Statistical models for predicting obstructive sleep apnea syndrome
Date Issued
2009
Date
2009
Author(s)
Lin, Chia-Mo
Abstract
Background：he predictive model for obstructive sleep apnea (OSA) measured by apnoea - hypopnoea index using epidemiological and clinical parameters has been proposed in previous studies. However, how the statistical property of AHI corresponds to the severity of OSA has been hardly addressed. Nor the application of predictive model to estimating risk reduction after the administration of intervention program that modulates the modifiable predictors.bjectives： e are tempted to investigate the statistical property of AHI, then applied different multiple regression model to estimating the relative contributions of each predictors to the variation of AHI, and finally to estimate the risk reduction of OSA by modifying the significant predictors. ethods： total of 500 samples were randomly selected form 2110 patients (1599 males and 511 females) who underwent nocturnal sleeping examination between 2005 and 2007 in one medical centre, northern Taiwan, with the average AHI of 28.32 (SD=26.38). Data on four predictors were collected, including body mass index (BMI), neck circumference, smoking and self-reported hypertension by reviewing medical chart. The multiple linear regression model was first applied to identifying significant predictors. Proportional odds model and multinominal logistic regression model were further adopted to estimating clinical weights (regression coefficients) of each predictor by taking AHI as categorical and ordinal property. Each receiver operating characteristics (ROC) curve corresponding to each logistic regression model was plotted to assess predictive validity of each predictive model. Based on risk score computed from the predictive model, Bayesian approach given different prevalence rate of OSA was applied to calculating the posterior risk in order to project risk reduction due to the modification of hypertension and BMI. esults ：HI score is a positively skewed distribution. The conventional cutoffs on 5, 15 and 30, defined as mild, moderate, and severe state, were commensurate with 20%, 40%, and 60% of the distribution of AHI. Four predictors were identified statistically significant, age, gender, BMI, and neck circumference in multiple linear regression model. In the multinominal logistic regression model, hypertension was a significant predictor, particularly affecting the moderate OSA. By looking at piecewise comparison of AHI < 5, 5-14, 15-29, and 30+, the contribution of clinical weight to each outcome are similar. These findings are supported by using proportional odds model. Posterior odds using Bayesian approach by prior odds (prevalence odds of OSA) and likelihood ratio based on the density of composite risk score calculated by multiplying the relevant covariates with each clinical weight obtained from the predictive model. Reduction of BMI from 30 to 23 yields the posterior risk of OSA defined by AHI greater than 15 from 21.7 to 6.9.onclusion：Predictive model for risk prediction for the OSA is helpful for the design of program intervention in modifying the risk factor.
Subjects
obstructive sleep apnea
statistical models
Type
thesis
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