Early Detection of Acute Respiratory Failure Using an Intelligent Respiratory Monitoring System

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.

NCT Number	NCT07164586
Lead Sponsor	University of Pernambuco Collaborators: Fundação de Amparo à Ciência e Tecnologia de Pernambuco, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.
Conditions	Patients at Risk of Acute Respiratory Failure
Enrollment	300 participants
Start Date	2025-08-18
Primary Completion	2025-11-28 (estimated)
Study Completion	2025-12-22 (estimated)
Updated on ClinicalTrials.gov	2025-09-10