1. Objectives: 1.-To create risk stratification scales of poor evolution in patients infected by SARS-CoV-2. 2.-Evaluate the accessibility and equity that these patients have had in the different care processes, diagnostic and therapeutic procedures, with special interest in patients who came from residences, by age, gender or geographic origin.3.-Evaluate the effectiveness of different therapeutic schemes that have been used in this pandemic. 4.-Evaluate the effectiveness of different diagnostic tests used to predict the poor evolution of these patients 5.- Evaluate the real costs associated with the treatment of hospitalized patients with COVID-19 ; 2. Methods: Information will be recorded from electronic medical record: epidemiological data, onset of symptoms, comorbidities and their treatments, symptoms, analytical data, vital signs, tests performed, treatments during admission and evolution up to 3 months after discharge. Statistical analysis: The investigators will use classic survival models, logistic regression, generalized linear models and also analysis using artificial intelligence techniques . Health care costs are assessed. Applications for decision making will be derived as a product.

Background: One of the fundamental problems of this epidemic is determined by the high percentage of SARS-CoV-2 infected patients who present rapid clinical deterioration that makes them need care in critical units. Identifying which factors are related to these more severe conditions would allow us to assess whether preventive or therapeutic measures can be put in place in advance or to better plan the services to be provided to these patients, either in this wave of the pandemic or in those that may occur in the future.

Objectives: This project aims to create stratification scales of the risk of poor evolution in patients infected by SARS-CoV-2, defined as the appearance of clinical deterioration, ARDS, sepsis, SRIS, septic shock or death. Additional goals are: 1.-Evaluate the accessibility and equity that these patients have had in the different care processes, diagnostic and therapeutic procedures, with special interest in patients who came from residences, by age, gender or geographic origin. 2.-Evaluate the effectiveness of different therapeutic schemes that have been used in this pandemic. 3.-Evaluate the effectiveness of different diagnostic tests used to predict the poor evolution of these patients 4.- Evaluate the real costs associated with the treatment of hospitalized patients with COVID-19.

Methods: The information will be extracted from the electronic medical record mostly, but will have to be done manually for certain fundamental parameters of prediction (clinical manifestations, date of onset of symptoms and duration of symptoms, and epidemiological history). Statistical analysis: Logistic regression/survival models/artificial intelligence algorithms will be created for the prediction of poor evolution of patients with CoVid-19.

Two samples are included: 1st.-All people SARS-CoV-2 positive from the Basque Country (around 380000 people) from March 2020 to January 2022; 2nd.-Patients admitted for COVID 19 in the centers participating in the study during the first wave of the pandemic, until May 31, will be included (in the case of the Basque Country, some of these patients will come from the population sample #1 described before). If there were new waves of a certain entity (more than 100 admissions in a month per center), this information would also be collected later. With the information the investigators have so far, the investigators see that the investigators would have between 6000-7000 to select. Later, patients from the autumn wave would be collected, if it were given, until the end of May 2021, due to greater temporal similarity with the first wave.

Sampling: 1st sample. - All people SARS-CoV-2 positive from the Basque Country from March 2020 to January 2022. 2nd sample. -The information to be reviewed from the medical record will be collected from the first wave of the pandemic between March-May 2020, where a random sampling will be carried out . For the second wave of autumn-winter of 2020-2021, a random sample of patients will also be collected, enough to meet the estimated sample size for this second wave. If not, the sample size will be completed with patients from the first wave.

VARIABLES: 1st sample. Sociodemographic, baseline comorbidities (including those of the Charlson Comobidity Index and based on ICD codes), baseline treatments (based on the Anatomical, Therapeutic, Chemical [ATC] classification system) [19]; vaccination status; dates of hospital admission and discharge and whether patients were admitted to an intensive care unit (ICU); and vital status. From those attended at any ED, we recorded vital signs (body temperature, blood pressure, heart rate and O2 saturation), gasometry, laboratory and chest X Ray image test, all from the unified electronic database.

2nd sample. -Exposure: 1.-Sociodemographic data: Age, gender, residence (yes / no), country of origin. 2.- Personal history: associated diseases; Basal treatments, etc. 3.-History of the disease 4.-Physical examination at home or AP. 5.-Hospital history: symptoms on arrival at the emergency department, vital signs, signs and physical examination, Laboratory tests, chest radiography pattern, CAT pattern, established treatments, ICU data.

