Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 5:08 AM
Ignite Modification Date:
2025-12-25 @ 5:08 AM
NCT ID:
NCT04782427
Brief Summary:
The medical charts of all COVID-19 cases (n=1200) from 17 long-term care facilities in Montreal, Canada will be reviewed, to compare patients who survived to patients who did not survive. Through multilevel logistic regression, the risk of death will be estimated for institutional predictors of mortality, while controlling for individual risk factors.
Individual covariates include clinical features (age, sex, Charlston comorbidity index, SMAF autonomy score, severity criteria) and medical treatments (IV fluids, anticoagulation, oxygen, regular opiates, corticosteroids). Aggregate covariates include epidemiological data (attack rates, timing of outbreak) and institutional characteristics (number of beds, air exchange per hour, presence of a dedicated COVID-19 unit at the time of outbreak, staff compliance to infection control measures, staff infection rates, understaffing, proportion of semi-private rooms, proportion of wandering wards and other special units).
Detailed Description:
A lot has been written about individual risk factors for COVID-19 death, mostly in the hospitalized population. However, even though most deaths around the world have occurred among the frail and elderly, little is known about the risk factors specific to the long-term care population.
In this retrospective cohort study, the investigators will review the medical charts of all COVID-19 cases (n=1200) from 17 long-term care facilities in Montreal, Canada, to compare patients who survived to patients who did not survive. Through multilevel logistic regression, the risk of death will be estimated for institutional predictors of mortality, while controlling for individual risk factors. The objective is to influence local and national policies in long-term care facilities, in the hopes of avoiding the tragic spring 2020 outcomes during subsequent waves of COVID-19 or future pandemics.
Covariates in the models will be drawn from a review of the medical literature and known risk factors for COVID-19 death. Individual-level covariates include clinical features (age, sex, Charlston comorbidity index, SMAF autonomy score, severity criteria) as well as medical treatments (IV fluids, anticoagulation, oxygen, regular opiates, corticosteroids). Aggregate-level covariates include epidemiological data (attack rates, timing of outbreak) and institutional characteristics (number of beds, air exchange per hour, presence of a dedicated COVID-19 unit at the time of outbreak, staff compliance to infection control measures, staff infection rates, understaffing, proportion of semi-private rooms, proportion of wandering wards and other special units).
Study:
NCT04782427
Study Brief:
Protocol Section:
NCT04782427