Disparities in Telemedicine Use Among Children Seen in Surgical Clinics During the COVID-19 Pandemic: Experience of One Tertiary Care Freestanding Children's Hospital.

Background: Telemedicine use dramatically increased during the COVID-19 pandemic. However, the effects of telemedicine on pre-existing disparities in pediatric surgical access have not been well described. We describe our center's early experience with telemedicine and disparities in patients' access to outpatient surgical care. Methods: A retrospective study of outpatient visits within all surgical divisions from May to December 2020 was conducted. We assessed the rates of scheduled telemedicine visits during that period, as well as the rate of completing a visit after it has been scheduled. Descriptive and logistic regression analyses were used to test for associations between these rates and patient characteristics. Results: Over the study period, 109,601 visits were scheduled. Telemedicine accounted for 6.1% of all visits with lower cancellation rates than in-person visits (26.9% vs. 34.7%). More scheduled telemedicine encounters were observed for older patients, White, English speakers, those with private insurance, and those living in rural areas. Lower odds of telemedicine visit completion were observed among patients with public insurance (odds ratio [OR] 0.7, 95% confidence interval [CI] 0.64-0.77), Spanish language preference (OR 0.84, 95% CI 0.72-0.97), and those living in rural areas (OR 0.73, 95% CI 0.64-0.84). In contrast, higher odds of telemedicine visit completion were associated with a higher Social Deprivation Index score (OR 1.41, 95% CI 1.27-1.58). Telemedicine visit completion was also associated with increasing community-level income and distance from the hospital. Conclusions: Telemedicine use for outpatient surgical care was generally low during the peak of the pandemic, and certain populations were less likely to utilize it. These findings call for further action to bridge gaps in telemedicine use.

Artificial intelligence-based clinical decision support in pediatrics.

Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.

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.

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.

Cerebral Autoregulation during Orthostatic Challenge in Congenital Central Hypoventilation Syndrome.

Rationale: Congenital central hypoventilation syndrome (CCHS) is a rare autonomic disorder with altered regulation of breathing, heart rate (HR), and blood pressure (BP). Aberrant cerebral oxygenation in response to hypercapnia/hypoxia in CCHS raises the concern that altered cerebral autoregulation may contribute to CCHS-related, variably impaired neurodevelopment. Objectives: To evaluate cerebral autoregulation in response to orthostatic challenge in CCHS cases versus controls. Methods: CCHS and age- and sex-matched control subjects were studied with head-up tilt (HUT) testing to induce orthostatic stress. Fifty CCHS and 100 control HUT recordings were included. HR, BP, and cerebral oxygen saturation (regional oxygen saturation) were continuously monitored. The cerebral oximetry index (COx), a real-time measure of cerebral autoregulation based on these measures, was calculated. Measurements and Main Results: HUT resulted in a greater mean BP decrease from baseline in CCHS versus controls (11% vs. 6%; P < 0.05) and a diminished increase in HR in CCHS versus controls (11% vs. 18%; P < 0.01) in the 5 minutes after tilt-up. Despite a similar COx at baseline, orthostatic provocation within 5 minutes of tilt-up caused a 50% greater increase in COx (P < 0.01) and a 29% increase in minutes of impaired autoregulation (P < 0.02) in CCHS versus controls (4.0 vs. 3.1 min). Conclusions: Cerebral autoregulatory mechanisms appear to be intact in CCHS, but the greater hypotension observed in CCHS consequent to orthostatic provocation is associated with greater values of COx/impaired autoregulation when BP is below the lower limits of autoregulation. Effects of repeated orthostatic challenges in everyday living in CCHS necessitate further study to determine their influence on neurodevelopmental disease burden.

Association Between Heart Rate Variability and Inflammatory Biomarkers in Critically Ill Children.

