JMIR Publications

ABSTRACT

Background:

Out-of-hospital cardiac arrest (OHCA) is a serious public health issue and predicting the prognosis of OHCA patients would be helpful for clinicians to make decisions on the treatment of patients or use of hospital resources.

Objective:

This study aimed to develop a time-adaptive conditional prediction model (TACOM) for predicting clinical outcomes every minute.

Methods:

We performed a retrospective observational study using data from the Korea OHCA Registry in South Korea. In this study, we excluded patients with trauma, those who experienced return of spontaneous circulation before arriving in the emergency departments (ED), and those who did not receive cardiopulmonary resuscitation (CPR) in the ED. We selected patients who received CPR in the ED. To develop the time-adaptive prediction model, we organized the training dataset as ongoing CPR patients by the minute. We used LightGBM as the machine-learning method. Model performance was quantified using the prediction probability of the model, area under the receiver operating characteristic curve (AUROC), and area under the precision recall curve.

Results:

From 0 to 30 min, the AUROC of TACOM for predicting good neurologic outcomes ranged from 0.910 (0.910–0.911) to 0.869 [0.865–0.871], whereas that for survival to hospital discharge was 0.800 (0.797–0.800) to 0.734 (0.736–0.740). The prediction probability of TACOM showed similar flow with cohort data, based on a comparison with the conventional model’s prediction probability.

Conclusions:

TACOM predicted the clinical outcome of patients with OHCA every minute. This model for predicting patient outcomes by minute can be expected to help clinicians make rational decisions for OHCA patients.