<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04463706</url>
  </required_header>
  <id_info>
    <org_study_id>COVID19_0459</org_study_id>
    <nct_id>NCT04463706</nct_id>
  </id_info>
  <brief_title>COVID19 Severity Prediction and Health Services Research Evaluation</brief_title>
  <official_title>Clinical Characterization of CoVid19 Infection: Prognostic Stratification and Complications</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Hospital Galdakao-Usansolo</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Hospital Costa del Sol</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Hospital del Mar</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University Hospital of the Nuestra Señora de Candelaria</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Instituto de Salud Carlos III</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Hospital Universitario Araba</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Biocruces Bizkaia Health Research Institute</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Hospital de Basurto</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Hospital Donostia</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Hospital Galdakao-Usansolo</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      1. Objectives: 1.-To create risk stratification scales of poor evolution in patients infected&#xD;
      by SARS-CoV-2. 2.-Evaluate the accessibility and equity that these patients have had in the&#xD;
      different care processes, diagnostic and therapeutic procedures, with special interest in&#xD;
      patients who came from residences, by age, gender or geographic origin.3.-Evaluate the&#xD;
      effectiveness of different therapeutic schemes that have been used in this pandemic.&#xD;
      4.-Evaluate the effectiveness of different diagnostic tests used to predict the poor&#xD;
      evolution of these patients 5.- Evaluate the real costs associated with the treatment of&#xD;
      hospitalized patients with COVID-19 ; 2. Methods: Information will be recorded from&#xD;
      electronic medical record: epidemiological data, onset of symptoms, comorbidities and their&#xD;
      treatments, symptoms, analytical data, vital signs, tests performed, treatments during&#xD;
      admission and evolution up to 3 months after discharge. Statistical analysis: The&#xD;
      investigators will use classic survival models, logistic regression, generalized linear&#xD;
      models and also analysis using artificial intelligence techniques . Health care costs are&#xD;
      assessed. Applications for decision making will be derived as a product.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Background: One of the fundamental problems of this epidemic is determined by the high&#xD;
      percentage of SARS-CoV-2 infected patients who present rapid clinical deterioration that&#xD;
      makes them need care in critical units. Identifying which factors are related to these more&#xD;
      severe conditions would allow us to assess whether preventive or therapeutic measures can be&#xD;
      put in place in advance or to better plan the services to be provided to these patients,&#xD;
      either in this wave of the pandemic or in those that may occur in the future.&#xD;
&#xD;
      Objectives: This project aims to create stratification scales of the risk of poor evolution&#xD;
      in patients infected by SARS-CoV-2, defined as the appearance of clinical deterioration,&#xD;
      ARDS, sepsis, SRIS, septic shock or death. Additional goals are: 1.-Evaluate the&#xD;
      accessibility and equity that these patients have had in the different care processes,&#xD;
      diagnostic and therapeutic procedures, with special interest in patients who came from&#xD;
      residences, by age, gender or geographic origin. 2.-Evaluate the effectiveness of different&#xD;
      therapeutic schemes that have been used in this pandemic. 3.-Evaluate the effectiveness of&#xD;
      different diagnostic tests used to predict the poor evolution of these patients 4.- Evaluate&#xD;
      the real costs associated with the treatment of hospitalized patients with COVID-19 Methods:&#xD;
      The information will be extracted from the electronic medical record mostly, but will have to&#xD;
      be done manually for certain fundamental parameters of prediction (clinical manifestations,&#xD;
      date of onset of symptoms and duration of symptoms, and epidemiological history). Statistical&#xD;
      analysis: Logistic regression/survival models/artificial intelligence algorithms will be&#xD;
      created for the prediction of poor evolution of patients with CoVid-19.&#xD;
&#xD;
      Two samples are included: 1.-All people COVID19 positive from the Basque Country (around&#xD;
      18768 people); 2.-Patients admitted for COVID 19 in the centers participating in the study&#xD;
      during the first wave of the pandemic, until May 31, will be included (in the case of the&#xD;
      Basque Country, some of these patients will come from the population sample #1 described&#xD;
      before). If there were new waves of a certain entity (more than 100 admissions in a month per&#xD;
      center), this information would also be collected later. With the information the&#xD;
      investigators have so far, the investigators see that the investigators would have between&#xD;
