Ignite Creation Date:
2024-05-06 @ 3:34 PM
Last Modification Date:
2024-10-26 @ 1:51 PM
Study NCT ID:
NCT04670094
Status:
COMPLETED
Last Update Posted:
2022-11-09
First Post:
2020-12-14
Brief Title:
Comorbidities and Risk Score in COVID-19 Patients
Sponsor:
University of Milano Bicocca
Organization:
University of Milano Bicocca
Study Overview
Official Title:
Comorbidities and Risk Score for Severity and Outcome in Patients With Infection by SARS-CoV-2
Status:
COMPLETED
Status Verified Date:
2022-11
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:
Comorbidities
Brief Summary:
Retrospective multi-center cohort study Consecutive patients hospitalized for severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 up to October 2020 will be included Patients are followed until discharge from hospital or death
Detailed Description:
Background
A virus causing clusters of severe pneumonia was first detected in the city of Wuhan China in December 2019
This pathogen was designated as SARS-CoV-2 Although of probable zoonotic origin human-to-human transmission has rapidly fuelled the spread of SARS-CoV-2 infection globally
On February 20th the first case of locally acquired SARS-CoV-2 infection was diagnosed in Northern Italy in a critically ill hospitalized young man with no travel history to known areas of viral circulation or link to a probable or confirmed coronavirus infectious disease 2019 COVID-19 case Prior to this date only three cases of COVID-19 had been reported in Central Italy all with a travel history to Wuhan Following this unexpected finding case counts and death tolls has increased rapidly in the country with a total of 192994 confirmed cases and 25969 deaths as of 24 April 2020
Study rationale
Multiple variables have been described as possible risk factors for SARS-CoV-2 susceptibility severity and prognosis among which age sex and comorbidities play an important role
Centers for Disease Control and Prevention CDC listed the underlying medical conditions that have shown to increase the risk of severe illness from SARS-CoV-2 While some comorbidities such as serious heart conditions and chronic kidney disease have a consistent and strong evidence as bad prognostic factors in SARS-CoV-2 infection others as HIV have a limited evidence and heterogeneous results
Further despite it is well-known that the burden of co-existing diseases may be additive or even multiplicative the effect of specific disease cluster on COVID-19 adverse outcomes has never been evaluated Finally the proposed models and risk scores currently available to predict disease severity and mortality are poorly reported and at high risk of bias raising concern that their predictions could be unreliable when applied in daily practice A reliable riskprognostic score developed by a multidimensional and cross-validated approach will pave the way for future research on frail sectors of the population and on the use of health system resources At the clinical level a prognostic score will allow to predict severity and mortality risk in patients requiring hospitalization and to stratify patients according to clinical severity helping clinicians in their therapeutic decision-making
Objectives
The primary objective of the study is to evaluate the role of patients comorbidities on clinical outcome in patients hospitalized for SARS-CoV-2 The investigators will confirm risk predictors already known and provide evidence for the uncertain ones The investigators will also develop a prognostic score able to predict negative clinical outcomes primarily short-term mortality that will be useful to stratify patients at hospital admission according to their different risk profiles and therefore to tailor the individuals level of care
A secondary objective could be that to extend this approach at the susceptible population level especially the elders to stratify according to the higher risk of being infected by SARS-CoV-2 hospitalized and to have a dismal outcome not developed here but related to a possible amendment
Sample size
The investigators expect the total number of patients with complete data to be approximately 2500 based on the expected recruitment of each center
Analysis Plan
Data will be summarized by counts and percentage and quartiles for categorical and continuous variables respectively Multi-state models will be used to describe patients hospital mortality and discharge In-hospital mortality will be estimate accounting for discharge as competing event Kaplan-Meier estimator will be used to estimate mortality up to 3-months from admission The role of patients comorbidities on clinical outcome will be evaluated by the Cox model adjusting for relevant confounders A clinically-based prognostic score will be developed including comorbidities and other risk factors The score will be constructed by a multidimensional approach and Lasso approach will be used to select relevant risk factors The Area Under the Receiving Operating Characteristics curve AUC and Brier score will be used to evaluate model performance and the final score will be cross-validated A sensitivity analysis will be performed using a training and test validation approach The use of regression trees for a practical definition of risk subgroups and latent variable models will also be considered
Multiple imputation will be performed if missing would exceed 10
Data collection
