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Small sleepers, big data: leveraging big data to explore sleep-disordered breathing in infants and young children
Zarmina Ehsan1,2, Earl F Glynn3, Mark A Hoffman2,3
1Division of Pulmonary and Sleep Medicine, Children's Mercy-Kansas City, Kansas City, MO.
Insights
Sleep-disordered breathing (SDB) affects many infants and is linked to other medical conditions, leading to high healthcare costs. Further research can help target resources for personalized infant SDB management.
Area of Science:
- Pediatric Sleep Medicine
- Big Data in Healthcare
- Neurocognitive Development
Background:
- Infants are an understudied population in sleep-disordered breathing (SDB) research.
- SDB can significantly impact neurocognitive development during early childhood.
- A large-scale data approach is needed to understand SDB in infants.
Purpose of the Study:
- To investigate the prevalence and characteristics of SDB in infants using a big data approach.
- To identify common comorbid conditions associated with SDB in this age group.
- To analyze healthcare utilization and economic outcomes related to infant SDB.
Main Methods:
- Utilized the Cerner Health Facts database for de-identified electronic health record (EHR) data.
- Analyzed data from 68.7 million unique patients over a 9-year period.
- Included infants and young children diagnosed with obstructive sleep apnea (OSA) and other forms of SDB across various healthcare settings.
Main Results:
- Identified 9,773 infants and young children with SDB diagnoses across 17,574 encounters.
- The most common comorbidities included micrognathia, congenital airway abnormalities, and gastroesophageal reflux.
- The majority of patients were covered by government-funded insurance, indicating a significant healthcare cost burden.
Conclusions:
- SDB in infants is multifactorial and strongly associated with comorbid medical conditions.
- Infant SDB contributes to a substantial burden of healthcare costs.
- Further research is crucial to identify high-risk infants and personalize SDB management strategies.
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.