      6000-7000 to select. Later, patients from the autumn wave would be collected, if it were&#xD;
      given, until the end of May 2021, due to greater temporal similarity with the first wave.&#xD;
&#xD;
      Sampling: The information to be reviewed from the medical record will be collected from the&#xD;
      first wave of the pandemic between March-May 2020, where a random sampling will be carried&#xD;
      out . For the second wave of autumn-winter of 2020-2021, a random sample of patients will&#xD;
      also be collected, enough to meet the estimated sample size for this second wave. If not, the&#xD;
      sample size will be completed with patients from the first wave.&#xD;
&#xD;
      VARIABLES: Exposure: 1.-Sociodemographic data: Age, gender, residence (yes / no), country of&#xD;
      origin. 2.- Personal history: associated diseases; Basal treatments, etc. 3.-History of the&#xD;
      disease 4.-Physical examination at home or AP. 5.-Hospital history: symptoms on arrival at&#xD;
      the emergency department, vital signs, signs and physical examination, Laboratory tests,&#xD;
      chest radiography pattern, CAT pattern, established treatments, ICU data.&#xD;
&#xD;
      Result: Clinical impairment: Dyspnoea at rest, Development of ARDS, sepsis, SIRS, shock, ICU&#xD;
      admission, Death (date). Relief of symptoms, days until the absence of disease, death.&#xD;
&#xD;
      Follow-up (6 months): Readmissions, New diagnoses, Complications, Biomarkers of fibrogenesis,&#xD;
      Results of the diagnostic procedure (radiographs, MRI, CT), Death (with date and cause) Costs&#xD;
      (index and 6 months income): Emergency or programmed admission; number of days of admission&#xD;
      (in each of the Units / Plants / ICU / Emergencies); laboratory tests (number and type);&#xD;
      number of days in which respiratory support was required; treatments used throughout the stay&#xD;
      (drug, dose, dosage, duration); diagnostic procedures (radiographs, MRI, CT, etc.) performed&#xD;
      during the study period; surgical procedures performed; external consultations (number and&#xD;
      Service); day hospital (number and procedures); AP and home visits (related to COVID-19) DATA&#xD;
      COLLECTION METHODS: Manual data extraction will be carried out by reviewers under the&#xD;
      supervision of each PI per center. All the collected data will be entered in the RedCap&#xD;
      database. Once the information is extracted, a common database will be created for subsequent&#xD;
      analysis.&#xD;
&#xD;
      STATISTIC ANALYSIS. The study unit will be the patient. A descriptive analysis of the entire&#xD;
      sample will be carried out. A univariate analysis will be performed to determine potential&#xD;
      factors or variables related to the outcome variables of interest. In the multivariate&#xD;
      analysis, different models will be carried out according to the dependent variable of&#xD;
      interest. In the case of dichotomous dependent variables, logistic regression models will be&#xD;
      used. Statistical significance will be assumed when p &lt;0.05 and all analyzes will be&#xD;
      performed using SAS v9.4 and R statistical software. Also, the prediction of the variables&#xD;
      will be evaluated individually by measuring the statistical correlation between each variable&#xD;
      and the poor evolution; and collectively looking at the ability to predict the bad evolution&#xD;
      from combinations, which will be obtained by generating Association Rules between variables&#xD;
      from the underlying statistical relationships.&#xD;
&#xD;
      The analysis of the comparative effectiveness between the different treatment options that&#xD;
      have been observed will be carried out by intention to treat. In addition to descriptive&#xD;
      statistical techniques, a time-to-event (mortality) survival analysis will be performed using&#xD;
      multivariate Cox proportional hazards regression, and a parametric survival analysis with the&#xD;
      corresponding distribution (Weibull, etc.) together with an estimate. of average survival.&#xD;
      For the evaluation of comparative effectiveness, propensity score techniques will be used to&#xD;
      create comparable treatment groups by adjusting baseline covariates by inverse weighting of&#xD;
      treatment probability. Additionally, and because it is foreseeable that there will be&#xD;
      multiple treatment groups, the specific estimation procedure called generalized boosted&#xD;
      models will be applied.&#xD;
&#xD;
      For the analysis of cost data, both for the analysis of associated variables and for a cost&#xD;
      comparison objective, GLM regression techniques will be used with the type of distribution&#xD;
      that best fits the data (using the Modified Park Test ), although preferably the gamma and&#xD;
      logarithm family will be used as the link. The data will be analyzed with the Stata v14.2&#xD;
      program.&#xD;
&#xD;
      ETHICAL AND CONFIDENTIALITY ASPECTS. The project has been evaluated by the research&#xD;
      commissions and the Research Ethics Committee with Medicines (CEIm), where it has been&#xD;
      approved. The laws on personal data will be followed (RGPD 2018) All information will be&#xD;