Outcomes: 1st sample. -Hospital, ICU admission and death up to 90 days. 2nd sample. - Clinical impairment: Dyspnea at rest, Development of ARDS, sepsis, SIRS, shock, ICU admission, Death (date). Relief of symptoms, days until the absence of disease, death.

Follow-up: 1st sample. -Hospital, ICU admission and death up to 90 days. 2nd sample (6 months). Readmissions, New diagnoses, Complications, Biomarkers of fibrogenesis, Results of the diagnostic procedure (radiographs, MRI, CT), Death (with date and cause) Costs (index and 6 months income): Emergency or programmed admission; number of days of admission (in each of the Units / Plants / ICU / Emergencies); laboratory tests (number and type); number of days in which respiratory support was required; treatments used throughout the stay (drug, dose, dosage, duration); diagnostic procedures (radiographs, MRI, CT, etc.) performed during the study period; surgical procedures performed; external consultations (number and Service); day hospital (number and procedures); AP and home visits (related to COVID-19).

DATA COLLECTION METHODS: 1st sample: All data on patients in the care of our health service are held in a unified electronic database. Analysts retrieved previously described data for all SARS-CoV-2 infected patients during the study period. 2nd sample. Manual data extraction will be carried out by reviewers under the supervision of each PI per center. All the collected data will be entered in the RedCap database. Once the information is extracted, a common database will be created for subsequent analysis.

STATISTIC ANALYSIS. The study unit will be the patient. A descriptive analysis of the entire sample will be carried out. A univariate analysis will be performed to determine potential factors or variables related to the outcome variables of interest. In the multivariate analysis, different models will be carried out according to the dependent variable of interest. In the case of dichotomous dependent variables, logistic regression and Lasso models will be used. Statistical significance will be assumed when p <0.05 and all analyzes will be performed using SAS v9.4 and R statistical software. Also, the prediction of the variables will be evaluated individually by measuring the statistical correlation between each variable and the poor evolution; and collectively looking at the ability to predict the bad evolution from combinations, which will be obtained by generating Association Rules between variables from the underlying statistical relationships.

The analysis of the comparative effectiveness between the different treatment options that have been observed will be carried out by intention to treat. In addition to descriptive statistical techniques, a time-to-event (mortality) survival analysis will be performed using multivariate Cox proportional hazards regression, and a parametric survival analysis with the corresponding distribution (Weibull, etc.) together with an estimate. of average survival. For the evaluation of comparative effectiveness, propensity score techniques will be used to create comparable treatment groups by adjusting baseline covariates by inverse weighting of treatment probability. Additionally, and because it is foreseeable that there will be multiple treatment groups, the specific estimation procedure called generalized boosted models will be applied.

For the analysis of cost data, both for the analysis of associated variables and for a cost comparison objective, GLM regression techniques will be used with the type of distribution that best fits the data (using the Modified Park Test ), although preferably the gamma and logarithm family will be used as the link. The data will be analyzed with the Stata v14.2 program.

ETHICAL AND CONFIDENTIALITY ASPECTS. The project has been evaluated by the research commissions and the Research Ethics Committee with Medicines (CEIm), where it has been approved. The laws on personal data will be followed (RGPD 2018) All information will be treated in an absolutely confidential manner.

Expected results: A prognostic stratification tool based on predictive models of poor evolution in CoVid-19 infection: clinical deterioration and development of ARDS, SRS, sepsis, and/or septic shock and/or death. This tool will help guide the most appropriate clinical management of patients, mainly those with the most severe presentations that may require attention in critical care units. Additionally, purposes of this study are also to provide information on the variability and costs in the provision of health care that may have been given, both in the use of diagnostic tests and in the use of different therapeutic options and also in the results finally obtained. The investigators seek to identify problems in the accessibility of different groups (elderly, people in residences, by gender, higher level of comorbidities, immigrants ...), and that can help us identify problems in equity in access to health services.

Inclusion Criteria:

• Positive COVID19 people in the Basque country
• Patients admitted (confirmed cases) by CoVid-19

Exclusion Criteria:

• Pediatric population (for objective #1 only)

• Positive COVID19 people in the Basque Country
• Patients admitted by Covid-19 to hospitals in the Basque Country, from 01/02/2020 to 01/04/2020