Is Precariousness a Risk Factor for COVID-19 Mortality in Intensive Care?

Sponsor

Centre Hospitalier de Saint-Denis (Other)

Overall Status

Completed

CT.gov ID

NCT04896944

Collaborator

(none)

200

Enrollment

Location

13.6

Actual Duration (Months)

14.7

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Is precariousness a risk factor for COVID-19 mortality in intensive care units ?

Abstract

Background: During the SARS-CoV-2 pandemic, the first wave overwhelmed hospitals in Paris area (Ile-de-France) with a variable impact depending on the territory. Several studies highlighted variable ICU mortality rates during COVID-19 surges across territories (10 to 60%) with higher rates in those most affected by poverty. We assessed the impact of precariousness, as an independent risk factor, on mortality linked to Covid-19 between ICUs at Delafontaine hospital and Ambroise Paré hospital.

Method: Investigators carry out a retrospective observational cohort study of consecutive ICU patients aged ≥ 18 years admitted at Delafontaine and Ambroise Paré hospitals during the first wave of the Covid-19 outbreak in order to compare mortality rates according to predefined risk factors (age, diabetes, arterial hypertension, BMI, active solid or haematological cancer, IGS2, poverty rate at the threshold of 60% (%) according to the island grouped for statistical information (IRIS)37 of the patient, invasive ventilation or not) that include precariousness.

Results:

Conclusion:

Condition or Disease	Intervention/Treatment	Phase
Precariousness Critically Ill Covid19	Other: No intervention

Detailed Description

Introduction:

During the SARS-CoV-2 pandemic, the first health crisis overwhelmed hospitals in Ile de France with a different impact depending on the territory . According to data on deaths registered in the civil registry published by the National Institute of Statistics and Economic Studies (INSEE), Seine-Saint-Denis has recorded the highest excess mortality rate in Ile-de-France, over the period from 1st March to 19th April. Seine-Saint-Denis has an excess mortality of over 130%, compared to over 74% in Paris and over 122% in Hauts-de-Seine. By considering the mortality rates according to the place of residence rather than the place of death, excess mortality has reached 134% in Seine-Saint-Denis, over 114% in Hauts-de-Seine, and over 99 % regarding Paris.

Seine-Saint-Denis is one of the French department most affected by poverty, which is a factor of vulnerability, and constitutes a risk factor for mortality after hospitalization in the context of traumatic, cardiovascular or cancer pathologies. In addition, precarious people have a reduced life expectancy, but it is not a risk factor for ICU mortality when considering severity on admission.

In Ile-de-France, the mortality rate in intensive care could significantly vary depending on the territory (10 to 60%), reflecting thus the disparities noticed in mortality from Covid-19. The opensafely study enumerates some of the primary mortality risk factors from Covid-19 such as age, sex, obesity, smoking, ethnicity, diabetes, solid and hematological cancers, kidney failure, chronic cardiorespiratory diseases but also precariousness independently and with a dose-response effect, whereas these risk factors could result from precariousness itself.

Knowing these risk factors for severe infection with Covid-19, investigators assume a link between precariousness and mortality in intensive care with Covid-19 pneumonia.

To validate this hypothesis, investigators suggest to study two intensive care populations (Ambroise Paré Boulogne Hospital in the Hauts-de-Seine and Delafontaine Hospital in Seine-Saint-Denis) from contrasted territories regarding the socio-economic context in Ile de France. The socio-demographic characteristics of Seine-Saint-Denis may be one of the reason to explain this particularity. It is an area densely populated (6,802 inhabitants per km2), just like Hauts-de-Seine (9,164 inhabitants / km2) with households often living in over-occupied dwellings (21% against 12,8% in Hauts-de-Seine and 5% in France excluding Mayotte). The socio-professional category of workers is more represented than in the other departments of Ile-de-France and the population therefore does not necessarily have a job suitable for teleworking. Hence the fact that precariousness could promote the circulation of the virus.

