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Validation of a predictive model for obstructive sleep apnea in people with Down syndrome.


ABSTRACT: Detecting obstructive sleep apnea (OSA) is important to both prevent significant comorbidities in people with Down syndrome (DS) and untangle contributions to other behavioral and mental health diagnoses. However, laboratory-based polysomnograms are often poorly tolerated, unavailable, or not covered by health insurance for this population. In previous work, our team developed a prediction model that seemed to hold promise in identifying which people with DS might not have significant apnea and, consequently, might be able to forgo a diagnostic polysomnogram. In this study, we sought to validate these findings in a novel set of participants with DS. We recruited an additional 64 participants with DS, ages 3-35 years. Caregivers completed the same validated questionnaires, and our study team collected vital signs, physical exam findings, and medical histories that were previously shown to be predictive. Patients then had a laboratory-based polysomnogram. The best modeling had a validated negative predictive value of 50% for an apnea-hypopnea index (AHI) > 1/hTST and 73.7% for AHI >5/hTST. The positive predictive values were 60% and 39.1%, respectively. As such, a clinically reliable screening tool for OSA in people with DS was not achieved. Patients with DS should continue to be monitored for OSA according to current healthcare guidelines.

SUBMITTER: Skotko BG 

PROVIDER: S-EPMC9988250 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Validation of a predictive model for obstructive sleep apnea in people with Down syndrome.

Skotko Brian G BG   Garza Flores Alexandra A   Elsharkawi Ibrahim I   Patsiogiannis Vasiliki V   McDonough Mary Ellen ME   Verda Damiano D   Muselli Marco M   Hornero Roberto R   Gozal David D   Macklin Eric A EA  

American journal of medical genetics. Part A 20221125 2


Detecting obstructive sleep apnea (OSA) is important to both prevent significant comorbidities in people with Down syndrome (DS) and untangle contributions to other behavioral and mental health diagnoses. However, laboratory-based polysomnograms are often poorly tolerated, unavailable, or not covered by health insurance for this population. In previous work, our team developed a prediction model that seemed to hold promise in identifying which people with DS might not have significant apnea and,  ...[more]

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