Prediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision trees. (October 2019) - Semenova, Oksana; Carra, Giorgia; Lightbody, Gordon; Boylan, Geraldine; Dempsey, Eugene; Temko, Andriy
Highlights: It is shown here that preterm infants, of all gestational ages, with a poor short-term health outcome, are unable to significantly alter their heart-rate variability (HRV) in response to low blood pressure events. This differs from healthy preterms, where a clear change in HRV in response to hypotension is demonstrated. The predictive power of HRV features for short-term health prediction in preterm infants improves when observed during episodes of hypotension. An objective decision support tool for clinical prediction of short-term outcome in preterms with hypotension was constructed based on multimodal HRV and blood pressure data. The best performing decision-support system based on the HRV features extracted during hypotensive episodes, achieved subject level AUC of 97% using leave-one-out subject-independent performance assessment. Abstract: Background and Objective: Efficient management of low blood pressure (BP) in preterm neonates remains challenging with considerable variability in clinical practice. There is currently no clear consensus on what constitutes a limit for low BP that is a risk to the preterm brain. It is argued that a personalised approach rather than a population based threshold is more appropriate. This work aims to assist healthcare professionals in assessing preterm wellbeing during episodes of low BP in order to decide when and whether hypotension treatment should be initiated. In particular, the study investigates the relationship between heart rate variability (HRV) and BP in preterm infants and its relevance to a short-term health outcome. Methods: The study is performed on a large clinically collected dataset of 831 h from 23 preterm infants of less than 32 weeks gestational age. The statistical predictive power of common HRV features is first assessed with respect to the outcome. A decision support system, based on boosted decision trees (XGboost), was developed to continuously estimate the probability of neonatal morbidity based on the feature vector of HRV characteristics and the mean arterial blood pressure. Results: It is shown that the predictive power of the extracted features improves when observed during episodes of hypotension. A single best HRV feature achieves an AUC of 0.87. Combining multiple HRV features extracted during hypotensive episodes with the classifier achieves an AUC of 0.97, using a leave-one-patient-out performance assessment. Finally it is shown that good performance can even be achieved using continuous HRV recordings, rather than only focusing on hypotensive events – this had the benefit of not requiring invasive BP monitoring. Conclusions: The work presents a promising step towards the use of multimodal data in providing objective decision support for the prediction of short-term outcome in preterm infants with hypotensive episodes.