Archivos de bronconeumologia · 2003
[Clinical predictors of sleep apnea-hypopnea syndrome susceptible to treatment with continuous positive airway pressure].
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Martínez García M A, Soler Cataluña J J, Román Sánchez P, Cabero Salt L, Giménez Ibáñez I, Gastaldo Palop T
Аннотация
OBJECTIVE: To analyze the predictive value of clinical data for identifying patients suspected of sleep apnea-hypopnea syndrome with an apnea-hypopnea index (AHI)> or = 30.
MATERIAL AND METHODS: Patient characteristics, cardiorespiratory medical history, and clinical signs and symptoms were recorded for all patients. Exclusion criteria were daytime respiratory insufficiency or heart failure. All patients underwent polysomnographic testing (AutoSet Portable Plus II, ResMed Corp, Sydney, Australia) for automatic AHI calculation and manual determination of central and obstructive apneas. A logistic regression model was constructed to calculate the likelihood of an individual's presenting an AHI> or = 30 as well as the predictive value of each variable and of the final model.
RESULTS: Three hundred twenty-nine patients with a mean +/- SD age of 58 +/- 13.45 years were studied; 76.4% were men. Data for 207 patients were used to construct the logistic regression model: logit (P) = 2.5 blood pressure + 1.5 Epworth test + body mass index + 0.6 repeated observed episodes of apnea 2.1. Logit(P) was loge (1-P)/P and variables were dichotomized with cut points of 11 for the Epworth test and of 30 kg/m2 for body mass index. The diagnostic sensitivity of the model was 80.2% (75%-86%), specificity was 93.4% (89%-95%), positive predictive value was 89.6% (84%-93%) and negative predictive value was 86.9% (81%-90%), such that 89.6% of the patients were correctly classified. The variable with the greatest predictive value was high blood pressure. The model was validated prospectively in the remaining 102 patients.
CONCLUSIONS: Prior to diagnostic tests for SAHS, clinical data can be useful for identifying patients suspected to have a AHI> or = 30.
MATERIAL AND METHODS: Patient characteristics, cardiorespiratory medical history, and clinical signs and symptoms were recorded for all patients. Exclusion criteria were daytime respiratory insufficiency or heart failure. All patients underwent polysomnographic testing (AutoSet Portable Plus II, ResMed Corp, Sydney, Australia) for automatic AHI calculation and manual determination of central and obstructive apneas. A logistic regression model was constructed to calculate the likelihood of an individual's presenting an AHI> or = 30 as well as the predictive value of each variable and of the final model.
RESULTS: Three hundred twenty-nine patients with a mean +/- SD age of 58 +/- 13.45 years were studied; 76.4% were men. Data for 207 patients were used to construct the logistic regression model: logit (P) = 2.5 blood pressure + 1.5 Epworth test + body mass index + 0.6 repeated observed episodes of apnea 2.1. Logit(P) was loge (1-P)/P and variables were dichotomized with cut points of 11 for the Epworth test and of 30 kg/m2 for body mass index. The diagnostic sensitivity of the model was 80.2% (75%-86%), specificity was 93.4% (89%-95%), positive predictive value was 89.6% (84%-93%) and negative predictive value was 86.9% (81%-90%), such that 89.6% of the patients were correctly classified. The variable with the greatest predictive value was high blood pressure. The model was validated prospectively in the remaining 102 patients.
CONCLUSIONS: Prior to diagnostic tests for SAHS, clinical data can be useful for identifying patients suspected to have a AHI> or = 30.