NORMO2: The effect of NOn-invasive Respiratory support on outcoMe and its risks in SARS-COV-2-related hypoxemic respiratory failure

Project description

COVID-19 kan lijden tot zuurstoftekort door longontsteking. Behandeling op de Intensive Care (IC) met kunstmatige beademing met een buisje in de luchtpijp (ofwel invasieve beademing) is vaak noodzakelijk. Deze belastende behandeling heeft veel nadelen voor de patiënt, maar ook voor de maatschappij, gezien het aantal beschikbare IC-bedden beperkt is. Voorkomen van kunstmatige beademing en IC opname is dus van groot belang.


Bepalen of het gebruik van minder complexe ademhalingsondersteuning, kunstmatige beademing met tube bij patiënten met COVID-19 kan voorkomen.


Met behulp van databases die tijdens de pandemie gegevens hebben verzameld van duizenden COVID-19 patiënten hopen de onderzoekers een duidelijk antwoord te kunnen geven op de volgende vragen:

  • Kunnen kunstmatige beademing en IC opname voorkomen worden met niet-invasieve ademhalingsondersteuning (NIAO)? Voorbeelden hiervan zijn hoge flow zuurstoftoediening en gezichtsmasker-beademing.
  • Is het mogelijk om vroegtijdig het succes van NIAO te voorspellen? Lang doorgaan met NIAO en daarmee kunstmatige beademing uitstellen kan schadelijk zijn.
  • Heeft NIAO een gunstig effect voor patiënten die niet meer naar de IC gaan?

Uitvoerende partijen

Uitvoerende partijen

Amsterdam UMC – locatie VUmc, Franciscus Gasthuis & Vlietland, Erasmus MC, Universitair Medisch Centrum Groningen, Radboud UMC, Reinier de Graaf Gasthuis, Amsterdam UMC – locatie AMC, Maasstad Ziekenhuis

Meer informatie

Hoofdaanvrager: Prof. dr. L. Heunks (Amsterdam UMC – locatie VUmc)
Projectleider en penvoerder: Dr. E.J. Wils (Franciscus Gasthuis & Vlietland


Summary of the application

RESEARCH QUESTION and HYPOTHESIS: COVID-19 can result in severe hypoxemic respiratory failure that ultimately may require invasive mechanical ventilation in the Intensive Care Unit (ICU). Although lifesaving, invasive mechanical ventilation is associated with high mortality, severe discomfort for patient, long-term sequelae, stress to loved-ones and high costs for society. During the ongoing pandemic high number of invasively ventilated covid-19 patients overwhelmed ICU capacity. Non-invasive respiratory support, such as high flow nasal oxygen (HFNO) or non-invasive ventilation (NIV) have the potential to reduce the risk for invasive mechanical ventilation and in selected cases ICU admission. However, data from different studies are conflicting and studies performed in COVID-19 patients are of limited quality. Furthermore, identification of early predictors of HFNO/NIV treatment failure may prevent unnecessary delay of initiation of invasive ventilation, which may be associated with adverse clinical outcome. The development and validation of a prediction model, that incorporates readily available clinically data may prove pivotal to fine-tune non-invasive respiratory support. The overall aim of the NORMO2 project is to investigate the role and risks of HFNO and NIV to improve outcome in hospitalized hypoxemic COVID-19 patients. STUDY DESIGN: Prospective and retrospective multicentre cohort studies. STUDY POPULATION/DATA SOURCES/OUTCOME MEASURES: Data from hospitalized COVID-19 patients from 4 ongoing large Dutch cohort studies (HFNO-COVID, Clinico, Pro-Act, COVIDpredict) will be included if they received non-invasive respiratory support with conventional oxygen, HFNO or NIV. These patients may be admitted to the ICU or hospital ward, with or without a treatment restriction (i.e., no ICU admission). The selected cohorts contain sufficient number of eligible patients and granular data as needed for the planned detailed analysis (see below). Data collection include demographics, disease severity markers, treatment characteristics and respiratory variables at different time-points during the in-hospital course. Outcome measures are endotracheal intubation (start of invasive mechanical ventilation), ICU admission, mortality (ICU, hospital, 90 days, 1 year) and lung mechanics. INTERVENTION: none DATA-ANALYSIS and SAMPLE SIZE: Research questions and data analysis are structured in 4 related work packages (WP): WP1: To determine if an approach that includes HFNO and/or NIV is superior to conventional oxygen therapy in reducing the risk of invasive mechanical ventilation (primary endpoint), ICU admission, ICU length of stay, and mortality (secondary endpoints). WP2: To develop and validate a novel model to predict HFNO/NIV failure (endotracheal intubation and/or death) in hospitalized hypoxemic patients with COVID-19. WP3: To determine whether early endotracheal intubation compared to late endotracheal intubation affects clinical outcome (duration of invasive mechanical ventilation, respiratory mechanics, mortality). WP4: To determine if HFNO and/or NIV as compared to conventional oxygen therapy only, improves survival in COVID-19 patients with treatment restrictions (i.e., no ICU admission). For data-analysis of WP1, WP3 and WP4 we compare the outcomes in two groups (HFNO or NIV vs. conventional oxygen therapy; early vs. late intubation) using either multivariable regression models or propensity score matching to correct for confounding, as deemed appropriate based on data characteristics. We will adhere to the rule-of-thumb of 10 events per variable for model building. For model building we will use shrinkage methods, discrimination and calibration evaluation, and internal and external validation (i.e., WP2). An elaborate missing data strategy including the option of sensitivity analyses will be applied and we will account for hierarchical structure as needed for analysis of data obtained from multiple centres and cohorts. For WP1, we required a minimum of 200 patients per group to detect a difference in outcome risk of 10% resulting in a minimum power of 80%. For other analyses, we will adhere to the rule-of-thumb of 10 events per variable and anticipate that with our expected sample size a minimum of 10 predictors can be included in model. This project relies on a fruitful collaboration between patients, clinicians, scientists, data experts, statisticians, and experts in implementation. This team is well equipped and highly motivated to address these important research questions, and as such improve outcome of future patients and optimize the use of health care resources.



Project number:
Duration: 100%
Duration: 100 %
Part of program:
Project lead and secretary:
dr. E.J. Wils
Responsible organisation:
Erasmus Medisch Centrum