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

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

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.

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.

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

https://link.springer.com/article/10.1186/s12873-025-01233-9

https://link.springer.com/journal/12873

13 may 2025

Fajar bagus W

PDF

English

Text