OBJECTIVES: The autonomic nervous system (ANS) can both modulate and be modulated by the inflammatory response during critical illness. We aimed to determine whether heart rate variability (HRV), a measure of ANS function, is associated with proinflammatory biomarker levels in critically ill children. DESIGN: Two cohorts were analyzed. The first was a prospective observational cohort from August 2018 to August 2020 who had plasma proinflammatory cytokine measurements within 72 hours of admission, including tumor necrosis factor-α, interleukin (IL)-1ß, IL-6, and IL-8. The second was a retrospective cohort from June 2012 to August 2020 who had at least one C-reactive protein (CRP) measurement within 72 hours of admission. SETTING: Forty-six-bed PICU. PATIENTS: Critically ill children in either cohort who had continuous heart rate data available from the bedside monitors. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Sixty-two patients were included in the prospective cohort and 599 patients in the retrospective cohort. HRV was measured using the age-adjusted integer heart rate variability (HRVi), which is the sd of the heart rate sampled every 1 second over 5 consecutive minutes. The median HRVi was measured in the 12-hour period ending 30 minutes prior to inflammatory biomarker collection. HRVi was inversely correlated with IL-6, IL-8, and CRP levels (p ≤ 0.02); correlation with IL-8 and CRP persisted after adjusting for Pediatric Risk of Mortality III and age, and median HR and age (p < 0.001). CONCLUSIONS: HRVi is inversely correlated with IL-6, IL-8, and CRP. Further studies are needed to validate this measure as a proxy for a proinflammatory state.

The Pathophysiology of Rett Syndrome With a Focus on Breathing Dysfunctions.

Rett syndrome (RTT), an X-chromosome-linked neurological disorder, is characterized by serious pathophysiology, including breathing and feeding dysfunctions, and alteration of cardiorespiratory coupling, a consequence of multiple interrelated disturbances in the genetic and homeostatic regulation of central and peripheral neuronal networks, redox state, and control of inflammation. Characteristic breath-holds, obstructive sleep apnea, and aerophagia result in intermittent hypoxia, which, combined with mitochondrial dysfunction, causes oxidative stress-an important driver of the clinical presentation of RTT.

Development of a Heart Rate Variability Risk Score to Predict Organ Dysfunction and Death in Critically Ill Children.

OBJECTIVES: Determine whether the Heart Rate Variability Dysfunction score, a novel age-normalized measure of autonomic nervous system dysregulation, is associated with the development of new or progressive multiple organ dysfunction syndrome or death in critically ill children. DESIGN, SETTING, AND PATIENTS: This was a retrospective, observational cohort study from 2012 to 2018. Patients admitted to the PICU with at least 12 hours of continuous heart rate data available from bedside monitors during the first 24 hours of admission were included in the analysis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Heart rate variability was measured using the integer heart rate variability, which is the sd of the heart rate sampled every 1 second over 5 consecutive minutes. The Heart Rate Variability Dysfunction score was derived from age-normalized values of integer heart rate variability and transformed, so that higher scores were indicative of lower integer heart rate variability and a proxy for worsening autonomic nervous system dysregulation. Heart Rate Variability Dysfunction score performance as a predictor of new or progressive multiple organ dysfunction syndrome and 28-day mortality were determined using the area under the receiver operating characteristic curve. Of the 7,223 patients who met inclusion criteria, 346 patients (4.8%) developed new or progressive multiple organ dysfunction syndrome, and 103 (1.4%) died by day 28. For every one-point increase in the median Heart Rate Variability Dysfunction score in the first 24 hours of admission, there was a 25% increase in the odds of new or progressive multiple organ dysfunction syndrome and a 51% increase in the odds of mortality. The median Heart Rate Variability Dysfunction score in the first 24 hours had an area under the receiver operating characteristic curve to discriminate new or progressive multiple organ dysfunction syndrome of 0.67 and to discriminate mortality of 0.80. These results were reproducible in a temporal validation cohort. CONCLUSIONS: The Heart Rate Variability Dysfunction score, an age-adjusted proxy for autonomic nervous system dysregulation derived from bedside monitor data is independently associated with new or progressive multiple organ dysfunction syndrome and mortality in PICU patients. The Heart Rate Variability Dysfunction score could potentially be used as a single continuous physiologic biomarker or as part of a multivariable prediction model to increase awareness of at-risk patients and augment clinical decision-making.

