Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms

TL;DR

This study explores how heart rate variability during low blood pressure episodes can predict short-term neurological outcomes in preterm infants, showing high accuracy with combined features and machine learning.

Contribution

It introduces a novel approach using HRV features during hypotensive episodes to predict outcomes, enhancing clinical decision support in preterm care.

Findings

HRV features during low BP episodes predict outcomes with AUC of 0.87

Combining multiple features yields an AUC of 0.97

Proposes a multimodal data approach for clinical prediction

Abstract

Efficient management of low blood pressure (BP) in preterm neonates remains challenging with a considerable variability in clinical practice. The ability to assess preterm wellbeing during episodes of low BP will help to decide when and whether hypotension treatment should be initiated. This work aims to investigate the relationship between heart rate variability (HRV), BP and the short-term neurological outcome in preterm infants less than 32 weeks gestational age (GA). The predictive power of common HRV features with respect to the outcome is assessed and shown to improve when HRV is observed during episodes of low mean arterial pressure (MAP) - with a single best feature leading to an AUC of 0.87. Combining multiple features with a boosted decision tree classifier achieves an AUC of 0.97. The work presents a promising step towards the use of multimodal data in building an objective decision support tool for clinical prediction of short-term outcome in preterms who suffer episodes of low BP.