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Early Detection of Acute Respiratory Failure Using an Intelligent Respiratory Monitoring System
Sponsor: University of Pernambuco
Summary
Introduction: The monitoring of respiratory patterns is crucial in the management of respiratory diseases, but in many cases, it still relies on subjective and visual assessment. The use of healthcare technologies based on artificial intelligence (AI) can, in these contexts, enhance clinical decision-making by providing a more objective and accurate analysis. Given the high prevalence of acute and chronic respiratory diseases, the implementation of a device capable of detecting variables such as flow, volume, and time becomes a priority for more effective diagnosis and therapeutic planning. Objective: Evaluate the accuracy, validity, and usability of an intelligent system for monitoring the respiratory pattern of patients at risk of acute respiratory failure. Methods: This is a prospective cohort study that will be conducted in the emergency departments of the Otávio de Freitas Hospital and Urgent Care Units (UPAs). The sample will consist of volunteers of both sexes, aged 18 years or older, breathing spontaneously, and suspected of having acute respiratory failure. Screening will be performed daily, where sociodemographic information, blood gas data, laboratory results, and additional information will be collected. When indicated, pulmonary function tests, respiratory muscle strength tests, and diaphragmatic ultrasound will be conducted. Respiratory pattern data will be collected using the Respiratory Diagnostic Assistant. Statistical analysis will be performed according to data modeling and treatment, adopting significant differences with p \< 0.05. Expected Results: It is expected that the results of this study will provide quantitative data on the respiratory pattern of volunteers suspected of having acute respiratory failure. This information will be integrated into a database with the aim of enhancing the device's ability to detect changes in respiratory patterns, as well as contributing to the development of artificial intelligence capable of accurately and efficiently identifying these changes.
Official title: Accuracy, Validation, and Usability of an Intelligent Respiratory Pattern Monitoring System in Patients at Risk of Acute Respiratory Failure
Key Details
Gender
All
Age Range
18 Years - 90 Years
Study Type
OBSERVATIONAL
Enrollment
300
Start Date
2025-08-18
Completion Date
2025-12-22
Last Updated
2025-09-10
Healthy Volunteers
Yes
Interventions
Respiratory Pattern Monitoring
Respiratory pattern assessment and continuous monitoring will be conducted utilizing the Respiratory Diagnostic Assistant, a device capable of providing comprehensive quantitative evaluation of respiratory variables including frequency, volume, flow rates, inspiratory/expiratory timing parameters, and respiratory entropy analysis, as well as qualitative analysis of respiratory pattern curves for volume and flow dynamics, incorporating entropy measurements for assessment of respiratory pattern complexity and variability.
Diaphragmatic Excursion
Diaphragmatic mobility assessment will be conducted using diaphragmatic ultrasonography, a method capable of providing comprehensive quantitative evaluation of diaphragmatic excursion parameters including range of motion, contraction velocity, muscle thickening during inspiration and expiration, as well as qualitative analysis of diaphragm muscle morphology and echogenicity, incorporating craniocaudal displacement measurements for assessment of contractile function and early detection of diaphragmatic dysfunction."
Respiratory muscle electrical activity
espiratory muscle electrical activity assessment will be conducted using surface electromyography, a method capable of providing comprehensive quantitative evaluation of muscle activation parameters including electrical signal amplitude, firing frequency, muscle recruitment patterns during inspiration and expiration, as well as qualitative analysis of synchronization and coordination between respiratory muscles, incorporating Root Mean Square (RMS) measurements and spectral analysis for assessment of respiratory neuromuscular function and detection of muscle fatigue.
Respiratory Muscle Strength
Respiratory muscle strength assessment will be conducted using manovacuometry, a method capable of providing comprehensive quantitative evaluation of respiratory muscle force parameters including maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP), as well as qualitative analysis of contractile capacity and muscle endurance, incorporating peak pressure measurements and performance indices for assessment of global respiratory muscle function and early detection of muscle weakness
Pulmonary Function Assessment
Pulmonary function assessment will be conducted using spirometry, a method capable of providing comprehensive quantitative evaluation of respiratory function parameters including forced vital capacity (FVC), forced expiratory volume in one second (FEV1), FEV1/FVC ratio, and forced expiratory flow 25-75% (FEF25-75%), as well as qualitative analysis of flow-volume and volume-time curves, incorporating lung capacity and volume measurements for assessment of respiratory mechanics and early detection of obstructive and restrictive ventilatory disorders."
Data Acquisition System
The PowerLab C is a biomedical data acquisition system used as an auxiliary tool for physiological monitoring. In this study, it will be employed to collect respiratory signals and diaphragmatic excursion data through specific sensors, integrated with LabChart software for data visualization and analysis. The device is not considered an investigational intervention, but a supporting tool for data collection.
Nijmegen Questionnaire
The Nijmegen Questionnaire is a standardized self-report instrument designed to assess symptoms related to dysfunctional breathing and hyperventilation. In this study, it will be administered to evaluate respiratory symptoms and their impact on participants' daily life. The questionnaire is used solely for data collection and is not considered a therapeutic intervention.
System Usability Scale (SUS)
The System Usability Scale (SUS) is a standardized questionnaire used to evaluate the usability and user experience of systems and devices. In this study, it will be applied to assess participants' perceptions of the usability of an intelligent respiratory monitoring system. The SUS will be used solely as a data collection tool and does not constitute a therapeutic intervention.
Borg Scale
The Borg Scale is a standardized self-report instrument used to assess perceived exertion and breathlessness during physical activity. In this study, it will be applied to evaluate participants' perception of respiratory effort. The scale is used solely for data collection and is not considered a therapeutic intervention.
Patient Identification Questionnaire
The Patient Identification Questionnaire is a standardized form used to collect sociodemographic and basic clinical information from participants, such as age, sex, and medical history. In this study, it will serve solely for data collection and will not constitute a therapeutic intervention.
Peak Expiratory Flow (PEF) Measurement
Peak Expiratory Flow (PEF) measurement is a rapid and noninvasive method to assess the maximum expiratory volume a participant can achieve after a full inhalation. In this study, participants will be seated upright and use a mouthpiece connected to a peak flow meter. After a deep inspiration, they will exhale forcefully, and three measurements will be taken with at least one minute between attempts; the highest value will be recorded. PEF values provide an objective measure of airflow limitation, correlate with asthma symptoms, and will be used solely for data collection, not as a therapeutic intervention
Locations (3)
Emergency Care Unit from Engenho Velho
Jaboatão dos Guararapes, Pernambuco, Brazil
Department of Physical Therapy
Recife, Pernambuco, Brazil
Otávio de Freitas Hospital
Recife, Pernambuco, Brazil