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.

Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Infants Born Extremely Preterm.

OBJECTIVE: The objective of this study was to examine the association of cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, with late-onset sepsis for extremely preterm infants (<29 weeks of gestational age) on vs off invasive mechanical ventilation. STUDY DESIGN: This is a retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in 5 level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean gestational age: 26.4 weeks, SD 1.71). Monitoring data were available and analyzed for 719 infants (47 512 patient-days); of whom, 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72 hours after birth and ≥5-day antibiotics). RESULTS: For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer events with oxygen saturation <80% (IH80) and more bradycardia events before sepsis. IH events were associated with higher sepsis risk but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model including postmenstrual age, cardiorespiratory variables (apnea, periodic breathing, IH80, and bradycardia), and ventilator status predicted sepsis with an area under the receiver operator characteristic curve of 0.783. CONCLUSION: We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis.

Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Extremely Preterm Infants.

Objectives: Detection of changes in cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, may facilitate earlier detection of sepsis. Our objective was to examine the association of cardiorespiratory events with late-onset sepsis for extremely preterm infants (<29 weeks' gestational age (GA)) on versus off invasive mechanical ventilation. Study Design: Retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in five level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean GA 26.4w, SD 1.71). Monitoring data were available and analyzed for 719 infants (47,512 patient-days), of whom 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72h after birth and ≥5d antibiotics). Results: For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer IH80 events and more bradycardia events before sepsis. IH events were associated with higher sepsis risk, but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model predicted sepsis with an AUC of 0.783. Conclusion: We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis.

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.

Computer-aided diagnostic screen for Congenital Central Hypoventilation Syndrome with facial phenotype.

BACKGROUND: Congenital Central Hypoventilation Syndrome (CCHS) has devastating consequences if not diagnosed promptly. Despite identification of the disease-defining gene PHOX2B and a facial phenotype, CCHS remains underdiagnosed. This study aimed to incorporate automated techniques on facial photos to screen for CCHS in a diverse pediatric cohort to improve early case identification and assess a facial phenotype-PHOX2B genotype relationship. METHODS: Facial photos of children and young adults with CCHS were control-matched by age, sex, race/ethnicity. After validating landmarks, principal component analysis (PCA) was applied with logistic regression (LR) for feature attribution and machine learning models for subject classification and assessment by PHOX2B pathovariant. RESULTS: Gradient-based feature attribution confirmed a subtle facial phenotype and models were successful in classifying CCHS: neural network performed best (median sensitivity 90% (IQR 84%, 95%)) on 179 clinical photos (versus LR and XGBoost, both 85% (IQR 75-76%, 90%)). Outcomes were comparable stratified by PHOX2B genotype and with the addition of publicly available CCHS photos (n = 104) using PCA and LR (sensitivity 83-89% (IQR 67-76%, 92-100%). CONCLUSIONS: Utilizing facial features, findings suggest an automated, accessible classifier may be used to screen for CCHS in children with the phenotype and support providers to seek PHOX2B testing to improve the diagnostics. IMPACT: Facial landmarking and principal component analysis on a diverse pediatric and young adult cohort with PHOX2B pathovariants delineated a distinct, subtle CCHS facial phenotype. Automated, low-cost machine learning models can detect a CCHS facial phenotype with a high sensitivity in screening to ultimately refer for disease-defining PHOX2B testing, potentially addressing gaps in disease underdiagnosis and allow for critical, timely intervention.

Maturation of cardioventilatory physiological trajectories in extremely preterm infants.

BACKGROUND: In extremely preterm infants, persistence of cardioventilatory events is associated with long-term morbidity. Therefore, the objective was to characterize physiologic growth curves of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants during the first few months of life. METHODS: The Prematurity-Related Ventilatory Control study included 717 preterm infants <29 weeks gestation. Waveforms were downloaded from bedside monitors with a novel sharing analytics strategy utilized to run software locally, with summary data sent to the Data Coordinating Center for compilation. RESULTS: Apnea, periodic breathing, and intermittent hypoxemia events rose from day 3 of life then fell to near-resolution by 8-12 weeks of age. Apnea/intermittent hypoxemia were inversely correlated with gestational age, peaking at 3-4 weeks of age. Periodic breathing was positively correlated with gestational age peaking at 31-33 weeks postmenstrual age. Females had more periodic breathing but less intermittent hypoxemia/bradycardia. White infants had more apnea/periodic breathing/intermittent hypoxemia. Infants never receiving mechanical ventilation followed similar postnatal trajectories but with less apnea and intermittent hypoxemia, and more periodic breathing. CONCLUSIONS: Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. IMPACT: Physiologic curves of cardiorespiratory events in extremely preterm-born infants offer (1) objective measures to assess individual patient courses and (2) guides for research into control of ventilation, biomarkers and outcomes. Presented are updated maturational trajectories of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in 717 infants born <29 weeks gestation from the multi-site NHLBI-funded Pre-Vent study. Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. Different time courses for apnea and periodic breathing suggest different maturational mechanisms.

Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring.

The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.

Assessment of comorbid symptoms in pediatric autonomic dysfunction.

PURPOSE: Pediatric patients with autonomic dysfunction and orthostatic intolerance (OI) often present with co-existing symptoms and signs that might or might not directly relate to the autonomic nervous system. Our objective was to identify validated screening instruments to characterize these comorbidities and their impact on youth functioning. METHODS: The Pediatric Assembly of the American Autonomic Society reviewed the current state of practice for identifying symptom comorbidities in youth with OI. The assembly includes physicians, physician-scientists, scientists, advanced practice providers, psychologists, and a statistician with expertise in pediatric disorders of OI. A total of 26 representatives from the various specialties engaged in iterative meetings to: (1) identify and then develop consensus on the symptoms to be assessed, (2) establish committees to review the literature for screening measures by member expertise, and (3) delineate the specific criteria for systematically evaluating the measures and for making measure recommendations by symptom domains. RESULTS: We review the measures evaluated and recommend one measure per system/concern so that assessment results from unrelated clinical centers are comparable. We have created a repository to apprise investigators of validated, vetted assessment tools to enhance comparisons across cohorts of youth with autonomic dysfunction and OI. CONCLUSION: This effort can facilitate collaboration among clinical settings to advance the science and clinical treatment of these youth. This effort is essential to improving management of these vulnerable patients as well as to comparing research findings from different centers.

Simple, miniaturized biosensors for wireless mapping of thermoregulatory responses.

Temperature is the most commonly collected vital sign in all of clinical medicine; it plays a critical role in care decisions related to topics ranging from infection to inflammation, sleep, and fertility. Most assessments of body temperature occur at isolated anatomical locations (e.g. axilla, rectum, temporal artery, or oral cavity). Even this relatively primitive mode for monitoring can be challenging with vulnerable patient populations due to physical encumbrances and artifacts associated with the sizes, weights, shapes and mechanical properties of the sensors and, for continuous monitoring, their hard-wired interfaces to data collection units. Here, we introduce a simple, miniaturized, lightweight sensor as a wireless alternative, designed to address demanding applications such as those related to the care of neonates in high ambient humidity environments with radiant heating found in incubators in intensive care units. Such devices can be deployed onto specific anatomical locations of premature infants for homeostatic assessments. The estimated core body temperature aligns, to within 0.05 °C, with clinical grade, wired sensors, consistent with regulatory medical device requirements. Time-synchronized, multi-device operation across multiple body locations supports continuous, full-body measurements of spatio-temporal variations in temperature and additional modes of determining tissue health status in the context of sepsis detection and various environmental exposures. In addition to thermal sensing, these same devices support measurements of a range of other essential vital signs derived from thermo-mechanical coupling to the skin, for applications ranging from neonatal and infant care to sleep medicine and even pulmonary medicine.

A narrative review of the mechanisms and consequences of intermittent hypoxia and the role of advanced analytic techniques in pediatric autonomic disorders.

Disorders of autonomic functions are typically characterized by disturbances in multiple organ systems. These disturbances are often comorbidities of common and rare diseases, such as epilepsy, sleep apnea, Rett syndrome, congenital heart disease or mitochondrial diseases. Characteristic of many autonomic disorders is the association with intermittent hypoxia and oxidative stress, which can cause or exaggerate a variety of other autonomic dysfunctions, making the treatment and management of these syndromes very complex. In this review we discuss the cellular mechanisms by which intermittent hypoxia can trigger a cascade of molecular, cellular and network events that result in the dysregulation of multiple organ systems. We also describe the importance of computational approaches, artificial intelligence and the analysis of big data to better characterize and recognize the interconnectedness of the various autonomic and non-autonomic symptoms. These techniques can lead to a better understanding of the progression of autonomic disorders, ultimately resulting in better care and management.