      treated in an absolutely confidential manner.&#xD;
&#xD;
      Expected results: A prognostic stratification tool based on predictive models of poor&#xD;
      evolution in CoVid-19 infection: clinical deterioration and development of ARDS, SRS, sepsis,&#xD;
      and/or septic shock and/or death. This tool will help guide the most appropriate clinical&#xD;
      management of patients, mainly those with the most severe presentations that may require&#xD;
      attention in critical care units. Additionally, purposes of this study are alsoto provide&#xD;
      information on the variability and costs in the provision of health care that may have been&#xD;
      given, both in the use of diagnostic tests and in the use of different therapeutic options&#xD;
      and also in the results finally obtained. The investigators seek to identify problems in the&#xD;
      accessibility of different groups (elderly, people in residences, by gender, higher level of&#xD;
      comorbidities, immigrants ...), and that can help us identify problems in equity in access to&#xD;
      health services.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">June 1, 2020</start_date>
  <completion_date type="Anticipated">December 31, 2021</completion_date>
  <primary_completion_date type="Anticipated">December 30, 2021</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Clinical deterioration</measure>
    <time_frame>Admission</time_frame>
    <description>Clinical deterioration: Resting dyspnoea (Breathing rate &gt; 30 breaths/minute) or 93% oxygen saturation at rest and partial pressure of arterial oxygen ; (PaO2) /Inspired fraction of O2 &lt;300 mm Hg&#xD;
Development of ARDS, sepsis, SIRS, shock&#xD;
entry in ICU(date and days of stay)&#xD;
Decease (date)</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Relief of symptoms</measure>
    <time_frame>Admission</time_frame>
    <description>Relief of symptoms (days), days until absence of disease (negative test), .</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Mortality</measure>
    <time_frame>6 months</time_frame>
    <description>Mortality</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Complications at follow up</measure>
    <time_frame>6 months</time_frame>
    <description>readmissions, clinical complications</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Cost</measure>
    <time_frame>Admission</time_frame>
    <description>Economic cost</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">20000</enrollment>
  <condition>Covid19</condition>
  <arm_group>
    <arm_group_label>COVID19 REDISSEC</arm_group_label>
    <description>Patients admitted (confirmed cases) by CoVid-19, excluding paediatric population. No losses are expected. A case of SARS-CoV-2 infection is defined as one that meets the laboratory criteria: PCR positive for a specific gene [RdRp or S gene] or PCR positive for at least 2 genes used for screening [E or N gene].</description>
  </arm_group>
  <arm_group>
    <arm_group_label>COVID19 Basque Country</arm_group_label>
    <description>All people from thw Basque Country positive to CoVid-19. A case of SARS-CoV-2 infection is defined as one that meets the laboratory criteria: PCR positive for a specific gene [RdRp or S gene] or PCR positive for at least 2 genes used for screening [E or N gene], or, as well and in the general population of the Basque Country, by detection of COVID-19 IgM or IgG antibodies.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Predictors adverse evolution</intervention_name>
    <description>Predictors adverse evolution in all hospital participant admitted patients</description>
    <arm_group_label>COVID19 REDISSEC</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Predictors of health care provide</intervention_name>
    <description>Predictors of death, unequity, variability in process of care, cost in all COVID positive patients form the Basque Country</description>
    <arm_group_label>COVID19 Basque Country</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        -  Positive COVID19 people in the Basque Country&#xD;
&#xD;
          -  Patients admitted by Covid-19 to hospitals in the Basque Country, from 01/02/2020 to&#xD;
             01/04/2020&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Positive COVID19 people in the Basque country&#xD;
&#xD;
          -  Patients admitted (confirmed cases) by CoVid-19&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Pediatric population (for objective #1 only)&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Susana Garcia-Gutierrez, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Hospital Galdakao-Usansolo</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Susana Garcia-Gutierrez, PhD</last_name>
    <phone>+35 944007105</phone>
    <email>SUSANA.GARCIAGUTIERREZ@osakidetza.eus</email>
  </overall_contact>
  <location>
    <facility>
      <name>Hospital Galdakao-Usansolo</name>
      <address>
        <city>Galdakao</city>
        <state>Bizkaia</state>
        <zip>48960</zip>
        <country>Spain</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Susana García-Gutierrez, PhD</last_name>
      <phone>944007105</phone>
      <email>susana.garciagutierrez@osakidetza.eus</email>
    </contact>
  </location>
  <location>
    <facility>
      <name>Hospital Galdakao-Usansolo</name>
