Clinical Characteristics and Prognostic Factors of Patients With COVID-19 (Coronavirus Disease 2019)

Savana Research

Status

Active, not recruiting

Conditions

COVID-19 Infection

Study type

Observational

Funder types

Other

NETWORK

Identifiers

NCT04569851

BigCoviData

Details and patient eligibility

About

This is a multicenter, non-interventional, retrospective study using data captured in the EHRs (Electronic Health Records) of the participating hospital sites to determine factors that predict disease prognosis and outcomes in COVID-19 patients, specifically: Hospitalization/Off-site monitoring, transfer to ICU and/or need for medical mechanical ventilation (both invasive and non- invasive), length of ICU stay, and outcome (cure/ hospital discharge, in-hospital death)

Full description

Data captured in the EHRs will be collected from all available departments, including inpatient hospital, outpatient hospital, emergency room, etc. for virtually all types of provided services in each participating site. The study period will be from January 1, 2020 to the most recent data available.

Primary objective To determine factors that predict disease prognosis and outcomes in COVID-19 patients, specifically: Hospitalization/Off-site monitoring, transfer to ICU and/or need for medical mechanical ventilation (both invasive and non- invasive), length of ICU stay, and outcome (cure/ hospital discharge, in-hospital death)
Secondary objectives
- To describe the demographic and clinical characteristics of COVID-19 patients
- To describe the patient management (treatment and procedures) in the target population
- To describe the outcomes of COVID-19 (discharge, hospitalization, transfer to ICU/ mechanical medical ventilation, in-hospital death) in relation to patients' clinical and demographic characteristics, and treatment received
- To determine whether the factors that predict COVID-19 prognosis and outcome also apply to other types of pneumonia.
Exploratory objectives One of the goals of this study is to configure the Big Data system to unravel any hidden variable/s (and their associations) that may offer novel clinical insights into COVID-19 management.