Analysis and comparisons of gene expression changes in patient- derived neurons from ROHHAD, CCHS, and PWS.

Background: Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) syndrome is an ultra-rare neurocristopathy with no known genetic or environmental etiology. Rapid-onset obesity over a 3-12 month period with onset between ages 1.5-7 years of age is followed by an unfolding constellation of symptoms including severe hypoventilation that can lead to cardiorespiratory arrest in previously healthy children if not identified early and intervention provided. Congenital Central Hypoventilation syndrome (CCHS) and Prader-Willi syndrome (PWS) have overlapping clinical features with ROHHAD and known genetic etiologies. Here we compare patient neurons from three pediatric syndromes (ROHHAD, CCHS, and PWS) and neurotypical control subjects to identify molecular overlap that may explain the clinical similarities. Methods: Dental pulp stem cells (DPSC) from neurotypical control, ROHHAD, and CCHS subjects were differentiated into neuronal cultures for RNA sequencing (RNAseq). Differential expression analysis identified transcripts variably regulated in ROHHAD and CCHS vs. neurotypical control neurons. In addition, we used previously published PWS transcript data to compare both groups to PWS patient-derived DPSC neurons. Enrichment analysis was performed on RNAseq data and downstream protein expression analysis was performed using immunoblotting. Results: We identified three transcripts differentially regulated in all three syndromes vs. neurotypical control subjects. Gene ontology analysis on the ROHHAD dataset revealed enrichments in several molecular pathways that may contribute to disease pathology. Importantly, we found 58 transcripts differentially expressed in both ROHHAD and CCHS patient neurons vs. control neurons. Finally, we validated transcript level changes in expression of ADORA2A, a gene encoding for an adenosine receptor, at the protein level in CCHS neurons and found variable, although significant, changes in ROHHAD neurons. Conclusions: The molecular overlap between CCHS and ROHHAD neurons suggests that the clinical phenotypes in these syndromes likely arise from or affect similar transcriptional pathways. Further, gene ontology analysis identified enrichments in ATPase transmembrane transporters, acetylglucosaminyltransferases, and phagocytic vesicle membrane proteins that may contribute to the ROHHAD phenotype. Finally, our data imply that the rapid-onset obesity seen in both ROHHAD and PWS likely arise from different molecular mechanisms. The data presented here describes important preliminary findings that warrant further validation.

Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD): a collaborative review of the current understanding.

PURPOSE: To provide an overview of the discovery, presentation, and management of Rapid-onset Obesity with Hypothalamic dysfunction, Hypoventilation, and Autonomic Dysregulation (ROHHAD). To discuss a search for causative etiology spanning multiple disciplines and continents. METHODS: The literature (1965-2022) on the diagnosis, management, pathophysiology, and potential etiology of ROHHAD was methodically reviewed. The experience of several academic centers with expertise in ROHHAD is presented, along with a detailed discussion of scientific discovery in the search for a cause. RESULTS: ROHHAD is an ultra-rare syndrome with fewer than 200 known cases. Although variations occur, the acronym ROHHAD is intended to alert physicians to the usual sequence or unfolding of the phenotypic presentation, including the full phenotype. Nearly 60 years after its first description, more is known about the pathophysiology of ROHHAD, but the etiology remains enigmatic. The search for a genetic mutation common to patients with ROHHAD has not, to date, demonstrated a disease-defining gene. Similarly, a search for the autoimmune basis of ROHHAD has not resulted in a definitive answer. This review summarizes current knowledge and potential future directions. CONCLUSION: ROHHAD is a poorly understood, complex, and potentially devastating disorder. The search for its cause intertwines with the search for causes of obesity and autonomic dysregulation. The care for the patient with ROHHAD necessitates collaborative international efforts to advance our knowledge and, thereby, treatment, to decrease the disease burden and eventually to stop, and/or reverse the unfolding of the phenotype.

Cardiorespiratory Monitoring Data to Predict Respiratory Outcomes in Extremely Preterm Infants.

