Ignite Creation Date:
2024-05-06 @ 2:55 PM
Last Modification Date:
2024-10-26 @ 1:39 PM
Study NCT ID:
NCT04463706
Status:
COMPLETED
Last Update Posted:
2024-02-28
First Post:
2020-07-06
Brief Title:
COVID19 Severity Prediction and Health Services Research Evaluation
Sponsor:
Hospital Galdakao-Usansolo
Organization:
Hospital Galdakao-Usansolo
Study Overview
Official Title:
Clinical Characterization of CoVid19 Infection Prognostic Stratification and Complications
Status:
COMPLETED
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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 origin3-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
Detailed Description:
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 regressionsurvival modelsartificial 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 005 and all analyzes will be performed using SAS v94 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 v142 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 andor septic shock andor 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
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 regressionsurvival modelsartificial 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 005 and all analyzes will be performed using SAS v94 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 v142 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 andor septic shock andor 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None