Screening for Obstructive Sleep Apnea: Bayes Weighs in

Abstract

A fundamental challenge associated with screening tests is recognition of the impact of disease prevalence upon the predictive value of the result. For example, in the common circumstance of screening for low prevalence diseases, even good tests may have unacceptably high false positive rates. The converse situation, screening in high prevalence populations, is less common but occurs with obstructive sleep apnea (OSA): even ostensibly good screening tests may have unacceptably high false negative rates. The challenge of recognizing false negative OSA screening results has important implications as screens are increasingly implemented in high risk populations. This raises two clinically important questions: 1) How sensitive and specific should a screening test be to minimize false negative results across a spectrum of baseline prevalence; and 2) Given a screening test with known sensitivity and specificity, in what range of disease prevalence may the test be reasonably applied? Simple graphics are presented that incorporate acceptable risk thresholds and illustrate combinations of prevalence, sensitivity, and specificity in which disease probability remains high despite a negative test result. Adopting a Bayesian approach, together with acceptable risk thresholds, may help to avoid potential pitfalls of false negative screening results.