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Background <p>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.</p> Methods <p>Using data from the Japanese Society for Aeromedical Services registry, we retrospectively analyzed data from patients aged ≥18&#xa0;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.</p> Results <p>The study included 10,478 patients (median age 70&#xa0;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.</p> Conclusion <p>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.</p>

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Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月13日

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  • Yasuyuki Kawai,
  • Koji Yamamoto,
  • Keisuke Tsuruta,
  • Keita Miyazaki,
  • Hideki Asai,
  • Hidetada Fukushima

摘要

Background

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.

Methods

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.

Results

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.

Conclusion

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.