Changes in Cardiac and Pulmonary Hemodynamics as Predictor of Outcome in Hospitalized COVID-19 Patients (COVID-HO)

Hasselt University

Status

Unknown

Conditions

Covid-19

Treatments

Other: No interventions planned

Study type

Observational

Funder types

Other

Identifiers

NCT04371679

Covid-HO

Details and patient eligibility

About

The primary objective of the study is to evaluate cardiac and pulmonary hemodynamic changes over time as predictor of disease progression and outcome in COVID-19 patients admitted to ICU.

The primary endpoint is the occurrence of a major event predefined as either: death (all-cause mortality) or discharge from ICU (limit of 4 months).

This is a uni-center prospective observational cohort study with an inclusion period of 2 months. The end of the study is foreseen in 6 months.

Full description

Background COVID-19 can lead to a bilateral pneumonia overwhelming the lungs causing dyspnea and respiratory distress. Up to 20% of the infected population is hospitalized and 5% is submitted to the intensive care unit (ICU). Up to 31% of patients in ICU develop sepsis and 61% develop ARDS with a deadly outcome at ICU of 38%. While sepsis typically causes diffuse vasodilation, the pulmonary vasculature resistance in ARDS is high. Although heart failure is per definition not the cause of ARDS, the resulting elevated pressures in the pulmonary circulation affect right and left heart function. Early detection in alterations of cardiac and pulmonary hemodynamics might prompt to actions to prevent ARDS.

Primary objective To evaluate cardiac and pulmonary hemodynamic changes over time as predictor of disease progression and outcome in COVID-19 patients admitted to ICU.

Secondary objective

Analysis of prognostic factors based on the data at initial presentation
Performing a trajectory analysis of the time course during ICU stay to determine what leads to optimal outcome - gain insight in the pathophysiology of the cardio-pulmonary evolution of COVID-19 pts
Feasibility study for the creation of an individualized expected data-trajectory for new cases and continuously updating its visualization in relation to the expected trajectory related to an improved outcome
Evaluate how Machine Learning, based on manifold learning for quantifying information similarity and its temporal evolution, is able to predict outcome using rich data in a limited number of patients Primary Endpoint

Occurrence of a major event predefined as either:

Death (all-cause mortality)
Discharge from ICU (limit of 4 months) Secondary Endpoint
Decrease of left ventricular (LV) function defined by LV global longitudinal strain (GLS) > 5% (absolute value) and LV S' as compared to the initial evaluation
Evolution of LV diastolic function related to prognosis - Doppler Data and ML interpretation
Decrease of right ventricular (RV) function by RV GLS > 5% (absolute values) or decrease of RV S' to an absolute value <9.5 cm/s
Dynamic RV response to PEEP maneuver to differentiate intrinsic RV dysfunction from excessive PEEP.
Changes in pulmonary arterial compliance from RVOT-VTI and PASP Methods Uni-center cohort study (Prospective Observational) Duration of the study Duration of the inclusion period: 2 months Duration of participation for each patient: average 4 weeks until death or discharge from ICU Duration of data processing and reports: 4 months Total duration of the study: 6 months

Enrollment

40 estimated patients

Sex

All

Volunteers

No Healthy Volunteers

Inclusion criteria

Patient admitted to ICU that is COVID-19 positive based on rt-PCR
Ventilated or not ventilated
No restrictions on age
No restrictions on comorbidities or a diversity of underlying pathology (malignancies, COPD, ...)

Exclusion criteria

Patients that are not COVID-19 tested (rt-PCR) or where the diagnosis is pending.
Patients that refuse their participation in the study.
Patients under legal protection, or deprived of their liberty.
Patients that are so critically ill that a minimum of 1 follow-up is very unlikely to be realised