1.- Create risk stratification scales of poor evolution in patients affected by COVID-19, and evolutionary profile of said patients;
2.- Assess the effectiveness of the treatment and diagnostic tests;
3.- Evaluate the accessibility, equality, variability, and costs.
A prospective study of cohorts, which will include hospitalised COVID-19 patients who will be monitored for up to three months after the date of their first contact with the healthcare system. The paediatric population is excluded.
The information will be extracted manually/automatically from the electronic clinical history. The following parameters will be collected: epidemiological background, initial symptoms, clinical expression, tests made, treatments, and evolution up to 3 months after the patients’ discharge. The quality of the data will be verified and the discrepancies detected by data mining and Artificial Intelligence experts will be searched manually.
1650 patients hospitalised for COVID-19 during the first phase of the pandemic will be recruited: 900 from the Basque Country (Galdakao-Usansolo Hospital, Basurto University Hospital, and Araba University Hospital), who will be the derivation and internal validation cohort; and 750 from the Costa del Sol Hospital (Andalusia), the Canarias University Complex/Insular Materno-infantil, the Gran Canaria General Hospital, and Hospital del Mar, which will comprise the external validation cohort. In the second phase, which is expected for the fall, 850 patients from among all the participating centres will be recruited to continue with the external validation process. If this second wave does not take place, 850 additional patients will be recruited from the first wave.
The data will be analysed following the classic survival models, logistic regression, generalised linear models, and artificial intelligence techniques that assess the risks of poor evolution. Similarly, the costs of the healthcare will be estimated. As a result, data will be derived for decision-making.