Autism risk in neonatal intensive care unit patients associated with novel heart rate patterns

Circadian rhythm development in preterm infants. The role of postnatal versus postmenstrual age

BACKGROUND, AIMS

Circadian rhythm maturation may be disturbed in premature infants undergoing neonatal intensive care. We used continuous heart rate recordings across the entire neonatal intensive care period to study circadian rhythm development in preterm infants and to evaluate the roles of postmenstrual (PMA) versus postnatal age (PNA).

MATERIALS AND METHODS

The circadian rhythm was calculated using a cosine fit of heart rate. The circadian rhythm amplitudes were averaged weekly and studied relative to PMA and PNA using the linear mixed effects models, adjusting for clinical variables that could affect the heart rate. The daily circadian rhythms were used to create grand averages for PMA groups: ≤31, 32-35, and > 35 weeks, and for PNA groups: ≤30, 31-60, and > 60 days.

RESULTS

Sixty-six infants were evaluated as part of an ongoing prospective study with gestational ages between 23 and 36 weeks. The PMA (1.47 × 10-2 beats per minute (bpm)/week, P = 2.07 × 10-8) and PNA (1.87 × 10-2 bpm/day; P = 1.86 × 10-6) were significantly associated with the circadian rhythm amplitude independent of covariates. Infants ≤31 weeks' PMA and ≤30 days PNA, the phase of circadian rhythm amplitude grand averages showed a peak at night and a nadir during the day. Hereafter the circadian rhythm phase reversed to that established for mature individuals. The highest circadian rhythm amplitudes present >35 weeks' PMA and > 60 days PNA.

CONCLUSIONS

Our results indicate circadian rhythm matures with advancing gestation. The reversed circadian rhythm phase during the early postnatal period could be due to premature exposure to the ex-utero environment and warrant further study.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants

Objective.Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes.Approach. We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90_DPE (duration per event of intermittent hypoxemia events below 90%).Main Results.The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850).Significance. These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

Central Autonomic Network and heart rate variability in premature neonates

INTRODUCTION

The Central Autonomic Network (CAN) is a hierarchy of brain structures that collectively influence cardiac autonomic input, mediating the majority of brain-heart interactions, but has never been studied in premature neonates. In this study, we use heart rate variability (HRV), which has been described as the "primary output" of the CAN, and resting state functional MRI to characterize brain-heart relationships in premature neonates.

METHODS

We studied premature neonates who underwent resting state functional MRI (rsfMRI) at term, (37-weeks postmenstrual age [PMA] or above) and had HRV data recorded during the same week of their MRI. HRV was derived from continuous electrocardiogram data during the week of the rsfMRI scan. For rsfMRI, a seed-based approach was used to define regions of interest (ROI) pertinent to the CAN, and blood oxygen level-dependent signal was correlated between each ROI as a measure of functional connectivity. HRV was correlated with CAN connectivity (CANconn) for each region, and sub-group analysis was performed based on sex and clinical comorbidities.

RESULTS

Forty-seven premature neonates were included in this study, with a mean gestational age at birth of 28.1 +/- 2.6 weeks. Term CANconn was found to be significantly correlated with HRV in approximately one-fifth of CAN connections. Two distinct patterns emerged among these HRV-CANconn relationships. In the first, increased HRV was associated with stronger CANconn of limbic regions. In the second pattern, stronger CANconn at the precuneus was associated with impaired HRV maturation. These patterns were especially pronounced in male premature neonates.

CONCLUSION

We report for the first time evidence of brain-heart relationships in premature neonates and an emerging CAN, most striking in male neonates, suggesting that the brain-heart axis may be more vulnerable in male premature neonates. Signatures in the heart rate may eventually become an important non-invasive tool to identify premature males at highest risk for neurodevelopmental impairment.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants

Objective

Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from > 700 extremely preterm infants to identify physiologic features that predict respiratory outcomes. We calculated a subset of 33 HCTSA features on > 7M 10-minute windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on > 3500 HCTSA algorithms. Performance of each feature was measured by individual area under the receiver operating curve (AUC) at various days of life and binary respiratory outcomes. These were compared to optimal PreVent physiologic predictor IH90 DPE, the duration per event of intermittent hypoxemia events with threshold of 90%.

Main Results

The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

Neonatal Autonomic Regulation as a Predictor of Autism Spectrum Disorder in Very Preterm Infants

Infants born preterm are at a significantly higher likelihood of having autism spectrum disorder (ASD). Preterm birth and ASD are both associated with neurological differences, notably autonomic nervous system (ANS) dysfunction, pointing to preterm ANS dysfunction as a potential pathway to ASD, particularly in VPT infants. In this study, a subset of very preterm (VPT) infants enrolled in a large, multisite clinical trial were enrolled in this study at birth (N=20). Continuous measures of minute-by-minute thermal gradients, defined by the difference between central and peripheral temperatures, and hour-by-hour abnormal heart rate characteristics (HRCs) were collected from birth-28 days (>40,000 samples/infant). Following NICU discharge, standardized measures of cognition, language, and motor skills were collected at adjusted ages 6, 9, and 12 months. At 12 months, assessments of social communication and early ASD symptoms were administered. Results suggest significant ASD concerns for half of the sample by 12 months of age. Neonatal abnormal HRCs were strongly associated with 12-month ASD symptoms (r=0.81, p<.01), as was birth gestational age (GA), birth weight (BW), and abnormal negative thermal gradients. ANS measures collected in the first month of neonatal life, more than a year prior to the ASD evaluation, were surprisingly strong predictors of ASD. This study highlights complementary ANS measures that describe how ANS dysfunction, likely resulting from an imbalance between the parasympathetic and sympathetic systems, may impact very early regulatory processes for neonates who later develop ASD. This finding offers a promising avenue for researching ANS-related etiological mechanisms and biomarkers of ASD.

Heart rate patterns predicting cerebral palsy in preterm infants

BACKGROUND

Heart rate (HR) patterns can inform on central nervous system dysfunction. We previously used highly comparative time series analysis (HCTSA) to identify HR patterns predicting mortality among patients in the neonatal intensive care unit (NICU) and now use this methodology to discover patterns predicting cerebral palsy (CP) in preterm infants.

METHOD

We studied NICU patients <37 weeks' gestation with archived every-2-s HR data throughout the NICU stay and with or without later diagnosis of CP (n = 57 CP and 1119 no CP). We performed HCTSA of >2000 HR metrics and identified 24 metrics analyzed on HR data from two 7-day periods: week 1 and 37 weeks' postmenstrual age (week 1, week 37). Multivariate modeling was used to optimize a parsimonious prediction model.

RESULTS

Week 1 HR metrics with maximum AUC for CP prediction reflected low variability, including "RobustSD" (AUC 0.826; 0.772-0.870). At week 37, high values of a novel HR metric, "LongSD3," the cubed value of the difference in HR values 100 s apart, were added to week 1 HR metrics for CP prediction. A combined birthweight + early and late HR model had AUC 0.853 (0.805-0.892).

CONCLUSIONS

Using HCTSA, we discovered novel HR metrics and created a parsimonious model for CP prediction in preterm NICU patients.

IMPACT

We discovered new heart rate characteristics predicting CP in preterm infants. Using every-2-s HR from two 7-day periods and highly comparative time series analysis, we found a measure of low variability HR week 1 after birth and a pattern of recurrent acceleration in HR at term corrected age that predicted CP. Combined clinical and early and late HR features had AUC 0.853 for CP prediction.