COPD Acute Exacerbation
Conditions
Keywords
sleep, breathing variability
Brief summary
hronic obstructive pulmonary disease (COPD) is a common lung disease that can worsen suddenly, leading to hospital admission. During these exacerbations, breathing becomes unstable and recovery is difficult to predict. Currently, doctors lack simple tools to monitor how patients recover day by day during hospitalization and after discharge. This observational study aims to describe how breathing patterns during sleep change over time in patients hospitalized for a COPD exacerbation. Breathing will be monitored using standard sleep recordings and a non-contact sensor placed under the mattress, which measures breathing without disturbing the patient. By better understanding how nocturnal breathing variability evolves during recovery, this study may help identify early signs of improvement or deterioration, support safer hospital discharge decisions, and improve follow-up after hospitalization for COPD exacerbation
Interventions
None listed
Sponsors
Study design
Eligibility
Inclusion criteria
* Adults (≥ 18 years) with a confirmed diagnosis of chronic obstructive pulmonary disease (COPD) based on spirometry * Hospitalized for an acute exacerbation of COPD for less than 24 hours at the time of inclusion * Admitted to an acute medical ward * Able to provide written informed consent * Affiliated with the national health insurance system
Exclusion criteria
* Ongoing invasive or non-invasive mechanical ventilation at the time of inclusion * Long-term non-invasive ventilation or positive airway pressure therapy prior to hospitalization * Use of an anti-pressure (anti-decubitus) mattress incompatible with the study devices * Severe anxiety disorders interfering with study procedures * Known allergy to materials used for sensor placement * Pregnancy or breastfeeding * Minor or legally protected adult (guardianship, curatorship) or deprivation of liberty * Lack of health insurance coverage
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Nocturnal ventilatory variability during sleep assessed by polysomnography | Baseline (first night after hospitalization), Day 3 of hospitalization, Day 7 of hospitalization or the day before discharge (if discharge occurs earlier), and 6 weeks after hospital discharge (study completion) | Nocturnal ventilatory variability will be quantified during sleep using polysomnography. Variability will be assessed on a breath-by-breath basis using 30-second analysis windows and summarized by the following predefined indice SD. SD will be calculated separately for each sleep stage (N1, N2, N3, REM) and then averaged across total sleep time for each recording night. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Nocturnal ventilatory variability assessed by a non-contact respiratory sensor | Nightly during hospitalization (up to 7 days) and during one overnight recording at 6 weeks after hospital discharge | Nocturnal ventilatory variability will be assessed using a non-contact respiratory sensor (Withings Sleep Analyzer). Variability will be quantified using the same predefined indice derived from polysomnography SD. Indice will be calculated on 30-second windows during overnight recordings and averaged across total sleep time for each night. |
| Correlation between nocturnal ventilatory variability and clinical worsening during hospitalization | From baseline (first night after hospitalization) through Day 7 of hospitalization or the day before discharge (maximum hospitalization duration: 7 days) | Clinical worsening during hospitalization will be assessed as a binary outcome (clinical worsening: yes/no), defined by the occurrence of at least one of the following events: escalation of respiratory support, transfer to intensive care, need for non-invasive or invasive mechanical ventilation, in-hospital death. Nocturnal ventilatory variability will be quantified using polysomnography-derived SD1. The correlation between ventilatory variability indice and clinical worsening status will be evaluated. |
| Correlation between nocturnal ventilatory variability and physician-assessed readiness for hospital discharge | Daily from baseline (first night after hospitalization) until hospital discharge (up to 7 days) | Physician-assessed readiness for hospital discharge will be evaluated daily during hospitalization as a binary variable (ready for discharge: yes/no), based on routine clinical judgment documented in the medical record. Nocturnal ventilatory variability will be assessed using SD1 derived from overnight respiratory recordings obtained by polysomnography on scheduled nights and by a non-contact respiratory sensor during hospitalization. The correlation between ventilatory variability indices and physician-assessed readiness for discharge will be evaluated. |
| Association between nocturnal ventilatory variability and 28-day hospital readmission | Up to 28 days after hospital discharge | Hospital readmission will be assessed as a binary outcome (readmitted: yes/no) within 28 days following discharge from the index hospitalization. Nocturnal ventilatory variability will be quantified during hospitalization using SD1. The association between ventilatory variability indices and 28-day hospital readmission will be assessed using regression models, and results will be expressed as odds ratios per 1-unit increase in each variability index. |
| Correlation between nocturnal ventilatory variability and daytime ventilatory drive assessed by parasternal electromyography | Day 1, Day 3, Day 7 (or the night before discharge if discharge occurs earlier), and 6 weeks after hospital discharge. | Daytime ventilatory drive will be assessed using parasternal electromyography (EMGpara) recorded during wakefulness with surface electrodes, as part of routine physiological assessment. EMGpara measurements will be obtained at predefined time points during the index hospitalization and at follow-up. The relationship between nocturnal ventilatory variability and daytime ventilatory drive will be evaluated using correlation analyses and mixed-effects models accounting for repeated measurements within participants. |
Countries
France