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Breathing is an automatic vital function that has the peculiarity of being controllable voluntary for actions other than breathing. Speech production is a characteristic example of use of the respiratory system for nonrespiratory purposes. A healthy respiratory system is necessary for speech to be adequately produced and modulated. In patients with respiratory diseases, it becomes difficult to interfere with an automatic control of breathing that is intensely active to compensate for the respiratory deficience. Speech production is impeded, and, reciprocally, speech can generate dyspnea. This study explores the hypothesis that longitudinal changes in speech characteristics will parallel the clinical evolution of acute respiratory episodes. The aim is to validate such changes as prognostic indicators, in the perspective of future telemedicine applications. The hypothesis tested is that of an association between :
In the conceptual framework describe in the "brief summary" section of this document, this observational longitudinal monocentric study will include consecutive patients admitted in a specialised respiratory medicine ward for acute respiratory episodes. Any such episode will be considered be it "de novo" or complicating an underlying chronic respiratory disease. Vocal recordings will be performed daily, and will be analysed according to standard in the fields. Clinical parameters will also be recorded daily (vital signs, treatment intensity, outcome -including requirement for treatment intensification, transfer to the ICU, death, discharge to rehabilitation facility, discharge to home). The clinical follow-up and the vocal follow-up will be confronted to determine if voice analysis has an intrinsic prognostic value, alone, or in combination with clinical signs.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Arm 1 | Experimental | intervention correspond to the voice registration |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Voice registration | Other | Voice registration |
|
| Measure | Description | Time Frame |
|---|---|---|
| Characterize voice analysis as a biomarker of respiratory status and its evolution in patients hospitalized in pneumology using machine learning algorithmshospitalized in pneumology | machine learning algorithms trained on the audio database obtained from patients discussion with medical staff. Voice parameters: respiratory rythms and intensity, and articulatory performances, will be extracted from voice recording, combined and analysed by the algorithms. | 1 month |
| Measure | Description | Time Frame |
|---|---|---|
| Correlation of the used of algorithms based on voice and medical diagnosis. | Medical diagnosis based on physiological parameters (heart rate (bpm) ; oxygen saturation (%) ; respiratory rate (cycle/min)) will be carried out in the routine care and correlated with the algorythms results. | 1 month |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Thomas SIMILOWSKI, MD, PhD | Assistance Publique - Hôpitaux de Paris | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Departement of Respiratory Medicine , Pitié-Salpêtrière Hospital | Paris | France | 75013 | France |
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| ID | Term |
|---|---|
| D011014 | Pneumonia |
| D004417 | Dyspnea |
| D013060 | Speech |
| ID | Term |
|---|---|
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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| D012120 |
| Respiration Disorders |
| D012818 | Signs and Symptoms, Respiratory |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D014705 | Verbal Behavior |
| D003142 | Communication |
| D001519 | Behavior |