The predictive value of clinical and epidemiological parameters in the identification of patients with obstructive sleep apnoea (OSA): a clinical prediction algorithm in the evaluation of OSA.

We sought to analyze the predictive value of anthropometric, clinical and epidemiological parameters in the identification of patients with suspected OSA, and their relationship with apnoea/hypopnoea respiratory events during sleep. We studied retrospectively 433 patients with OSA, 361 men (83.37%) and 72 women (16.63%), with an average age of +/-47, standard deviation +/-11.10 years (range 18-75 years). The study variables for all of the patients were age, sex, spirometry, neck circumference, body mass index (BMI), Epworth sleepiness scale, nasal examination, pharyngeal examination, collapsibility of the pharynx (Müller Manoeuvre), and apnoea-hypopnoea index (AHI). Age, neck circumference, BMI, Epworth sleepiness scale, pharyngeal examination and pharyngeal collapse were the significant variables. Of the patients, 78% were correctly classified, with a sensitivity of 74.6% and a specificity of 66.3%. We found a direct relationship between the variables analysed and AHI. Based on these results, we obtained the following algorithm to calculate the prediction of AHI for a new patient: AHI = -12.04 + 0.36 neck circumference +2.2286 pharyngeal collapses (MM) + 0.1761 Epworth + 0.0017 BMI x age + 1.1949 pharyngeal examinations. The ratio variance in the number of respiratory events explained by the model was 33% (r2 = 0.33). The variables given in the algorithm are the best ones for predicting the number of respiratory events during sleep in patients studied for suspected OSA. The algorithm proposed may be a good screening method to the identification of patients with OSA.