Respiratory Insufficiency, Sleep Disordered Breathing
Conditions
Keywords
Breathing pattern, Gas exchange, Sleep quality
Brief summary
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
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.
Interventions
DEVICEAutoVPAP with addition of AutoEPAP
Implementation of automated algorithm for adjustment of conventional device parameter (EPAP0.
DEVICEAutoVPAP with EPAP manually selected
Conventionally applied Expiratory Positive Airway Pressure (EPAP)
Sponsors
University Hospital, Essen
ResMed
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
TREATMENT
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Index of Apneoas Plus Hypopnoeas Per Hour of Sleep (AHI) | On completion of each consecutive night of polysomnography. | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Mean SpO2 | On completion of each night of 2 consecutive nights polysomnography. | During sleep, pulse oximetery is recorded through a sensor on the participants finger |
Countries
Germany
Participant flow
Recruitment details
Respiratory insufficiency and hypercapnia requiring noninvasive ventilation. Stable on bi-level CPAP. Both genders. Age \>18 and \<75years. Requiring EPAP \>5cmH2O for upper airway patency, or expected to (e.g. obese patients). Some known to have airway patency at EPAP of 5cmH2O will be included to test for falsely positive response.
Participants by arm
| Arm | Count |
|---|---|
| All Participants Treatment period with conventional device modified to enable algorithm for automatically applied Expiratory Positive Airway Pressure
AutoVPAP: Implementation of automated algorithm for adjustment of conventional device parameter during a single night of polysomnography following randomisation. | 21 |
| Total | 21 |
Baseline characteristics
| Characteristic | All Participants |
|---|---|
| Age, Customized Age in years 19-30 | 2 Participants |
| Age, Customized Age in years 31-40 | 0 Participants |
| Age, Customized Age in years 41-50 | 1 Participants |
| Age, Customized Age in years 51-60 | 7 Participants |
| Age, Customized Age in years 61-70 | 7 Participants |
| Age, Customized Age in years 71-80 | 4 Participants |
| FEV1/FVC | 52 % |
| FEV1(%predicted) | 37.5 %predicted |
| Region of Enrollment Germany | 21 count of participants |
| Sex: Female, Male Female | 11 Participants |
| Sex: Female, Male Male | 10 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 0 / 21 | 0 / 21 |
| other Total, other adverse events | 0 / 21 | 0 / 21 |
| serious Total, serious adverse events | 0 / 21 | 0 / 21 |
Outcome results
Primary
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
Time frame: On completion of each consecutive night of polysomnography.
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Automatic Algorithm - AutoVPAP With Addition of AutoEPAP | Index of Apneoas Plus Hypopnoeas Per Hour of Sleep (AHI) | 1.3 Events per hour of sleep | Standard Deviation 2.3 |
| Conventional Therapy - AutoVPAP With EPAP Manually Selected | Index of Apneoas Plus Hypopnoeas Per Hour of Sleep (AHI) | 1.7 Events per hour of sleep | Standard Deviation 2.8 |
Secondary
Mean SpO2
During sleep, pulse oximetery is recorded through a sensor on the participants finger
Time frame: On completion of each night of 2 consecutive nights polysomnography.
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Automatic Algorithm - AutoVPAP With Addition of AutoEPAP | Mean SpO2 | 92 Percent | Standard Deviation 3 |
| Conventional Therapy - AutoVPAP With EPAP Manually Selected | Mean SpO2 | 93 Percent | Standard Deviation 3 |