Congenital central hypoventilation syndrome: a bedside-to-bench success story for advancing early diagnosis and treatment and improved survival and quality of life.

The "bedside-to-bench" Congenital Central Hypoventilation Syndrome (CCHS) research journey has led to increased phenotypic-genotypic knowledge regarding autonomic nervous system (ANS) regulation, and improved clinical outcomes. CCHS is a neurocristopathy characterized by hypoventilation and ANS dysregulation. Initially described in 1970, timely diagnosis and treatment remained problematic until the first large cohort report (1992), delineating clinical presentation and treatment options. A central role of ANS dysregulation (2001) emerged, precipitating evaluation of genes critical to ANS development, and subsequent 2003 identification of Paired-Like Homeobox 2B (PHOX2B) as the disease-defining gene for CCHS. This breakthrough engendered clinical genetic testing, making diagnosis exact and early tracheostomy/artificial ventilation feasible. PHOX2B genotype-CCHS phenotype relationships were elucidated, informing early recognition and timely treatment for phenotypic manifestations including Hirschsprung disease, prolonged sinus pauses, and neural crest tumors. Simultaneously, cellular models of CCHS-causing PHOX2B mutations were developed to delineate molecular mechanisms. In addition to new insights regarding genetics and neurobiology of autonomic control overall, new knowledge gained has enabled physicians to anticipate and delineate the full clinical CCHS phenotype and initiate timely effective management. In summary, from an initial guarantee of early mortality or severe neurologic morbidity in survivors, CCHS children can now be diagnosed early and managed effectively, achieving dramatically improved quality of life as adults.

Psychometric Properties of the Automated Insulin Delivery: Benefits and Burdens Scale for Adults with Type 1 Diabetes.

Objective: To evaluate the psychometric properties of a patient-reported outcome measure, the Automated Insulin Delivery-Benefits and Burdens Scale (AID-BBS), which was designed to assess benefits and burdens of AID use in adults with type 1 diabetes (T1D). The measure was hypothesized to have validity, reliability, and clinical utility for predicting likelihood of continued use of an AID system. Research Design and Methods: A total of 217 adults with T1D (ages from 18 to 82 years) who were enrolled in an AID system research trial completed AID-BBS items at study midpoint (6 weeks) and at the end of the trial (13 weeks). Data were collected on pre-post glycemic outcomes. Participants completed other patient-reported psychosocial outcome measures (e.g., emotional well-being, diabetes distress, attitudes toward diabetes technology, diabetes treatment satisfaction) at Week 13. Likelihood of continued device use was assessed with three items at 13 weeks. Results: Exploratory factor analysis supported a one-factor structure for each subscale (15-item benefit and 9-item burden subscale) when evaluated separately. Convergent, discriminant, and predictive validity, internal consistency, and test-retest reliability were supported. Benefit and burden subscales at week 6 predicted usage intention above and beyond device impact on glycemic outcomes, also controlling for baseline glycemic outcomes. Conclusion: Findings support the AID-BBS as a psychometrically valid, reliable, and useful instrument for assessing burdens and benefits associated with AID system use in adults with T1D. The measure can be used to help health care providers set realistic expectations and proactively address modifiable burdens. Clinical Trial Registration Number: NCT04200313.

Cycle-by-cycle assembly of respiratory network activity is dynamic and stochastic.

