The role of organizational characteristics on the outcome of COVID-19 patients admitted to the ICU in Belgium

Abstract

Background: Several studies have investigated the predictors of in-hospital mortality for COVID-19 patients who need to be admitted to the Intensive Care Unit (ICU). However, no data on the role of organizational issues on patients' outcome are available in this setting. The aim of this study was therefore to assess the role of surge capacity organisation on the outcome of critically ill COVID-19 patients admitted to ICUs in Belgium.

Methods: We conducted a retrospective analysis of in-hospital mortality in Belgian ICU COVID-19 patients via the national surveillance database. Non-survivors at hospital discharge were compared to survivors using multivariable mixed effects logistic regression analysis. Specific analyses including only patients with invasive ventilation were performed. To assess surge capacity, data were merged with administrative information on the type of hospital, the baseline number of recognized ICU beds, the number of supplementary beds specifically created for COVID-19 ICU care and the "ICU overflow" (i.e. a time-varying ratio between the number of occupied ICU beds by confirmed and suspected COVID-19 patients divided by the number of recognized ICU beds reserved for COVID-19 patients; ICU overflow was present when this ratio is ≥ 1.0).

Findings: Over a total of 13,612 hospitalised COVID-19 patients with admission and discharge forms registered in the surveillance period (March, 1 to August, 9 2020), 1903 (14.0%) required ICU admission, of whom 1747 had available outcome data. Non-survivors (n = 632, 36.1%) were older and had more frequently various comorbid diseases than survivors. In the multivariable analysis, ICU overflow, together with older age, presence of comorbidities, shorter delay between symptom onset and hospital admission, absence of hydroxychloroquine therapy and use of invasive mechanical ventilation and of ECMO, was independently associated with an increased in-hospital mortality. Similar results were found in in in the subgroup of invasively ventilated patients. In addition, the proportion of supplementary beds specifically created for COVID-19 ICU care to the previously existing total number of ICU beds was associated with increased in-hospital mortality among invasively ventilated patients. The model also indicated a significant between-hospital difference in in-hospital mortality, not explained by the available patients and hospital characteristics.

Interpretation: Surge capacity organisation as reflected by ICU overflow or the creation of COVID-19 specific supplementary ICU beds were found to negatively impact ICU patient outcomes.

Funding: No funding source was available for this study.

Keywords: COVID-19; Intensive care unit; Mortality; Organisation; Surge.

Fig. 1

Flow-chart of the study: COVID-19 hospital clinical surveillance, Belgium, March 1^st – August 9^th 2020.

Fig. 2

Multivariable mixed-effects model for predictors of in-hospital mortality (fixed effects) among COVID-19 patients admitted to ICU. COVID-19 hospital clinical surveillance, Belgium, March 1st – August 9th 2020. The following fixed effects were retained in the final model: age, gender, chronic immunosuppression, chronic renal disease, chronic pulmonary disease, arterial hypertension, days from symptoms to hospital admission, extra-corporeal membrane oxygenation, invasive mechanical ventilation, and overflow. Hospital was added as a random effect to the model. Odds ratio per 10 years of age is shown. Abbrevations: HCQ = hydroxychloroquine; ECMO = extra-corporeal membrane oxygenation; IMV = invasive mechanical ventilation; ImmS = chronic immunosuppression; CRenD = chronic renal disease; CPulmD = chronic pulmonary disease; HTN = arterial hypertension.

Fig. 3

Adjusted predicted values of mortality for overflow. COVID-19 hospital clinical surveillance, Belgium, March 1st – August 9th 2020. The marginal effect of overflow is based on a mixed effects model with a random effect for each hospital and fixed effects for age, gender, chronic immunosuppression, chronic renal disease, chronic pulmonary disease, arterial hypertension, days from symptoms to hospital admission, hydroxychloroquine, extra-corporeal membrane oxygenation, invasive mechanical ventilation, and overflow. Means are used to fix continuous variables and proportions are used to fix categorical variables.

Fig. 4

Between hospital variation in in-hospital mortality among COVID-19 patients admitted to ICU, based on a mixed effects model with a random effect for each hospital and fixed effects for age, gender, chronic immunosuppression, chronic renal disease, chronic pulmonary disease, arterial hypertension, days from symptoms to hospital admission, hydroxychloroquine, extra-corporeal membrane oxygenation, invasive mechanical ventilation, and overflow. COVID-19 hospital clinical surveillance, Belgium, March 1st – August 9th 2020. In red, hospitals with higher adjusted in-hospital mortality; in blue, hospitals with lower adjusted in-hospital mortality compared to the average over all hospitals (i.e. the global estimate for the intercept).

Fig. 5

Adjusted predicted values of mortality for the proportion of created ICU beds in ventilated patients. COVID-19 hospital clinical surveillance, Belgium, March 1st – August 9th 2020. The marginal effect of the proportion of created ICU beds is based on a mixed effects model with a random effect for each hospital and fixed effects for age, gender, chronic immunosuppression, chronic renal disease, chronic pulmonary disease, arterial hypertension, days from symptoms to hospital admission, hydroxychloroquine, macrolides, extra-corporeal membrane oxygenation, invasive mechanical ventilation, nursing home resident, and the proportion of created ICU beds. Means are used to fix continuous variables and proportions are used to fix categorical variables.