COVID-19 Clinical Trial
Official title:
Differential Diagnosis of Persistent COVID-19 by Artificial Intelligence
The pandemic caused by SARS-CoV-2 infection has resulted, in addition to the well-known acute symptoms, in the emergence of persistent, diffuse and heterogeneous symptoms referred to as persistent COVID. Common symptoms include fatigue, shortness of breath, and cognitive dysfunction, among others, and result in an impact on daily functioning. Symptoms may be new onset, appear after initial recovery from an acute episode of COVID-19, or persist after the initial illness. Cardiac variability (HRV) was initially used in COVID-19 to predict mortality in the acute setting. Dysautonomia which partly evaluates HRV is frequent in patients with persistent COVID. Several groups have used voice or other respiratory noise analysis for the diagnosis of acute COVID. Patients in the persistent COVID cohort will be able to be differentiated from an age, sex and vaccination status matched cohort of recovered COVID patients without sequelae by means of a model created by Machine Learning that will be trained using cardiac variability (HRV), skin conductance and acoustic analysis data. The primary objetive will be to obtain a classification algorithm by Machine Learning to differentiate the group of patients with persistent COVID diagnosis from the paired group of recovered COVID patients without sequelae.
This is a validation study of a Machine Learning algorithm for the diagnosis of persistent COVID using clinical diagnosis as the "gold standard". The sample will be composed of post-COVID patients, one group of which developed persistent COVID and another paired with the previous one with cured COVID without sequelae. ;
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