Use and limitations of databases and big data in sleep-disordered breathing research

2021 ◽  
Author(s):  
Karin G. Johnson ◽  
Douglas C. Johnson ◽  
Stephen Derose
Keyword(s):  
Big Data ◽  
Sleep Disordered Breathing ◽  
Disordered Breathing

Small sleepers, big data: leveraging big data to explore sleep-disordered breathing in infants and young children

SLEEP ◽  
2020 ◽  
Author(s):  
Zarmina Ehsan ◽  
Earl F Glynn ◽  
Mark A Hoffman ◽  
David G Ingram ◽  
Baha Al-Shawwa
Keyword(s):  
Big Data ◽  
Sleep Apnea ◽  
Young Children ◽  
Sleep Disordered Breathing ◽  
Large Scale ◽  
Obstructive Sleep ◽  
Significant Burden ◽  
Report Data ◽  
Infants And Young Children ◽  
Disordered Breathing

Abstract Study Objectives Infants represent an understudied minority in sleep-disordered breathing (SDB) research and yet the disease can have a significant impact on health over the formative years of neurocognitive development that follow. Herein we report data on SDB in this population using a big data approach. Methods Data were abstracted using the Cerner Health Facts database. Demographics, sleep diagnoses, comorbid medication conditions, healthcare utilization, and economic outcomes are reported. Results In a cohort of 68.7 million unique patients, over a 9-year period, there were 9,773 infants and young children with a diagnosis of SDB (obstructive sleep apnea [OSA], nonobstructive sleep apnea, and “other” sleep apnea) who met inclusion criteria, encompassing 17,574 encounters, and a total of 27,290 diagnoses across 62 U.S. health systems, 172 facilities, and 3 patient encounter types (inpatient, clinic, and outpatient). Thirty-nine percent were female. Thirty-nine percent were ≤1 year of age (6,429 infants), 50% were 1–2 years of age, and 11% were 2 years of age. The most common comorbid diagnoses were micrognathia, congenital airway abnormalities, gastroesophageal reflux, chronic tonsillitis/adenoiditis, and anomalies of the respiratory system. Payor mix was dominated by government-funded entities. Conclusions We have used a novel resource, large-scale aggregate, de-identified EHR data, to examine SDB. In this population, SDB is multifactorial, closely linked to comorbid medical conditions and may contribute to a significant burden of healthcare costs. Further research focusing on infants at highest risk for SDB can help target resources and facilitate personalized management.

scholarly journals The Role of Big Data in the Management of Sleep-Disordered Breathing

2016 ◽  
Vol 11 (2) ◽  
pp. 241-255 ◽  
Author(s):  
Rohit Budhiraja ◽  
Robert Thomas ◽  
Matthew Kim ◽  
Susan Redline
Keyword(s):  
Big Data ◽  
Sleep Disordered Breathing ◽  
Disordered Breathing

Comparison of the State of Sleep in Patients with Sleep-disordered Breathing Before and After Surgical Treatment

2003 ◽  
Vol 123 (0) ◽  
pp. 51-55
Author(s):  
Munenori Hayakawa ◽  
Tadao Nishimura ◽  
Kenji Suzuki ◽  
Natsuki Morishima ◽  
Nobuhiro Shibata ◽  
...  
Keyword(s):  
Surgical Treatment ◽  
Sleep Disordered Breathing ◽  
The State ◽  
Before And After ◽  
Disordered Breathing
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