Browsing by Author "Bandyopadhyay, Anuja"
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Item Antenatal corticosteriods decrease forced vital capacity in infants born fullterm(Wiley, 2020-10) Bandyopadhyay, Anuja; Slaven, James E.; Evrard, Cindy; Tiller, Christina; Haas, David M.; Tepper, Robert S.; Pediatrics, School of MedicineAntenatal corticosteroids (ACS) administration to pregnant women for threatened preterm labor is standard obstetric care to reduce neonatal respiratory distress syndrome and the associated respiratory morbidity. While ACS stimulates surfactant production in the fetal lung, the effects of ACS upon the subsequent growth and development of the lung are unclear. Follow-up studies outside of the neonatal period have been primarily limited to spirometry, and most subjects evaluated were born prematurely. To our knowledge, no study has assessed both airway and parenchymal function in infants or adults following ACS exposure. We hypothesized that ACS impairs lung growth and performed infant pulmonary function testing, which included spirometry, alveolar volume (VA ) and lung diffusion (DL ). As a pilot study, we limited our assessment to infants whose mothers received ACS for threatened preterm labor, but then proceeded to full term delivery. This approach evaluated a more homogenous population and eliminated the confounding effects of preterm birth. We evaluated 36 full-term infants between 4 to 12 months of age; 17 infants had ACS exposure and 19 infants had no ACS exposure. Infants exposed to ACS had a significantly lower forced vital capacity compared with non-ACS exposed infants (250 vs 313 mL; P = .0075). FEV0.5 tended to be lower for the ACS exposed group (205 vs 237 mL; P = .075). VA and DL did not differ between the two groups. These findings suggest that ACS may impair subsequent growth of the lung parenchyma.Item Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective(Springer, 2023) Bandyopadhyay, Anuja; Goldstein, Cathy; Pediatrics, School of MedicineBackground: The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. Method: The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. Results: Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. Conclusion: Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.Item Effect of myofunctional therapy on children with obstructive sleep apnea: a meta-analysis(Elsevier, 2020-11) Bandyopadhyay, Anuja; Kaneshiro, Kellie; Camacho, Macario; Pediatrics, School of MedicineObjective To systematically review the current literature for articles describing the effect of myofunctional therapy on pediatric obstructive sleep apnea (OSA) and to perform a meta-analysis on the sleep study data. Methods Three authors (A.B., K.K. and M.C.) independently searched from inception through April 20, 2020 in PubMed/MEDLINE, Scopus, Embase, Google Scholar and The Cochrane Library. Mean difference (MD), standard deviations and 95% confidence intervals were combined in the meta-analysis for apnea-hypopnea index (AHI), mean oxygen saturations, and lowest oxygen saturations (nadir O2). Results 10 studies with 241 patients met study criteria and were further analyzed. The AHI reduced from 4.32 (5.2) to 2.48 (4.0) events/hr, a 43% reduction. Random effects modeling demonstrated a mean difference in AHI of −1.54 (95% CI -2.24,-0.85)/hr, z-score is 4.36 (p < 0.0001). Mean oxygen saturation increased by 0.37 (95% CI 0.06,0.69) percent, z-score is 2.32 (p = 0.02). There was no significant increase in nadir O2. Conclusions Despite heterogeneity in exercises, myofunctional therapy decreased AHI by 43% in children, and increased mean oxygen saturations in children with mild to moderate OSA and can serve as an adjunct OSA treatment.Item Health outcomes associated with improvement in mouth breathing in children with OSA(Springer, 2021) Bandyopadhyay, Anuja; Slaven, James E.; Pediatrics, School of MedicinePurpose Children with mouth breathing (MB) report poor quality of life. It is unknown whether improvement in MB is associated with improvement in behavior or quality of life. We hypothesized that in children with MB and obstructive sleep apnea (OSA), improvement in MB is associated with improvement in behavior and quality of life, independent of improvement in OSA. Methods This is a retrospective post hoc analysis utilizing Childhood Adenotonsillectomy Trial (CHAT) dataset, a multicenter controlled study evaluating outcomes in children with OSA randomized into early adenotonsillectomy or watchful waiting. Children with OSA and MB at baseline (determined by reporting 2 or greater to OSA-18 questionnaire on mouth breathing) were divided into 2 groups: improved mouth breathing (IMB, determined by a lower score compared to baseline at follow up) and persistent mouth breathing (PMB, determined by an unchanged or higher score). Baseline characteristics, behavior (Conners GI score), sleepiness (Epworth Sleepiness Scale), and quality of life (PedsQL) were compared between