Rhythmically active networks are typically composed of neurons that can be classified as silent, tonic spiking, or rhythmic bursting based on their intrinsic activity patterns. Within these networks, neurons are thought to discharge in distinct phase relationships with their overall network output, and it has been hypothesized that bursting pacemaker neurons may lead and potentially trigger cycle onsets. We used multielectrode recording from 72 experiments to test these ideas in rhythmically active slices containing the pre-Bötzinger complex, a region critical for breathing. Following synaptic blockade, respiratory neurons exhibited a gradient of intrinsic spiking to rhythmic bursting activities and thus defied an easy classification into bursting pacemaker and nonbursting categories. Features of their firing activity within the functional network were analyzed for correlation with subsequent rhythmic bursting in synaptic isolation. Higher firing rates through all phases of fictive respiration statistically predicted bursting pacemaker behavior. However, a cycle-by-cycle analysis indicated that respiratory neurons were stochastically activated with each burst. Intrinsically bursting pacemakers led some population bursts and followed others. This variability was not reproduced in traditional fully interconnected computational models, while sparsely connected network models reproduced these results both qualitatively and quantitatively. We hypothesize that pacemaker neurons do not act as clock-like drivers of the respiratory rhythm but rather play a flexible and dynamic role in the initiation and stabilization of each burst. Thus, at the behavioral level, each breath can be thought of as de novo assembly of a stochastic collaboration of network topology and intrinsic properties.

Patterns of inspiratory phase-dependent activity in the in vitro respiratory network.

Mechanistic descriptions of rhythmogenic neural networks have often relied on ball-and-stick diagrams, which define interactions between functional classes of cells assumed to be reasonably homogenous. Application of this formalism to networks underlying respiratory rhythm generation in mammals has produced increasingly intricate models that have generated significant insight, but the underlying assumption that individual cells within these network fall into distinct functional classes has not been rigorously tested. In the present study we used multiunit extracellular recording in the in vitro pre-Bötzinger complex to identify and characterize the rhythmic activity of 951 cells. Inspiratory phase-dependent activity was estimated for all cells, and the data set as a whole was analyzed with principal component analysis, nonlinear dimensionality reduction, and hierarchical clustering techniques. None of these techniques revealed categorically distinct functional cell classes, indicating instead that the behavior of these cells within the network falls along several continua of spiking behavior.

Parental Perceptions on Use of Artificial Intelligence in Pediatric Acute Care.

BACKGROUND: Family engagement is critical in the implementation of artificial intelligence (AI)-based clinical decision support tools, which will play an increasing role in health care in the future. We sought to understand parental perceptions of computer-assisted health care of children in the emergency department (ED). METHODS: We conducted a population-weighted household panel survey of parents with minor children in their home in a large US city to evaluate perceptions of the use of computer programs for the care of children with respiratory illness. We identified demographics associated with discomfort with AI using survey-weighted logistic regression. RESULTS: Surveys were completed by 1620 parents (panel response rate = 49.7%). Most respondents were comfortable with the use of computer programs to determine the need for antibiotics (77.6%) or bloodwork (76.5%), and to interpret radiographs (77.5%). In multivariable analysis, Black non-Hispanic parents reported greater discomfort with AI relative to White non-Hispanic parents (odds ratio [OR] 1.67, 95% confidence interval [CI] 1.03-2.70) as did younger parents (18-25 years) relative to parents ≥46 years (OR 2.48, 95% CI 1.31-4.67). The greatest perceived benefits of computer programs were finding something a human would miss (64.2%, 95% CI 60.9%-67.4%) and obtaining a more rapid diagnosis (59.6%; 56.2%-62.9%). Areas of greatest concern were diagnostic errors (63.0%, 95% CI 59.6%-66.4%), and recommending incorrect treatment (58.9%, 95% CI 55.5%-62.3%). CONCLUSIONS: Parents were generally receptive to computer-assisted management of children with respiratory illnesses in the ED, though reservations emerged. Black non-Hispanic and younger parents were more likely to express discomfort about AI.

Perceptions of Artificial Intelligence-Assisted Care for Children With a Respiratory Complaint.

