Investigation of Predictive Indices to Objectively Guide Choice of CPAP Interface

Obstructive Sleep Apnea of Adult Clinical Trial

Official title: Selecting the Appropriate CPAP Interface: Investigation of Predictive Indices to Objectively Guide the Choice of Interface

Status Completed

Clinical Trial Summary

Obstructive sleep apnea (OSA) is a prevalent disease, and one that is associated with significant negative impact on long-term survival. There is convincing evidence that treatment of OSA can improve outcome in patients with cardiovascular disease, improve blood pressure and blood glucose control, and improve the overall metabolic syndrome. Treatment is usually with continuous positive airway pressure (CPAP). However, many patients face problems with their CPAP interfaces, affecting their compliance and response to treatment. Proper mask fit is important in decreasing air leak and increasing comfort, thus leading to better compliance. Currently, the choice of interfaces depends mainly on the experience of the sleep disorder unit (SDU) technicians. Unsuitable choice of interface not only affects response to treatment but also increases medical costs when interfaces need to be replaced to better fitting ones. It is thus important to develop selection criteria to objectively guide the SDU technicians in choosing the suitable interfaces. The objective of this study is to study the facial profile of patients and look for predictive indices that can be used to objectively guide the choice of appropriate interfaces. Frontal and side profile photographs of patients will be taken and facial measurements will be recorded. Patients will then be given a one monthly trial of each of the 3 interfaces (nasal masks, oronasal masks, nasal cushions) in a randomised order. The patients' subjective perception of each of the interface will be assessed and patients will nominate their preferred interface at the end of the trial. The amount of leak and the hours of adherence for each type of interface will also be analysed. A multinomial logistic regression analysis will then be done to look for facial measurements that will predict the interface that patients will be most satisfied with.

Hypothesis That measurement of facial structures and clinical features will be able to predict the best-fit CPAP interface for each patient.

Study Objective

• To determine anthropometric facial measurements and clinical factors important in achieving a good interface fit for each patient

• To derive a prediction rule or equation that will be able to accurately predict which interface a patient should be prescribed

Clinical Trial Description

This clinical research will be a prospective crossover trial in which frontal and side facial profile photographs of patients will be taken and facial measurement will be recorded including the height and width of the face and nose. Body Mass Index (BMI), Apnea Hypopnea Index (AHI) and Epworth Sleepiness Score(ESS) will be recorded. Baseline symptoms of mouth breathing and nasal congestion will also be assessed. Patients will undergo a quick 5minute trial of the interfaces at CPAP setting of 6cm H2O and give feedback of their preferred interface and satisfaction of each interface (Visual analogue scale) at the end of the quick trial. Patients will then be given a one month trial of each of the 3 interfaces (nasal masks, oronasal masks, nasal cushions) in a randomised order. For purpose of standardisation to avoid confounders, humidification and auto-titrating CPAP (autoPAP) mode of ventilation will be used in all patients. The brand of interfaces and CPAP devices used will also be standardised. This standardised CPAP treatment will be provided free of charge to patients during the duration of the study (3 months). The patients' subjective perception of each of the interface will be assessed with a visual analogue scale to assess level of satisfaction. Patients will nominate their preferred interface at the end of the trial. The amount of leak and the hours of adherence to CPAP for each type of interface will also be analysed by downloading data from the CPAP devices. A multinomial logistic regression analysis will then be done to look for facial measurements and factors that will predict the interface that patients will be most satisfied with. ;

Related Conditions & MeSH terms

• Obstructive Sleep Apnea of Adult
• Sleep Apnea, Obstructive

• NCT number: NCT03063567
• Study type: Interventional
• Source: Singapore General Hospital
• Status: Completed
• Phase: N/A
• Start date: August 2014
• Completion date: March 30, 2017