MELANIA DIRCE OLIVEIRA MARQUES
Projetos de Pesquisa
Unidades Organizacionais
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina - Médico
2 resultados
Resultados de Busca
Agora exibindo 1 - 2 de 2
- Predicting sleep apnea responses to oral appliance therapy using polysomnographic airflow(2020) VENA, Daniel; AZARBARZIN, Ali; MARQUES, Melania; BEECK, Sara Op de; VANDERVEKEN, Olivier M.; EDWARDS, Bradley A.; CALIANESE, Nicole; HESS, Lauren B.; RADMAND, Reza; HAMILTON, Garun S.; JOOSTEN, Simon A.; TARANTO-MONTEMURRO, Luigi; KIM, Sang-Wook; VERBRAECKEN, Johan; BRAEM, Marc; WHITE, David P.; SANDS, Scott A.; WELLMAN, AndrewStudy Objectives: Oral appliance therapy is an increasingly common option for treating obstructive sleep apnea (OSA) in patients who are intolerant to continuous positive airway pressure (CPAP). Clinically applicable tools to identify patients who could respond to oral appliance therapy are limited. Methods: Data from three studies (N = 81) were compiled, which included two sleep study nights, on and off oral appliance treatment. Along with clinical variables, airflow features were computed that included the average drop in airflow during respiratory events (event depth) and flow shape features, which, from previous work, indicates the mechanism of pharyngeal collapse. A model was developed to predict oral appliance treatment response (>50% reduction in apnea-hypopnea index [AHI] from baseline plus a treatment AHI <10 events/h). Model performance was quantified using (1) accuracy and (2) the difference in oral appliance treatment efficacy (percent reduction in AHI) and treatment AHI between predicted responders and nonresponders. Results: In addition to age and body mass index (BMI), event depth and expiratory ""pinching"" (validated to reflect palatal prolapse) were the airflow features selected by the model. Nonresponders had deeper events, ""pinched"" expiratory flow shape (i.e. associated with palatal collapse), were older, and had a higher BMI. Prediction accuracy was 74% and treatment AHI was lower in predicted responders compared to nonresponders by a clinically meaningful margin (8.0 [5.1 to 11.6] vs. 20.0 [12.2 to 29.5] events/h, p < 0.001). Conclusions: A model developed with airflow features calculated from routine polysomnography, combined with age and BMI, identified oral appliance treatment responders from nonresponders. This research represents an important application of phenotyping to identify alternative treatments for personalized OSA management. Statement of Significance Treatment response to oral appliance in patients with obstructive sleep apnea can be predicted at baseline from metrics derived from routine polysomnography.
- Predicting epiglottic collapse in patients with obstructive sleep apnoea(2017) AZARBARZIN, Ali; MARQUES, Melania; SANDS, Scott A.; BEECK, Sara Op de; GENTA, Pedro R.; TARANTO-MONTEMURRO, Luigi; MELO, Camila M. de; MESSINEO, Ludovico; VANDERVEKEN, Olivier M.; WHITE, David P.; WELLMAN, AndrewObstructive sleep apnoea (OSA) is characterised by pharyngeal obstruction occurring at different sites. Endoscopic studies reveal that epiglottic collapse renders patients at higher risk of failed oral appliance therapy or accentuated collapse on continuous positive airway pressure. Diagnosing epiglottic collapse currently requires invasive studies (imaging and endoscopy). As an alternative, we propose that epiglottic collapse can be detected from the distinct airflow patterns it produces during sleep. 23 OSA patients underwent natural sleep endoscopy. 1232 breaths were scored as epiglottic/nonepiglottic collapse. Several flow characteristics were determined from the flow signal (recorded simultaneously with endoscopy) and used to build a predictive model to distinguish epiglottic from nonepiglottic collapse. Additionally, 10 OSA patients were studied to validate the pneumotachograph flow features using nasal pressure signals. Epiglottic collapse was characterised by a rapid fall(s) in the inspiratory flow, more variable inspiratory and expiratory flow and reduced tidal volume. The cross-validated accuracy was 84%. Predictive features obtained from pneumotachograph flow and nasal pressure were strongly correlated. This study demonstrates that epiglottic collapse can be identified from the airflow signal measured during a sleep study. This method may enable clinicians to use clinically collected data to characterise underlying physiology and improve treatment decisions.