OBJECTIVES: To evaluate caregiver opinions on the use of artificial intelligence (AI)-assisted medical decision-making for children with a respiratory complaint in the emergency department (ED). METHODS: We surveyed a sample of caregivers of children presenting to a pediatric ED with a respiratory complaint. We assessed caregiver opinions with respect to AI, defined as "specialized computer programs" that "help make decisions about the best way to care for children." We performed multivariable logistic regression to identify factors associated with discomfort with AI-assisted decision-making. RESULTS: Of 279 caregivers who were approached, 254 (91.0%) participated. Most indicated they would want to know if AI was being used for their child's health care (93.5%) and were extremely or somewhat comfortable with the use of AI in deciding the need for blood (87.9%) and viral testing (87.6%), interpreting chest radiography (84.6%), and determining need for hospitalization (78.9%). In multivariable analysis, caregiver age of 30 to 37 years (adjusted odds ratio [aOR] 3.67, 95% confidence interval [CI] 1.43-9.38; relative to 18-29 years) and a diagnosis of bronchospasm (aOR 5.77, 95% CI 1.24-30.28 relative to asthma) were associated with greater discomfort with AI. Caregivers with children being admitted to the hospital (aOR 0.23, 95% CI 0.09-0.50) had less discomfort with AI. CONCLUSIONS: Caregivers were receptive toward the use of AI-assisted decision-making. Some subgroups (caregivers aged 30-37 years with children discharged from the ED) demonstrated greater discomfort with AI. Engaging with these subgroups should be considered when developing AI applications for acute care.

Association of Youth Suicides and County-Level Mental Health Professional Shortage Areas in the US.

Importance: Suicide is the second leading cause of death among US adolescents. Workforce shortages of mental health professionals in the US are widespread, but the association between mental health workforce shortages and youth suicides is not well understood. Objective: To assess the association between youth suicide rates and mental health professional workforce shortages at the county level, adjusting for county demographic and socioeconomic characteristics. Design, Setting, and Participants: This retrospective cross-sectional study included all US counties and used data of all US youlth suicides from January 2015, through December 31, 2016. Data were analyzed from July 1, 2021, through December 20, 2021. Exposures: County health-professional shortage area designation for mental health, assigned by the US Health Resources and Services Administration based on mental health professionals relative to the population, level of need for mental health services, and service availability in contiguous areas. Designated shortage areas receive a score from 0 to 25, with higher scores indicating greater workforce shortages. Main Outcomes and Measures: Suicides by youth aged 5 to 19 years from 2015 to 2016 were identified from the US Centers for Disease Control and Prevention's Compressed Mortality File. A multivariable negative binomial regression model was used to analyze the association between youth suicide rates and mental health workforce shortage designation, adjusting for the presence of a children's mental health hospital and county-level markers of health insurance coverage, education, unemployment, income, poverty, urbanicity, racial and ethnic composition, and year. Similar models were performed for the subgroups of (1) firearm suicides and (2) counties assigned a numeric shortage score. Results: During the study period, there were 5034 youth suicides (72.8% male and 68.2% non-Hispanic White) with an annual suicide rate of 3.99 per 100 000 youths. Of 3133 US counties, 2117 (67.6%) were designated as mental health workforce shortage areas. After adjusting for county characteristics, mental health workforce shortage designation was associated with an increased youth suicide rate (adjusted incidence rate ratio [aIRR], 1.16; 95% CI, 1.07-1.26) and an increased youth firearm suicide rate (aIRR, 1.27; 95% CI, 1.13-1.42). For counties with an assigned numeric workforce shortage score, the adjusted youth suicide rate increased 4% for every 1-point increase in the score (aIRR, 1.04; 95% CI, 1.02-1.06). Conclusions and Relevance: In this cross-sectional study, US county mental health professional workforce shortages were associated with increased youth suicide rates. These findings may inform suicide prevention efforts.

Sepsis Prediction in Hospitalized Children: Model Development and Validation.

