Objectives
The overwhelmed situation under the COVID-19 pandemic has worsened the quality of emergency medical care and the mortality rate due to out-of-hospital cardiac arrest (OHCA). However, there has been no research conducted for the validation of prognostic prediction models for OHCA patients using data collected during the pandemic. We sought to develop a pre-hospital prediction model for neurological outcome at 1 month in adult patients following OHCA using a machine-learning technique and validate the model for data collected during the pandemic.
Methods
The data of 1 740 212 adult OHCA patients from a nationwide registry in Japan between 2005 and 2019 were used for developing a prediction model. Neurological outcome at 1 month after OHCA was set as the prediction target. We validated the model using 96 525 patient data collected during the pandemic from March to December 2020.
Results
The optimal predictive factors were all ascertained at the emergency scene. Although the neurological outcome was less favourable during the pandemic compared with the corresponding pre-pandemic periods, the model yielded substantially high performance with precise calibration: the area under the receiver operating characteristics curve of 0.94 and 0.95 before and during the pandemic, respectively.
Discussion
The model will improve the quality of emergency care by enabling accurate triage and swift preparation for advanced life-saving care regardless of overwhelmed situations due to disastrous circumstances.
Conclusion
We developed a prediction model for neurological outcome in OHCA patients using machine learning techniques, which was adaptable to the medical situation during the COVID-19 pandemic.