Materials and method:

Definition: the definition of precariousness is complex, multifactorial and non-consensual. Precariousness is describe as "a state of social instability characterized by the absence of one or more securities, in particular that of employment, allowing individuals and families to assume their professional, family and social obligations, and to enjoy their fundamental rights ".

In order to establish a link between precariousness and mortality in intensive care from Covid-19, investigators choose to determine the precariousness of each patient according to the economic data of the National Institute of Statistics and Economic Studies. It would allow to obtain a poverty rate (percentage of living below 60% of median income) according to the place of residence of each one, which we will divide into quintiles.

Hypothesis: there is a difference in mortality between resuscitation services at Delafontaine hospital and Ambroise Paré hospital with the underlying idea that precariousness is an independent risk factor for mortality linked to Covid-19.

Experimental plan and objectives: investigators will carry out a retrospective observational cohort study on analysis of the files of patients hospitalized in intensive care at Delafontaine and Ambroise Paré hospitals, aiming to compare their mortality according to predefined risk factors during the first wave of the epidemic at Covid-19 (admission dates between March 13 and May 11, 2020).

Inclusion criteria: all patients hospitalized in intensive care at Delafontaine hospital (12 intensive care beds and 6 CCU beds) and Ambroise Paré hospital (12 intensive care beds and 6 CCU beds) that has developed a Covid-19 pneumonitis confirmed biologically by nasopharyngeal PCR or on deep respiratory samples (bronchial, tracheal aspiration or bronchoalveolar lavage) or strongly suspected with a compatible CT27 and a very evocative clinical history depending on the practitioner in charge would be included into the study.

Exclusion criteria: all minor patients under the age of 18 and patients transferred after less than 24 hours of care in the service would be excluded.

Study locations: this study concerns the Ambroise Paré hospital in Boulogne and the Delafontaine hospital in Saint-Denis, whose sectors are different, specifically regarding precariousness of their surrounding populations.

Data to be collected:

Data studied: age, diabetes, arterial hypertension, BMI, unhealed solid or haematological cancer, IGS2, poverty rate at the threshold of 60% (%) according to the island grouped for statistical information (IRIS) of the patient, invasive ventilation or not, date of start of invasive ventilation, date of end of ventilation, NIV or high flow oxygen therapy at initial treatment, prone position, curarization, placement of ECMO, introduction of corticosteroid therapy within 7 first days of hospitalization, date of entry (defined as D0), date of discharge from intensive care (death or conventional discharge), date of discharge from hospital, death and date of death if it occurred in hospital.
Patients transferred to another intensive care unit after the first 24 hours for specific treatment or discharge from the service will be included and their data recovered by the recovery of hospitalization reports
Patients discharged from the hospital for a rehabilitation center or another long stay are considered alive for the study and will not have follow-up after resuscitation
Data to compare the workload related to Covid on the two hospitals will be collected in order to discuss the results obtained (see table below).
Average number of Covid patients present each day in each hospital during the period (Average patients / days)
Number of usual conventional hospital beds (permanently open which can admit patient urgently and / or whose length of stay is not under control)
Number of resuscitation beds and usual CCUs
Average number of Covid patients present each day in each intensive care unit
Total number of patients transferred to an intensive care unit in another hospital over the period within the first 24 hours of care by the intensive care team Primary endpoint: mortality in intensive care at 90 days.

Secondary judgment criteria: mortality at 90 days in hospital, length of hospital stay in intensive care unit, length of hospital stay in hospital.

Statistics:

univariate comparison of risk factors in the 2 groups (Chi2 / Student)
multivariate analysis on variables whose frequency varies in the 2 groups (logistic regression test)

Discussion:

Description of observed outcome, discussed after having identified side effect resulting from the differences between intensive care units of two hospitals, which could be a compounding factors during patient care.

Main compounding factors would be the workload on the hospital and the specific initial treatments in the lack of data from the literature at this stage of the pandemic.

Study Design

Study Type:

Observational

Actual Enrollment :

200 participants

Observational Model: