The retrospective study will be used to develop an artificial intelligence model of riskstratification of physiological and psychological complications arising from theinformation available in the electronic medical record and first consultation report tosupport patients and healthcare professionals in better managing the healthcare processfor patients diagnosed with long COVID.
The stratification of the risk of complications related to persistent COVID both
physiological and psychological in a personalized way would optimize the
cost-effectiveness model for the management of these patients. Similarly, the early
detection of complications associated with persistent COVID in patients belonging to
vulnerable groups would improve care times and, therefore, the patient's prognosis.
The primary objective for this study is to gather anonymized retrospective data of
patients suffering from long COVID in order to contribute to the generation of the
SENSING-AI cohort.
Other: Review of available clinical data sources related to use cases
There will be a review of available clinical data sources related to use cases. In
addition, this information will be complemented by a cohort of anonymized retrospective
data of 100 cases obtained from the clinical information resulting from the assistance to
COVID-19 patients managed by the Primary Care Health District of Sevilla Norte and the
Infectious Diseases Department of the Virgen Macarena University Hospital
Inclusion Criteria:
- Legal adult
- Diagnosed of long COVID-19 in the last year
- With the presence of any of these symptoms:
- Asthenia (Tiredness)
- Dyspnea
- Shortness of breath
- Anxiety
- Stress
- Depression
- Sleep disorder
Exclusion Criteria:
- Attended to specialized care consultation
- Was admitted in hospital in the last year due to a problem not related to the COVID
complications
Virgen Macarena University Hospital
Seville 2510911, Seville, Spain
Not Provided