Machine learning model on heart rate variability metrics identifies asymptomatic toddlers exposed to zika virus during pregnancy.

. Although the Zika virus (ZIKV) seems to be prominently neurotropic, there are some reports of involvement of other organs, particularly the heart. Of special concern are those children exposed prenatally to ZIKV and born without microcephaly or other congenital anomalies. Electrocardiogram (ECG)-derived heart rate variability (HRV) metrics represent an attractive, low-cost, widely deployable tool for early identification of developmental functional alterations in exposed children born without such overt clinical symptoms. We hypothesized that HRV in such children would yield a biomarker of fetal ZIKV exposure. Our objective was to test this hypothesis in young children exposed to ZIKV during pregnancy.. We investigated the HRV properties of 21 children aged 4-25 months from Brazil. The infants were divided into two groups, the ZIKV-exposed ( = 13) and controls ( = 8). Single-channel ECG was recorded in each child at ∼15 months of age and HRV was analyzed in 5 min segments to provide a comprehensive characterization of the degree of variability and complexity of the heart rate.Using a cubic support vector machine classifier we identified babies as Zika cases or controls with a negative predictive value of 92% and a positive predictive value of 86%. Our results show that a machine learning model derived from HRV metrics can help differentiate between ZIKV-affected, yet asymptomatic, and non-ZIKV-exposed babies. We identified the box count as the best HRV metric in this study allowing such differentiation, regardless of the presence of microcephaly.We show that it is feasible to measure HRV in infants and toddlers using a small non-invasive portable ECG device and that such an approach may uncover the memory ofexposure to ZIKV. We discuss putative mechanisms. This approach may be useful for future studies and low-cost screening tools involving this challenging to examine population.