the groups using appropriate statistical tests. ANCOVA models were used to analyze change in outcomes, adjusting for treatment arm and change in AHI. Results Of 273 children with OSA and MB at baseline, IMB (N = 195) had significantly improved score between visits for Conner’s GI Total T score, Epworth Sleepiness Scale, and PedsQL compared to PMB (N = 78), after adjusting for treatment arm and change in AHI. Conclusion Our study suggests an interesting association between mouth breathing and quality of life, independent of polysomnographic evidence. Future studies should explore the effect of mouth breathing on quality of life, in absence of OSA.Item Heart failure is not a determinant of central sleep apnea in the pediatric population(Wiley, 2021) Wheeler, Jonathan A.; Tutrow, Kaylee D.; Ebenroth, Eric S.; Gaston, Benjamin; Bandyopadhyay, Anuja; Medical and Molecular Genetics, School of MedicineBackground/objectives: Adults with heart failure (HF) have high prevalence of central sleep apnea (CSA). While this has been repeatedly investigated in adults, there is a deficiency of similar research in pediatric populations. The goal of this study was to compare prevalence of CSA in children with and without HF and correlate central apneic events with heart function. Methods: Retrospective analysis of data from children with and without HF was conducted. Eligible children were less than 18 years old with echocardiogram and polysomnogram within 6 months of each other. Children were separated into groups with and without HF based on left ventricular ejection fraction (LVEF). Defining CSA as central apnea-hypopnea index (CAHI) more than 1/hour, the cohort was also classified into children with and without CSA for comparative study. Results: A total of 120 children (+HF: 19, -HF: 101) were included. The +HF group was younger, with higher prevalence of trisomy 21, muscular dystrophy, oromotor incoordination, and structural heart disease. The +HF group had lower apnea-hypopnea index (median: 3/hour vs. 8.6/hour) and lower central apnea index (CAI) (median: 0.2/hour vs. 0.55/hour). Prevalence of CSA was similar in both groups (p = .195). LogCAHI was inversely correlated to age (Pearson correlation coefficient: -0.245, p = .022). Children with CSA were younger and had higher prevalence of prematurity (40% vs. 5.3%). There was no significant difference in LVEF between children with and without CSA. After excluding children with prematurity, relationship between CAHI and age was no longer sustained. Conclusions: In contrast to adults, there is no difference in prevalence of CSA in children with and without HF. Unlike in adults, LVEF does not correlate with CAI in children. Overall, it appears that central apneic events may be more a function of age and prematurity rather than of heart function.Item Neurodevelopmental Outcomes at Two Years of Age for Premature Infants Diagnosed With Neonatal Obstructive Sleep Apnea(American Academy of Sleep Medicine, 2017-11-15) Bandyopadhyay, Anuja; Harmon, Heidi; Slaven, James E.; Daftary, Ameet S.; Pediatrics, School of MedicineSTUDY OBJECTIVES: Neurocognitive deficits have been shown in school-aged children with sleep apnea. The effect of obstructive sleep apnea (OSA) on the neurodevelopmental outcome of preterm infants is unknown. METHODS: A retrospective chart review was performed for all preterm infants (< 37 weeks) who had neonatal polysomnography (PSG) and completed neurodevelopmental assessment with the Bayley Scales of Infant and Toddler Development, 3rd Edition, between 2006 to 2015 at Riley Hospital. Exclusion criteria included grade IV intraventricular hemorrhage, tracheostomy, cyanotic heart disease, severe retinopathy of prematurity, craniofacial anomalies, or central and mixed apnea on PSG. Sleep apnea was defined as an apnea-hypopnea index (AHI) > 1 event/h. Regression analyses were performed to find a relationship between PSG parameters and cognitive, language, and motor scores. RESULTS: Fifteen patients (males: n = 10) were eligible for the study. Median postmenstrual age at the time of the PSG was 41 weeks (37-46). Median AHI for the cohort was 17.4 events/h (2.2-41.3). Median cognitive, language, and motor scores were 90 (65-125), 89 (65-121), and 91 (61-112), respectively. Mean end-tidal CO2 (median 47 mm Hg [25-60]) negatively correlated with cognitive scores (P = .01) but did not significantly correlate with language or motor scores. AHI was not associated with cognitive, language, or motor scores. CONCLUSIONS: The median score for cognitive, language, and motor scores for preterm infants with neonatal OSA were within one standard deviation of the published norm. Mean end-tidal CO2, independent of AHI, may serve as a biomarker for predicting poor cognitive outcome in preterm infants with neonatal OSA.Item Obstructive Sleep Apnea in Infants During the First Year of Life: What the Pediatrician Needs to Know(SAGE, 2020-07-01) Bandyopadhyay, Anuja; Daftary, Ameet