BACKGROUND AND OBJECTIVES: Early recognition and treatment of pediatric sepsis remain mainstay approaches to improve outcomes. Although most children with sepsis are diagnosed in the emergency department, some are admitted with unrecognized sepsis or develop sepsis while hospitalized. Our objective was to develop and validate a prediction model of pediatric sepsis to improve recognition in the inpatient setting. METHODS: Patients with sepsis were identified using intention-to-treat criteria. Encounters from 2012 to 2018 were used as a derivation to train a prediction model using variables from an existing model. A 2-tier threshold was determined using a precision-recall curve: an "Alert" tier with high positive predictive value to prompt bedside evaluation and an "Aware" tier with high sensitivity to increase situational awareness. The model was prospectively validated in the electronic health record in silent mode during 2019. RESULTS: A total of 55 980 encounters and 793 (1.4%) episodes of sepsis were used for derivation and prospective validation. The final model consisted of 13 variables with an area under the curve of 0.96 (95% confidence interval 0.95-0.97) in the validation set. The Aware tier had 100% sensitivity and the Alert tier had a positive predictive value of 14% (number needed to alert of 7) in the validation set. CONCLUSIONS: We derived and prospectively validated a 2-tiered prediction model of inpatient pediatric sepsis designed to have a high sensitivity Aware threshold to enable situational awareness and a low number needed to Alert threshold to minimize false alerts. Our model was embedded in our electronic health record and implemented as clinical decision support, which is presented in a companion article.

Sepsis Prediction in Hospitalized Children: Clinical Decision Support Design and Deployment.

BACKGROUND: Following development and validation of a sepsis prediction model described in a companion article, we aimed to use quality improvement and safety methodology to guide the design and deployment of clinical decision support (CDS) tools and clinician workflows to improve pediatric sepsis recognition in the inpatient setting. METHODS: CDS tools and sepsis huddle workflows were created to implement an electronic health record-based sepsis prediction model. These were proactively analyzed and refined using simulation and safety science principles before implementation and were introduced across inpatient units during 2020-2021. Huddle compliance, alerts per non-ICU patient days, and days between sepsis-attributable emergent transfers were monitored. Rapid Plan-Do-Study-Act (PDSA) cycles based on user feedback and weekly metric data informed improvement throughout implementation. RESULTS: There were 264 sepsis alerts on 173 patients with an 89% bedside huddle completion rate and 10 alerts per 1000 non-ICU patient days per month. There was no special cause variation in the metric days between sepsis-attributable emergent transfers. CONCLUSIONS: An automated electronic health record-based sepsis prediction model, CDS tools, and sepsis huddle workflows were implemented on inpatient units with a relatively low rate of interruptive alerts and high compliance with bedside huddles. Use of CDS best practices, simulation, safety tools, and quality improvement principles led to high utilization of the sepsis screening process.

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.

Pupillometry in congenital central hypoventilation syndrome (CCHS): quantitative evidence of autonomic nervous system dysregulation.

INTRODUCTION: Congenital central hypoventilation syndrome (CCHS) is characterized by alveolar hypoventilation, autonomic nervous system (ANS) dysregulation (ANSD), and mutations in the paired-like homeobox 2B (PHOX2B) gene. ANSD in CCHS affects multiple systems and includes ophthalmologic abnormalities. We hypothesized that quantitative pupil measures, obtained using pupillometry, would vary between cases with CCHS and controls and within those with CCHS by PHOX2B genotype. RESULTS: Measures known to be illustrative of sympathetic and parasympathetic response (prestimulus, maximum pupil diameter, percentage of pupil constriction after light stimulus, and average constriction and dilation velocities) were significantly reduced in those with CCHS as compared with controls (all P < 0.05). DISCUSSION: These reductions are indicative of both sympathetic and parasympathetic deficits in CCHS, which is in keeping with the role of PHOX2B in ANS development. An inverse linear relationship was apparent in pupil diameter and velocity measurements among the cases with CCHS with the most common heterozygous PHOX2B polyalanine expansion repeat mutations, suggesting a graded phenotype/genotype dose response based on polyalanine repeat length. These results confirm our central hypotheses while offering the first objective measures of pupillary dysfunction and ophthalmologic-specific ANSD in CCHS. METHODS: A total of 316 monocular measurements were taken under dark-adapted conditions with a fixed light stimulus from 22 PHOX2B mutation-confirmed cases with CCHS and 68 healthy controls.

Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years.

Importance: Readmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children. Objective: To develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages. Design, Setting, and Participants: This population-based prognostic analysis used electronic health record-derived data from a freestanding children's hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021. Main Outcomes and Measures: Three models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges. Results: The derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort. Conclusions and Relevance: In this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.