Rationale: Immature control of breathing is associated with apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants. However, it is not clear if such events independently predict worse respiratory outcome. Objectives: To determine if analysis of cardiorespiratory monitoring data can predict unfavorable respiratory outcomes at 40 weeks postmenstrual age (PMA) and other outcomes, such as bronchopulmonary dysplasia at 36 weeks PMA. Methods: The Prematurity-related Ventilatory Control (Pre-Vent) study was an observational multicenter prospective cohort study including infants born at <29 weeks of gestation with continuous cardiorespiratory monitoring. The primary outcome was either "favorable" (alive and previously discharged or inpatient and off respiratory medications/O2/support at 40 wk PMA) or "unfavorable" (either deceased or inpatient/previously discharged on respiratory medications/O2/support at 40 wk PMA). Measurements and Main Results: A total of 717 infants were evaluated (median birth weight, 850 g; gestation, 26.4 wk), 53.7% of whom had a favorable outcome and 46.3% of whom had an unfavorable outcome. Physiologic data predicted unfavorable outcome, with accuracy improving with advancing age (area under the curve, 0.79 at Day 7, 0.85 at Day 28 and 32 wk PMA). The physiologic variable that contributed most to prediction was intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <90%. Models with clinical data alone or combining physiologic and clinical data also had good accuracy, with areas under the curve of 0.84-0.85 at Days 7 and 14 and 0.86-0.88 at Day 28 and 32 weeks PMA. Intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <80% was the major physiologic predictor of severe bronchopulmonary dysplasia and death or mechanical ventilation at 40 weeks PMA. Conclusions: Physiologic data are independently associated with unfavorable respiratory outcome in extremely preterm infants.

Creating a data dictionary for pediatric autonomic disorders.

PURPOSE: Whether evaluating patients clinically, documenting care in the electronic health record, performing research, or communicating with administrative agencies, the use of a common set of terms and definitions is vital to ensure appropriate use of language. At a 2017 meeting of the Pediatric Section of the American Autonomic Society, it was determined that an autonomic data dictionary comprising aspects of evaluation and management of pediatric patients with autonomic disorders would be an important resource for multiple stakeholders. METHODS: Our group created the list of terms for the dictionary. Definitions were prioritized to be obtained from established sources with which to harmonize. Some definitions needed mild modification from original sources. The next tier of sources included published consensus statements, followed by Internet sources. In the absence of appropriate sources, we created a definition. RESULTS: A total of 589 terms were listed and defined in the dictionary. Terms were organized by Signs/Symptoms, Triggers, Co-morbid Disorders, Family History, Medications, Medical Devices, Physical Examination Findings, Testing, and Diagnoses. CONCLUSION: Creation of this data dictionary becomes the foundation of future clinical care and investigative research in pediatric autonomic disorders, and can be used as a building block for a subsequent adult autonomic data dictionary.

Ventilatory and Orthostatic Challenges Reveal Biomarkers for Neurocognition in Children and Young Adults With Congenital Central Hypoventilation Syndrome.

BACKGROUND: Children and young adults with congenital central hypoventilation syndrome (CCHS) are at risk of cognitive deficits. They experience autonomic dysfunction and chemoreceptor insensitivity measured during ventilatory and orthostatic challenges, but relationships between these features are undefined. RESEARCH QUESTION: Can a biomarker be identified from physiologic responses to ventilatory and orthostatic challenges that is related to neurocognitive outcomes in CCHS? STUDY DESIGN AND METHODS: This retrospective study included 25 children and young adults with CCHS tested over an inpatient stay. Relationships between physiologic measurements during hypercarbic and hypoxic ventilatory challenges, hypoxic ventilatory challenges, and orthostatic challenges and neurocognitive outcomes (by Wechsler intelligence indexes) were examined. Independent variable inclusion was determined by significant associations in Pearson's analyses. Multivariate linear regressions were used to assess relationships between measured physiologic responses to challenges and neurocognitive scores. RESULTS: Significant relationships were identified between areas of fluid intelligence and measures of oxygen saturation (SpO2) and heart rate (HR) during challenges. Specifically, perceptual reasoning was related to HR (adjusted regression [ß] coefficient, -0.68; 95% CI, 1.24 to -0.12; P = .02) during orthostasis. Working memory was related to change in HR (ß, -1.33; 95% CI, -2.61 to -0.05; P = .042) during the hypoxic ventilatory challenge. Processing speed was related to HR (ß, -1.19; 95% CI, -1.93 to -0.46; P = .003) during orthostasis, to baseline SpO2 (hypercarbic and hypoxic ß, 8.57 [95% CI, 1.63-15.51]; hypoxic ß, 8.37 [95% CI, 3.65-13.11]; P = .002 for both) during the ventilatory challenges, and to intrachallenge SpO2 (ß, 5.89; 95% CI, 0.71-11.07; P = .028) during the hypoxic ventilatory challenge. INTERPRETATION: In children and young adults with CCHS, SpO2 and HR-or change in HR-at rest and as a response to hypoxia and orthostasis are related to cognitive outcomes in domains of known risk, particularly fluid reasoning. These findings can guide additional research on the usefulness of these as biomarkers in understanding the impact of daily physical stressors on neurodevelopment in this high-risk group.

