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Sex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos.

TitleSex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos.
Publication TypePublication
Year2016
AuthorsShah N, Hanna DB, Teng Y, Sotres-Alvarez D, Hall M, Loredo JS, Zee P, Kim M, H Yaggi K, Redline S, Kaplan RC
JournalChest
Volume149
Issue6
Pagination1409-18
Date Published2016 Jun
ISSN1931-3543
KeywordsAdult, Clinical Decision-Making, Cohort Studies, Cross-Sectional Studies, Female, Hispanic Americans, Humans, Male, Models, Statistical, Polysomnography, Prevalence, Risk Factors, Sex Factors, Sleep Apnea Syndromes, United States
Abstract

OBJECTIVE: We developed and validated the first-ever sleep apnea (SA) risk calculator in a large population-based cohort of Hispanic/Latino subjects.METHODS: Cross-sectional data on adults from the Hispanic Community Health Study/Study of Latinos (2008-2011) were analyzed. Subjective and objective sleep measurements were obtained. Clinically significant SA was defined as an apnea-hypopnea index ≥ 15 events per hour. Using logistic regression, four prediction models were created: three sex-specific models (female-only, male-only, and a sex × covariate interaction model to allow differential predictor effects), and one overall model with sex included as a main effect only. Models underwent 10-fold cross-validation and were assessed by using the C statistic. SA and its predictive variables; a total of 17 variables were considered.RESULTS: A total of 12,158 participants had complete sleep data available; 7,363 (61%) were women. The population-weighted prevalence of SA (apnea-hypopnea index ≥ 15 events per hour) was 6.1% in female subjects and 13.5% in male subjects. Male-only (C statistic, 0.808) and female-only (C statistic, 0.836) prediction models had the same predictor variables (ie, age, BMI, self-reported snoring). The sex-interaction model (C statistic, 0.836) contained sex, age, age × sex, BMI, BMI × sex, and self-reported snoring. The final overall model (C statistic, 0.832) contained age, BMI, snoring, and sex. We developed two websites for our SA risk calculator: one in English (https://www.montefiore.org/sleepapneariskcalc.html) and another in Spanish (http://www.montefiore.org/sleepapneariskcalc-es.html).CONCLUSIONS: We created an internally validated, highly discriminating, well-calibrated, and parsimonious prediction model for SA. Contrary to the study hypothesis, the variables did not have different predictive magnitudes in male and female subjects.

DOI10.1016/j.chest.2016.01.013
Alternate JournalChest
PubMed ID26836933
PubMed Central IDPMC4944765
Grant ListK23 HL125923 / HL / NHLBI NIH HHS / United States
N01HC65236 / HL / NHLBI NIH HHS / United States
N01HC65235 / HL / NHLBI NIH HHS / United States
N01HC65234 / HL / NHLBI NIH HHS / United States
K24 HL132093 / HL / NHLBI NIH HHS / United States
N01HC65233 / HL / NHLBI NIH HHS / United States
N01HC65237 / HL / NHLBI NIH HHS / United States
MS#: 
0023
Manuscript Lead/Corresponding Author Affiliation: 
Field Center: Bronx (Einstein College of Medicine)
ECI: 
Yes
Manuscript Status: 
Published