络多输出回归模型。通过使用平均绝对误差、R²分数、残差标准差和Spearman相关系数将预测值与到达医院时的实际测量值进行比较来评估模型性能。此外，将神经网络模型的性能与其他方法进行比较，即HEMS接触值和仅从院前数据得出的基于变化的预测。
该研究纳入了10478例患者（中位年龄70岁；69%为男性）。该模型实现的平均绝对误差为：心率7.1次/分钟、收缩压15.7 mmHg、舒张压10.8 mmHg、呼吸频率2.9次/分钟、格拉斯哥昏迷量表评分为0.62分。Spearman相关系数范围为0.54至0.86。该模型优于其他方法，尤其是在R²分数和残差标准差方面，表明其在预测实际生命体征值方面具有卓越能力。
多输出回归模型使用有限的院前信息准确预测了到达医院时测量的生命体征实际值，证明了先进建模技术的有效性。
