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Using heart rate variability to predict clinical disease in preterm babies

The early clinical signs of diseases in the preterm baby in the neonatal intensive care unit (NICU) are often very subtle and difficult to detect. However, once the infection or disease is developed, the preterm baby often deteriorates and becomes sick very rapidly. We aim to develop a new method using heart rate variability (HRV) to detect early clinical diseases. HRV is a measure of the beat-to-beat variation in time between each heartbeat. This variation is controlled by an important part of the nervous system called the autonomic nervous system (ANS). Our project will assess HRV as a noninvasive way to identify changes in the clinical condition of the preterm baby. We have recently acquired a clinical research software known as ICM+, developed at Cambridge University. The ICM+ software offers data collection and real-time analysis, facilitating personalised medicine. ICM+ can be connected to our bedside monitors in the NICU and perform continuous analyses of the HRV in real-time, on multiple babies simultaneously. We propose that continuous HRV can be used to assess well-being of the preterm babies in NICU, detect early infections and predict bleeding in the brain. RESEARCH PLAN: In preterm babies born at ≤28 weeks of gestation, the ECG recording from the NICU cotside monitor will be continuously analysed for HRV in the first 4 weeks of life, using the ICM+ software. Clinical records of the babies will be examined to determine periods of when the baby was clinically stable and when the baby suffered from infection and/or developed bleeding in the brain.
Essential criteria: 
Minimum entry requirements can be found here:
Prematurity, heart rate variability, infection, brain injury, paediatric
School of Clinical Sciences at Monash Health / Hudson Institute of Medical Research
Available options 
Time commitment 
Top-up scholarship funding available 
Physical location 
Monash Health Translation Precinct (Monash Medical Centre)
Rosemary Horne
Stephanie Yiallourou

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