Transitional care and clinical management of adolescents, young adults, and suspected new adult patients with congenital central hypoventilation syndrome.

PURPOSE: With contemporaneous advances in congenital central hypoventilation syndrome (CCHS), recognition, confirmatory diagnostics with PHOX2B genetic testing, and conservative management to reduce the risk of early morbidity and mortality, the prevalence of identified adolescents and young adults with CCHS and later-onset (LO-) CCHS has increased. Accordingly, there is heightened awareness and need for transitional care of these patients from pediatric medicine into a multidisciplinary adult medical team. Hence, this review summarizes key clinical and management considerations for patients with CCHS and LO-CCHS and emphasizes topics of particular importance for this demographic. METHODS: We performed a systematic review of literature on diagnostics, pathophysiology, and clinical management in CCHS and LO-CCHS, and supplemented the review with anecdotal but extensive experiences from large academic pediatric centers with expertise in CCHS. RESULTS: We summarized our findings topically for an overview of the medical care in CCHS and LO-CCHS specifically applicable to adolescents and adults. Care topics include genetic and embryologic basis of the disease, clinical presentation, management, variability in autonomic nervous system dysfunction, and clarity regarding transitional care with unique considerations such as living independently, family planning, exposure to anesthesia, and alcohol and drug use. CONCLUSIONS: While a lack of experience and evidence exists in the care of adults with CCHS and LO-CCHS, a review of the relevant literature and expert consensus provides guidance for transitional care areas.

Neurocognition as a biomarker in the rare autonomic disorders of CCHS and ROHHAD.

PURPOSE: Congenital central hypoventilation syndrome (CCHS) and rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) are rare disorders of autonomic regulation with risk for disrupted neurocognitive development. Our aim is to summarize research on neurocognitive outcomes in these conditions, advance understanding of how to best support these individuals throughout development, and facilitate future research. METHODS: We conducted a narrative review of literature on neurocognitive outcomes in CCHS and ROHHAD, supplemented with previously unpublished data from patients with CCHS and ROHHAD at our Center for Autonomic Medicine in Pediatrics (CAMP). RESULTS: Individuals with CCHS and ROHHAD experience a wide range of neurocognitive functioning ranging from above average to below average, but are at particular risk for difficulties with working memory, processing speed, perceptual reasoning, and visuographic skills. An assessment framework emphasizing fluid cognition seems especially appropriate for these conditions. Owing to small cohorts and varied methods of data collection, it has been difficult to identify associations between disease factors (including CCHS PHOX2B genotypes) and cognitive outcomes. However, results suggest that early childhood is a period of particular vulnerability, perhaps due to the disruptive impact of recurrent intermittent hypoxic episodes on brain and cognitive development. CONCLUSION: Neurocognitive monitoring is recommended as a component of routine clinical care in CCHS and ROHHAD as a marker of disease status and to ensure that educational support and disability accommodations are provided as early as possible. Collaborative efforts will be essential to obtain samples needed to enhance our understanding of neurocognitive outcomes in CCHS and ROHHAD.

Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children.

Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes existing evidence on novel approaches to continuous cardiorespiratory monitoring in hospitalized children. We aimed to identify opportunities for the development of monitoring technology and the use of machine learning to analyze continuous physiological data to improve the outcomes of hospitalized children. We included original research articles published on or after January 1, 2001, involving novel approaches to collect and use continuous cardiorespiratory physiological data in hospitalized children. OVID Medline, PubMed, and Embase databases were searched. We screened 2909 articles and performed full-text extraction of 105 articles. We identified 58 articles describing novel devices or approaches, which were generally small and single-center. In addition, we identified 47 articles that described the use of continuous physiological data in prediction models, but only 7 integrated multidimensional data (e.g., demographics, laboratory results). We identified three areas for development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using continuous cardiorespiratory data. IMPACT: We performed a comprehensive scoping review of novel approaches to capture and use continuous cardiorespiratory physiological data for monitoring, diagnosis, providing care, and predicting events in hospitalized infants and children, from novel devices to machine learning-based prediction models. We identified three key areas for future development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using cardiorespiratory data.