Consecutive patients hospitalized for SARS-CoV-2 up to October 2020 will be included Given the difficulty in systematically obtaining written informed consent and given the great public interest of the project the research will be conducted in the context of the authorizations guaranteed by Article 89 of the General Data Protection Regulation GDPR EU Regulation 2016679 which guarantees processing for purposes of public interest for scientific or historical research or for statistical purposes of health data
A virus causing clusters of severe pneumonia was first detected in the city of Wuhan China in December 2019
This pathogen was designated as SARS-CoV-2 Although of probable zoonotic origin human-to-human transmission has rapidly fuelled the spread of SARS-CoV-2 infection globally
On February 20th the first case of locally acquired SARS-CoV-2 infection was diagnosed in Northern Italy in a critically ill hospitalized young man with no travel history to known areas of viral circulation or link to a probable or confirmed coronavirus infectious disease 2019 COVID-19 case Prior to this date only three cases of COVID-19 had been reported in Central Italy all with a travel history to Wuhan Following this unexpected finding case counts and death tolls has increased rapidly in the country with a total of 192994 confirmed cases and 25969 deaths as of 24 April 2020
Study rationale
Multiple variables have been described as possible risk factors for SARS-CoV-2 susceptibility severity and prognosis among which age sex and comorbidities play an important role
Centers for Disease Control and Prevention CDC listed the underlying medical conditions that have shown to increase the risk of severe illness from SARS-CoV-2 While some comorbidities such as serious heart conditions and chronic kidney disease have a consistent and strong evidence as bad prognostic factors in SARS-CoV-2 infection others as HIV have a limited evidence and heterogeneous results
Further despite it is well-known that the burden of co-existing diseases may be additive or even multiplicative the effect of specific disease cluster on COVID-19 adverse outcomes has never been evaluated Finally the proposed models and risk scores currently available to predict disease severity and mortality are poorly reported and at high risk of bias raising concern that their predictions could be unreliable when applied in daily practice A reliable riskprognostic score developed by a multidimensional and cross-validated approach will pave the way for future research on frail sectors of the population and on the use of health system resources At the clinical level a prognostic score will allow to predict severity and mortality risk in patients requiring hospitalization and to stratify patients according to clinical severity helping clinicians in their therapeutic decision-making
Objectives
The primary objective of the study is to evaluate the role of patients comorbidities on clinical outcome in patients hospitalized for SARS-CoV-2 The investigators will confirm risk predictors already known and provide evidence for the uncertain ones The investigators will also develop a prognostic score able to predict negative clinical outcomes primarily short-term mortality that will be useful to stratify patients at hospital admission according to their different risk profiles and therefore to tailor the individuals level of care
A secondary objective could be that to extend this approach at the susceptible population level especially the elders to stratify according to the higher risk of being infected by SARS-CoV-2 hospitalized and to have a dismal outcome not developed here but related to a possible amendment
Sample size
The investigators expect the total number of patients with complete data to be approximately 2500 based on the expected recruitment of each center
Analysis Plan
Data will be summarized by counts and percentage and quartiles for categorical and continuous variables respectively Multi-state models will be used to describe patients hospital mortality and discharge In-hospital mortality will be estimate accounting for discharge as competing event Kaplan-Meier estimator will be used to estimate mortality up to 3-months from admission The role of patients comorbidities on clinical outcome will be evaluated by the Cox model adjusting for relevant confounders A clinically-based prognostic score will be developed including comorbidities and other risk factors The score will be constructed by a multidimensional approach and Lasso approach will be used to select relevant risk factors The Area Under the Receiving Operating Characteristics curve AUC and Brier score will be used to evaluate model performance and the final score will be cross-validated A sensitivity analysis will be performed using a training and test validation approach The use of regression trees for a practical definition of risk subgroups and latent variable models will also be considered
Multiple imputation will be performed if missing would exceed 10
Data collection
Consecutive patients hospitalized for SARS-CoV-2 up to October 2020 will be included Given the difficulty in systematically obtaining written informed consent and given the great public interest of the project the research will be conducted in the context of the authorizations guaranteed by Article 89 of the General Data Protection Regulation GDPR EU Regulation 2016679 which guarantees processing for purposes of public interest for scientific or historical research or for statistical purposes of health data
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