 selection procedure because it did not reach the specified significance level of 0.10. Both 2-way and 3-way interactions between the variables selected by the stepwise procedure were not significant (F tests) (Table 4).
The model finally selected included the FTP score, FL150, cervical perimeter, and age. The root mean square error for this model was 0.454, and the R2 statistic was 0.748. The parameter estimates and F tests are shown in Table 4 along with partial R2 values. Note that although F tests were significant, the partial contribution of FL150, age, and cervical perimeter to the fit (as measured by the partial R2 statistic) was negligible. The final predictive equation for the expected log AHI was −0.320 + 0.814 FTP (score) − 0.409 FL150 (dm3/s) + 0.033 Cervical Perimeter (cm) + 0.006 Age (years).
Obstructive sleep apnea syndrome is very common and affects a considerable proportion of the population. If untreated, it causes considerable increases in cardiac and cerebrovascular morbidity and mortality.13 Many patients with obstructive OSAS remain undiagnosed owing to inadequate resources for case detection and investigation, a situation that has clear repercussions for their quality of life.3,6,7,14-21 Moreover, OSAS represents a considerable socioeconomic burden, owing to comorbidity, increased health care utilization in the primary and secondary health care sectors, increased use of medication, impact on employment, and loss of income. Treatment of OSAS reduces morbidity, mortality, and hospitalization rates, and its cost-effectiveness has been demonstrated.22 So, there is a clear need to establish optimal procedures to identify patients at risk.
Traditionally, OSAS diagnosis has been performed using in-hospital, overnight polysomnography. This procedure is time consuming and expensive, and, since its capacity is limited, waiting lists tend to lengthen.
In recent decades, several attempts have been made to find predictive models based on clinical examination of the upper airway, combined or not with clinical data such as the Epworth Sleepiness Score and/or specific radiological examinations. These models have proved difficult to implement, at least to some extent because the description of the anatomical regions involved has not been uniform.23-26
Several studies have shown a statistical relationship between certain morphological abnormalities and OSAS severity. Tonsil size9,27,28 and cervical perimeter27,28 seem to be related to OSAS severity, although their specific weight in the pathophysiological mechanisms of the disease is not clear. The Mallampati score seems to be the only parameter that is strongly related to OSAS severity,9,27,29-33 but there seem to be certain racial variations, with Asians scoring higher for both Mallampati score and OSAS severity32,33 than white participants.
Our study corroborated the results in the literature referring to the association of AHI with age, sex, BMI, and cervical perimeter.27,29 However, our multiple regression analysis showed that all these variables had a low weight for predicting OSAS severity, with the FTP score emerging as the main predictor. Sex and BMI did not significantly contribute to the prediction model, whereas age and the cervical perimeter jointly explained 1.6% of the log AHI variation (see partial R2 values in Table 4). We also considered a uvula score and performed a rhinomanometry to explore their potential usefulness as predictors. The FL150 explained only 0.9% of the log-AHI variation (Table 4), and the uvula score did not significantly improve the prediction.
The low contribution of the nasal flow to the model suggests that the degree of nasal obstruction has a negligible influence on the pathophysiological mechanisms of the disease. These results question the usefulness of grades A and B in Fujita's classification.34 Interestingly, the cervical perimeter shows the second largest R2 value, which may point to obesity as a risk factor (BMI was also significantly correlated with AHI but did not enter the model, possibly because cervical perimeter 