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

Blackard, Kaitlin R.; Krahn, Katy N.; Andris, Robert T; Lake, Douglas E.; Fairchild, Karen D.

doi:10.1038/s41390-021-01381-1

Cited by 8 publications

(4 citation statements)

References 43 publications

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“…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. Existing research points to ANS dysfunction in ASD that can be characterized by an imbalance – sympathetic overactivation and/or parasympathetic underactivation 9,10 . The strong association between ASD features and abnormal HRCs and, albeit to a lesser extent, abnormal thermal gradients, represents novel evidence contributing to an emerging theoretical model in which ANS imbalance in very early infancy may be an underlying etiological feature of ASD, particularly in VPT infants.…”

Section: Discussionmentioning

confidence: 99%

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

Bradshaw,

O’Reilly,

Everhart

et al. 2023

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

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

Bradshaw,

O’Reilly,

Everhart

et al. 2023

Preprint

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“…Models have also been developed for other adverse outcomes during NICU stay including death (Sullivan et al 2016, Niestroy et al 2022, bronchopulmonary dysplasia (BPD) (Raffay et al 2019, Ramanand et al 2023, and retinopathy of prematurity (Di Fiore et al 2010, 2012. Vital sign data during an infant's stay has also been shown to predict long-term outcomes of cognitive impairment (Poets 2015), cerebral palsy (Letzkus et al 2022), and autism (Blackard et al 2021).…”

Section: Highly Comparative Time Series Analysis (Hctsa)mentioning

confidence: 99%

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

Qiu,

Di Fiore,

Krishnamurthi

et al. 2024

Physiol. Meas.

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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 PreVent 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. 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). 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.

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“…Models have also been developed for other adverse outcomes during NICU stay including death (Sullivan et al 2016, 2018, Niestroy et al 2022), bronchopulmonary dysplasia (BPD) (Raffay et al 2019, Gentle et al 2023, Ramanand et al 2023), and retinopathy of prematurity (Di Fiore et al 2010, 2012). Vital sign data during an infant’s stay has also been shown to predict long-term outcomes of cognitive impairment (Poets et al 2015), cerebral palsy (Letzkus et al 2022), and autism (Blackard et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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

Qiu,

Di Fiore,

Krishnamurthi

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. 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.

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

Cited by 8 publications

References 43 publications

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

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

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

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

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