Likelihood ratios for a sleep apnea clinical prediction rule.

Abstract

Nocturnal polysomnography, the standard diagnostic test for sleep apnea, is an expensive and limited resource. In order to help identify the urgency of need for treatment, we determined which clinical features were most useful for establishing an accurate estimate of the probability that a patient had sleep apnea. Of 263 physician-referred patients, 200 were eligible for the study and 180 (90%) completed it. All patients had their histories recorded with a standard questionnaire, and underwent anthropomorphic measurements and nocturnal polysomnography. Sleep apnea was defined as more than 10 episodes of apnea or hypopnea per hour of sleep. Multiple linear and logistic regression models predictive of sleep apnea were compared with physicians' subjective impressions and previously reported models. Likelihood ratios were calculated for several levels of a sleep apnea clinical score produced by one of the linear models. Predictors of sleep apnea in the final model (R2 = 0.34) included neck circumference, hypertension, habitual snoring, and bed partner reports of nocturnal gasping/choking respirations. This model was superior to physician impression, slightly inferior to more detailed linear and logistic models, and comparable to previously reported models. A sleep apnea clinical score of less than 5 had a likelihood ratio of 0.25 (95% CI: 0.15 to 0.42) and a corresponding posttest probability of 17%, while a score of greater than 15 had a likelihood ratio of 5.17 (95% CI: 2.54 to 10.51) and posttest probability of 81%. These likelihood ratios can simply and accurately determined the probability of whether a patient has sleep apnea.

A systematic review of screening questionnaires for obstructive sleep apnea

“…The characteristics of the included studies are summarized in Table 1. The excluded studies 11,[16][17][18][19][20][22][23][24] and the reasons for their exclusion are listed in Table 2.…”

“…Rowley et al 8 prospectively studied the utility of four clinical prediction models (Crocker, 9 Viner, 10 Flemons, 11 and Maislin 12 ) and concluded that they are not sufficiently accurate to discriminate between patients with or without OSA. In addition, some of these clinical models require the assistance of a computer and sophisticated mathematical calculations.…”

“…Studies were excluded from the review if there was inadequate information to draw the 2 by 2 contingency tables (seven papers). 11,[16][17][18][19][20] A validated computer program for meta-analysis of test accuracy data (Meta-DiSc, 21 version 1.4, Hospital Ramony Cajal, Madrid, Spain) was used to describe the overall accuracy of the questionnaires and to assess inconsistencies in accuracy parameters (sensitivity and specificity) across studies (heterogeneity). Accuracy parameters with a similar target population were analyzed together (sleep-disorder patients vs patients without history of sleep disorders).…”

Obstructive sleep apnoea syndrome-Clinical diagnosis versus limited channel diagnostic nocturnal polysomnography

“…There are various other studies showing strong correlation between OSAS, neck circumference and BMI 10,15,16,17 . In our study they were not statistically significant.…”

The role of Sleep Apnea Clinical Score (SACS) as a pretest probability in obstructive sleep apnea

“…• Snoring • Witnessed episodes of apnea • Neck circumference and • Systemic hypertension [8,9] In our study, patients were screened for the presence of OSA based on their SACS scores. It was found out that 74.15% of patients having a moderate to severe SACS score had OSA.…”