Early Pulse Oximetry Data Improves Prediction of Death and Adverse Outcomes in a Two-Center Cohort of Very Low Birth Weight Infants

Sullivan, Brynne A.; Wallman-Stokes, Aaron; Isler, Joseph R.; Sahni, Rakesh; Moorman, J. Randall; Fairchild, Karen D.; Lake, Douglas E.

doi:10.1055/s-0038-1654712

Cited by 40 publications

(28 citation statements)

References 15 publications

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“…18 A growing body of research suggests that analysis of subtle changes in vital sign patterns can help clinicians diagnose diseases such as sepsis and necrotising enterocolitis earlier in newborns. 10,19,20 Recently, the semi-manually calculated, low frequency neonatal Sequential Organ Failure Assessment (nSOFA) score that evaluates respiratory support, SpO 2 , laboratory values and need for medication, provided an AUROC for mortality of 0.91. 21 The usefulness of a clinical decision support system based on HRC was shown in a large randomised clinical trial including about 3000 VLBW infants.…”

Section: Combination Of Several Vital Sign Features and Demographic F...mentioning

confidence: 99%

Vital sign‐based detection of sepsis in neonates using machine learning

et al. 2023

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Aim: Sepsis is a leading cause of morbidity and mortality in neonates. Early diagnosis is key but difficult due to non-specific signs. We investigate the predictive value of machine learning-assisted analysis of non-invasive, high frequency monitoring data and demographic factors to detect neonatal sepsis.Methods: Single centre study, including a representative cohort of 325 infants (2866 hospitalisation days). Personalised event timelines including interventions and clinical findings were generated. Time-domain features from heart rate, respiratory rate and oxygen saturation values were calculated and demographic factors included. Sepsis prediction was performed using Naïve Bayes algorithm in a maximum a posteriori framework up to 24 h before clinical sepsis suspicion.Results: Twenty sepsis cases were identified. Combining multiple vital signs improved algorithm performance compared to heart rate characteristics alone. This enabled a prediction of sepsis with an area under the receiver operating characteristics curve of 0.82, up to 24 h before clinical sepsis suspicion. Moreover, 10 h prior to clinical suspicion, the risk of sepsis increased 150-fold. Conclusion:The present algorithm using non-invasive patient data provides useful predictive value for neonatal sepsis detection. Machine learning-assisted algorithms are promising novel methods that could help individualise patient care and reduce morbidity and mortality.

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Section: Combination Of Several Vital Sign Features and Demographic F...mentioning

confidence: 99%

Vital sign‐based detection of sepsis in neonates using machine learning

et al. 2023

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“…Despite continuous monitoring to assure high care standards, much of this collected information remains unused. This situation is worth noting since integrating these vital signs could improve the predictive power of the NICU monitors and better help reduce infants' mortality (Sullivan et al, 2018;Joshi et al, 2020;Kumar et al, 2020).…”

Section: A Technological Analysis To Efficiently Monitor and Use Hrv mentioning

confidence: 99%

“…A further crucial step is to integrate HRV signals with other vital signs, such as SpO 2 and blood pressure, to improve its predictive power and reduce mortality (Sullivan et al, 2018;Kumar et al, 2020). Even further is the integration of information about the infant's gestation, labor, and perinatal period is essential: since NICU professionals have to deal with complex pathologies, including SIDS (Siren, 2017), technological tools that can elaborate all these types of information could become paramount to improve clinical care (Hemphill et al, 2011).…”

Section: The Future Perspectives For the Clinical Use Of Hrv In The Nmentioning

confidence: 99%

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Chiera¹,

Cerritelli²,

Casini³

et al. 2020

Front. Neurosci.

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Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often timeconsuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a noninvasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby's overall wellbeing and neurological development to detect stress-/painrelated behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby's stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant's HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant's clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients' health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

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“…Comprehensive analysis of HR requires specialized equipment and systems for collecting, storing, and analyzing bedside monitor data. The University of Virginia (UVa) NICU has had such a system in place for the past decade, enabling us to analyze cardiorespiratory patterns and develop algorithms to predict various adverse outcomes ( 27 ) ( 28 ) ( 29 ), including intracranial hemorrhage ( 30 ) and hypoxic-ischemic brain injury ( 31 ). In the current study, we aimed to identify HR patterns of preterm and term NICU patients later diagnosed with ASD.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Background: Neonatal Intensive Care Unit (NICU) patients are at increased risk for autism spectrum disorder (ASD). Autonomic nervous system aberrancy has been described in children with ASD, and we aimed to identify heart rate (HR) patterns in NICU patients associated with eventual ASD diagnosis. Methods: This retrospective cohort study included NICU patients from 2009-2016 with archived HR data and follow-up beyond age 3 years. Medical records provided clinical variables and ASD diagnosis. HR data were compared in infants with and without ASD. Results: Of 2371 patients, 88 had ASD, and 689,016 hours of data were analyzed. HR skewness (HRskw) was significantly different between ASD and control infants. Preterm infants at early postmenstrual ages (PMA) had negative HRskw reflecting decelerations, which increased with maturation. From 34-42 weeks PMA, positive HRskw toward accelerations was higher in males with ASD. In 931 males with at least 4 days of HR data, overall ASD prevalence was 5%, whereas 11% in the top 5 th HRskw percentile had ASD. Conclusion: High HRskw in NICU males, perhaps representing autonomic imbalance, was associated with increased ASD risk. Further study is needed to determine whether HR analysis identifies highest risk infants who might benefit from earlier screening and therapies.

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Early Pulse Oximetry Data Improves Prediction of Death and Adverse Outcomes in a Two-Center Cohort of Very Low Birth Weight Infants

Abstract: Analysis of early HR-SpO characteristics adds to clinical risk factors to predict later adverse outcomes in VLBW infants.

Cited by 40 publications

References 15 publications

Vital sign‐based detection of sepsis in neonates using machine learning

Vital sign‐based detection of sepsis in neonates using machine learning

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

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

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