      <address>
        <city>Galdakao</city>
        <state>Bizkaia</state>
        <zip>48960</zip>
        <country>Spain</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Susana García-Gutierrez, MD, PhD</last_name>
      <phone>944007105</phone>
      <email>susana.garciagutierrez@osakidetza.eus</email>
    </contact>
    <investigator>
      <last_name>Maximino Redondo, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Xavier Castells, MD, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Lilisbeth Perestelo, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Adolfo Muñoz, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Naiara Parraza, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Milagros Iriberri, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Iñigo Gorostiza, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Oier Ateka, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>Spain</country>
  </location_countries>
  <verification_date>September 2021</verification_date>
  <study_first_submitted>July 6, 2020</study_first_submitted>
  <study_first_submitted_qc>July 8, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 9, 2020</study_first_posted>
  <last_update_submitted>September 28, 2021</last_update_submitted>
  <last_update_submitted_qc>September 28, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">September 29, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Hospital Galdakao-Usansolo</investigator_affiliation>
    <investigator_full_name>Susana García Gutiérrez</investigator_full_name>
    <investigator_title>PhD</investigator_title>
  </responsible_party>
  <keyword>COVID</keyword>
  <keyword>Risk stratification</keyword>
  <keyword>Health services research</keyword>
  <keyword>Cost-effectiveness</keyword>
  <keyword>Equity</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04463706
org study id: COVID19_0459
nct id: NCT04463706
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
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: 1.-All people COVID19 positive from the Basque Country (around
      18768 people); 2.-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: 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: 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.

      Result: Clinical impairment: Dyspnoea at rest, Development of ARDS, sepsis, SIRS, shock, ICU
      admission, Death (date). Relief of symptoms, days until the absence of disease, death.

      Follow-up (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: 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 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 alsoto 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.
observational model: Cohort
time perspective: Retrospective
measure: Clinical deterioration
time frame: Admission
description: Clinical deterioration: Resting dyspnoea (Breathing rate > 30 breaths/minute) or 93% oxygen saturation at rest and partial pressure of arterial oxygen ; (PaO2) /Inspired fraction of O2 <300 mm Hg
Development of ARDS, sepsis, SIRS, shock
entry in ICU(date and days of stay)
Decease (date)
measure: Relief of symptoms
time frame: Admission
description: Relief of symptoms (days), days until absence of disease (negative test), .
measure: Mortality
time frame: 6 months
description: Mortality
measure: Complications at follow up
time frame: 6 months
description: readmissions, clinical complications
measure: Cost
time frame: Admission
description: Economic cost
arm group label: COVID19 REDISSEC
description: Patients admitted (confirmed cases) by CoVid-19, excluding paediatric population. No losses are expected. A case of SARS-CoV-2 infection is defined as one that meets the laboratory criteria: PCR positive for a specific gene [RdRp or S gene] or PCR positive for at least 2 genes used for screening [E or N gene].
arm group label: COVID19 Basque Country
description: All people from thw Basque Country positive to CoVid-19. A case of SARS-CoV-2 infection is defined as one that meets the laboratory criteria: PCR positive for a specific gene [RdRp or S gene] or PCR positive for at least 2 genes used for screening [E or N gene], or, as well and in the general population of the Basque Country, by detection of COVID-19 IgM or IgG antibodies.
intervention type: Other
intervention name: Predictors adverse evolution
description: Predictors adverse evolution in all hospital participant admitted patients
arm group label: COVID19 REDISSEC
intervention type: Other
intervention name: Predictors of health care provide
description: Predictors of death, unequity, variability in process of care, cost in all COVID positive patients form the Basque Country
arm group label: COVID19 Basque Country
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Susana Garcia-Gutierrez, PhD
role: Principal Investigator
affiliation: Hospital Galdakao-Usansolo
last name: Susana Garcia-Gutierrez, PhD
phone: +35 944007105
email: SUSANA.GARCIAGUTIERREZ@osakidetza.eus
facility: 
status: Recruiting
contact: 
facility: 
status: Recruiting
contact: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
country: Spain
responsible party type: Principal Investigator
investigator affiliation: Hospital Galdakao-Usansolo
investigator full name: Susana García Gutiérrez
investigator title: PhD
mesh term: COVID-19
sharing ipd: Undecided