3 research outputs found

    Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse

    Get PDF

    A comparison of the effectiveness of different doses of tocilizumab and sarilumab in the treatment of severe COVID-19: a natural experiment due to drug shortages

    No full text
    Objectives: Interleukin (IL)-6 inhibitors are administered to treat patients hospitalized with COVID-19. In 2021, due to shortages, different dosing regimens of tocilizumab, and a switch to sarilumab, were consecutively implemented. Using real-world data, we compare the effectiveness of these IL-6 inhibitors. Methods: Hospitalized patients with COVID-19, treated with IL-6 inhibitors, were included in this natural experiment study. Sixty-day survival, hospital- and intensive care unit (ICU) length of stay, and progression to ICU or death were compared between 8 mg/kg tocilizumab, fixed-dose tocilizumab, low-dose tocilizumab, and fixed-dose sarilumab treatment groups. Results: A total of 5485 patients from 49 hospitals were included. After correction for confounding, increased hazard ratios (HRs) for 60-day mortality were observed for fixed-dose tocilizumab (HR 1.20, 95% confidence interval [CI] 1.04-1.39), low-dose tocilizumab (HR 1.12, 95% CI 0.97-1.31), and sarilumab (HR 1.24, 95% CI 1.08-1.42), all relative to 8 mg/kg. The 8 mg/kg dosing regimen had lower odds of progression to ICU or death. Both hospital- and ICU length of stay were shorter for low-dose tocilizumab than for the 8 mg/kg group. Conclusion: We found differences in the probability of 60-day survival and the incidence of the combined outcome of mortality or ICU admission, mostly favoring 8 mg/kg tocilizumab. Because of potential time-associated residual confounding, further clinical studies are warranted

    Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

    No full text
    Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes
    corecore