Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit

A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majority are difficult to identify even in a post-incident analysis. Changes in some patients’ physiology when analysed in detail can however be predictive of acute deterioration leading to cardiac or respiratory arrests. This paper seeks to exploit the causally-related changing patterns in signals such as heart rate, respiration rate, systolic blood pressure and peripheral cutaneous oxygen saturation to evaluate the predictability of cardiac arrests in critically ill paediatric patients in intensive care. In this paper we report the results of a framework constituting feature space embedding and time series forecasting methods to build an automated prediction system. The results were compared with clinical assessment of predictability. A sensitivity of 71% and specificity of 69% was obtained when the maximum value of Anomaly Index (12) in the 50 min (starting one hour and ending 10 min) before the arrest was considered for the case patients and a random 50 min of data was considered for the control set patients. A positive predictive value of 11% and negative predictive value of 98% was obtained with a prevalence of 5% by our method of prediction. While clinicians predicted 4 out of the 69 cardiac arrests (6%), the prediction system predicted 63 (91%) cardiac arrests. Prospective validation of the automated system remains.