S.; Pediatrics, School of MedicineItem Photoplethysmography-new applications for an old technology: a sleep technology review(American Academy of Sleep Medicine, 2023) Ryals, Scott; Chiang, Ambrose; Schutte-Rodin, Sharon; Chandrakantan, Arvind; Verma, Nitun; Holfinger, Steven; Abbasi-Feinberg, Fariha; Bandyopadhyay, Anuja; Baron, Kelly; Bhargava, Sumit; He, Ken; Kern, Joseph; Miller, Jennifer; Patel, Ruchir; Ratnasoma, Dulip; Deak, Maryann C.; Pediatrics, School of MedicineEducation is integral to the American Academy of Sleep Medicine (AASM) mission. The AASM Emerging Technology Committee identified an important and evolving piece of technology that is present in many of the consumer and clinical technologies that we review on the AASM #SleepTechnology (https://aasm.org/consumer-clinical-sleep-technology/) resource-photoplethysmography. As more patients with sleep tracking devices ask clinicians to view their data, it is important for sleep providers to have a general understanding of the technology, its sensors, how it works, targeted users, evidence for the claimed uses, and its strengths and weaknesses. The focus in this review is photoplethysmography-a sensor type used in the familiar pulse oximeter that is being developed for additional utilities and data outputs in both consumer and clinical sleep technologies.Item Retrospective Analysis of Factors Leading to Pediatric Tracheostomy Decannulation Failure. A Single-Institution Experience(American Thoracic Society, 2017-01) Bandyopadhyay, Anuja; Cristea, A. Ioana; Davis, Stephanie D.; Ackerman, Veda L.; Slaven, James E.; Jalou, Hasnaa E.; Givan, Deborah C.; Daftary, Ameet; Pediatrics, School of MedicineRATIONALE: There is a lack of evidence regarding factors associated with failure of tracheostomy decannulation. OBJECTIVES: We aimed to identify characteristics of pediatric patients who fail a tracheostomy decannulation challenge Methods: A retrospective review was performed on all patients who had a decannulation challenge at a tertiary care center from June 2006 to October 2013. Tracheostomy decannulation failure was defined as reinsertion of the tracheostomy tube within 6 months of the challenge. Data on demographics, indications for tracheostomy, home mechanical ventilation, and comorbidities were collected. Data were also collected on specific airway endoscopic findings during the predecannulation bronchoscopy and airway surgical procedures before decannulation. We attempted to predict the decannulation outcome by analyzing associations. MEASUREMENTS AND MAIN RESULTS: 147 of 189 (77.8%) patients were successfully decannulated on the first attempt. Tracheostomy performed due to chronic respiratory failure decreased odds for decannulation failure (odds ratio = 0.34, 95% confidence interval = 0.15-0.77). Genetic abnormalities (45%) and feeding dysfunction (93%) were increased in the population of patients failing their first attempt. The presence of one comorbidity increased the odds of failure by 68% (odds ratio = 1.68, 95% confidence interval = 1.23-2.29). Decannulation pursuit based on parental expectation of success, rather than medically determined readiness, was associated with a higher chance of failure (P = 0.01). CONCLUSIONS: Our study highlights the role of genetic abnormalities, feeding dysfunction, and multiple comorbidities in patients who fail decannulation. Our findings also demonstrate that the outcome of decannulation may be predicted by the indication for tracheostomy. Patients who had tracheostomy placed for chronic respiratory support had a higher likelihood of success. Absence of a surgically treatable airway obstruction abnormality on the predecannulation bronchoscopy increased the chances of success.Item Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine(American Academy of Sleep Medicine, 2023) Bandyopadhyay, Anuja; Bae, Charles; Cheng, Hao; Chiang, Ambrose; Deak, Maryann; Seixas, Azizi; Singh, Jaspal; Pediatrics, School of MedicineSince the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep clinicians. To help clinicians understand the current state of AI and sleep medicine, and to further enable these solutions to be adopted into clinical practice, a discussion panel was conducted on June 7, 2022, at the Associated Professional Sleep Societies Sleep Conference in Charlotte, North Carolina. The article is a summary of key discussion points from this session, including aspects of considerations for the clinician in evaluating AI-enabled solutions including but not limited to what steps might be taken both by the Food and Drug Administration and clinicians to protect patients, logistical issues, technical challenges, billing and compliance considerations, education and training considerations, and other unique challenges specific to AI-enabled solutions. Our summary of this session is meant to support clinicians in efforts to assist in the clinical care of patients with sleep disorders utilizing AI-enabled solutions.