Adjustment of Mask Pressure, for Bilevel Positive Airways Pressure Therapy, by Automated Algorithm

Study Overview
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Brief Summary
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The aim of the study is to test the hypothesis that an automated algorithm for desired mask pressure improves breathing pattern and sleep quality in patients with hypercapnic ventilatory failure. For this purpose, The investigators will study different groups of patients, including those with obstructive and restrictive ventilatory defect, and obstructive sleep apnoea, non-naive to conventional bi-level positive airways pressure therapy.

Detailed Description
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Persisting ventilatory failure associated with chronic obstructive pulmonary disease (COPD), obesity-hypoventilation-syndrome, sleep apnoea or neuromuscular disease is increasingly managed with domiciliary non-invasive positive pressure ventilation (NIPPV). Optimal settings of non-invasive ventilation are usually titrated manually and require time and expertise. The development of systems lead to automated analysis and development of algorithms to adjust ventilators. However, there is a paucity of optimal algorithms, particularly the problem of upper airway obstruction. Therefore, the central aim of this study is to develop the automated setting of an end-expiratory positive airway pressure (EPAP), because upper airway obstruction is relatively common in this group of patients. We hypothesise that an automated end-expiratory airway pressure (AutoEEP) adjusting algorithm could overcome these problems and further optimise and adjust ventilator settings. Using non-invasive ventilation in patients with hypercapnic ventilatory failure, awake and asleep, we will measure physiological outcome parameters and apply an AutoEEP algorithm, comparing it against usual care.

Official Title
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Adjustment of Non-invasive Positive Pressure Ventilation in Patients With Chronic Hypercapnic Ventilatory Failure Using Automated End-expiratory Pressure (AutoEEP) Algorithm

Conditions
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Respiratory Insufficiency, Sleep Disordered Breathing

Intervention / Treatment
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* Device: AutoVPAP with addition of AutoEPAP
* Device: AutoVPAP with EPAP manually selected


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Subjects will be patients not naive to noninvasive ventilation, and being so treated for any form of hypercapnic ventilatory failure. Previously stabilised on bilevel noninvasive pressure support ventilation. Both genders, age <75years. Previously shown to have a requirement for an EEP above cm H2O in order to maintain upper airway patency, or those in whom such a raised EEP would be expected, e.g. obese patients. Patients also known to have adequate airway patency at an EEP of 4 to 5 cm H2O will be included to ensure specificity of the algorithm. Exclusion Criteria: Acute critical illness (e.g. acute coronary syndrome, stroke) Serious anatomical variations of nose, sinuses, pharynx or oesophagus. Any condition at risk of oesophageal bleeding (e.g. oesophageal varices, gastric ulcer, etc.) Age >75 years Pregnancy Epilepsy Psychiatric disorders that could possibly influence the study Any kind of addiction Insufficient knowledge of the language Noninvasive ventilation otherwise contraindicated

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Crossover Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: AutoVPAP with addition of AutoEPAP<br>This arm will receive conventional device modified to enable algorithm for automatically applied Expiratory Positive Airway Pressure. Patients randomised to this group will then receive the other treatment the following night. | Device: AutoVPAP with addition of AutoEPAP<br>* Implementation of automated algorithm for adjustment of conventional device parameter (EPAP0.<br>Device: AutoVPAP with EPAP manually selected<br>* Conventionally applied Expiratory Positive Airway Pressure (EPAP)<br>|
| Active Comparator: AutoVPAP without addition of AutoEPAP<br>This arm will receive conventionally applied Expiratory Positive Airway Pressure. Patients randomised to this group will then receive the other treatment the following night. | Device: AutoVPAP with addition of AutoEPAP<br>* Implementation of automated algorithm for adjustment of conventional device parameter (EPAP0.<br>Device: AutoVPAP with EPAP manually selected<br>* Conventionally applied Expiratory Positive Airway Pressure (EPAP)<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Index of Apneoas Plus Hypopnoeas Per Hour of Sleep (AHI) | The AHI is a count of the number of pauses during sleep a person experiences. The total number of apneas/ hypopneas (sleep pauses) are divided by the total sleep time to get an index for that night | On completion of each consecutive night of polysomnography. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Mean SpO2 | During sleep, pulse oximetery is recorded through a sensor on the participants finger | On completion of each night of 2 consecutive nights polysomnography. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Breathing pattern, Gas exchange, Sleep quality