Sleep Disorders |

Mathematical Probability Model for Obstructive Sleep Apnea Syndrome (OSAS) FREE TO VIEW

Antonio Jesus Dueñas Fuentes, MD; Ana Mochón Doña; Ana Milagrosa Escribano Dueñas, MS; Juan Antonio Piña Fernandez; Diego Gachet Paez
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Air Liquide Healthcare, Malaga, Spain

Chest. 2014;145(3_MeetingAbstracts):597A. doi:10.1378/chest.1785482
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SESSION TYPE: Poster Presentations

PRESENTED ON: Saturday, March 22, 2014 at 01:15 PM - 02:15 PM

PURPOSE: Establish an econometric probability model to serve as a complementary tool to support the diagnostic test of respiratory polygraphy to predict the probability that a patient has to suffer OSAS

METHODS: It seeks to establish a Logit regression probability model with an explained variable Apnea and eight independent variables. We have performed a retrospective study in which we analyzed the different variables of 600 patients who have been subjected of a polygraphy diagnostic test. The eight apnea explanatory variables used in the study are: BMI, Epworth Scale, Snoring, Objectified Apnea pauses, Alcohol, Associated Othorhinolaryngology Pathologies, Gender and Age. Once all the data have been taken from the patients, were statistically analyzed and verified the significance of each variable in the regression model

RESULTS: The variables that are significant at the 5% (P-value <5%) are BMI, Objectified Apnea Pauses, Associated Othorhinolaryngology Pathologies , Gender and Age. These variables are positive and significant, so it is interpreted that the higher the BMI is, if a patient has Objectified Apnea Pauses or Associated Othorhinolaryngology Pathologies , or is older or male, the greater the probability that a patient has to suffer OSAS. The model was able to correctly predict 94.84% of patients suffering from OSAS

CONCLUSIONS: The specific probability model is: P = - 6,44389 + 0,0964407xBMI + 1,64257x Objectified Apnea Pauses + 0,575505x Associated Othorhinolaryngology Pathologies + 1,48944xGender + 0,0522672xAge The model is found globally significant and each variable has individual significance.

CLINICAL IMPLICATIONS: This prediction model is a useful tool to guide diagnosis of OSAS to Primary Care Physicians. For the Specialized Care Physicians, this model is useful as a prediagnostic tool to assess the priority to perform the polygraphies according to the probability percentage of every patient. In terms of driving, it is a simple tool to assess whether a driver should have a polygraphy before obtaining the driving license

DISCLOSURE: The following authors have nothing to disclose: Antonio Jesus Dueñas Fuentes, Ana Mochón Doña, Ana Milagrosa Escribano Dueñas, Juan Antonio Piña Fernandez, Diego Gachet Paez

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