Melioidosis in travelers: An analysis of Dutch melioidosis registry data 1985–2018

Background: Melioidosis, caused by the Gram-negative bacterium Burkholderia pseudomallei, is an opportunistic infection across the tropics. Here, we provide a systematic overview of imported human cases in a non-endemic country over a 25-year period. Methods: All 5

Richtlijn preventie, diagnose en behandeling van tuberculose bij patiënten met een hiv-infectie

An interdisciplinary workgroup from the National Committee for Practical Tuberculosis Control in the Netherlands has written an evidence-based practice guideline on the prevention, diagnosis, and treatment of HIV-infected patients with active tuberculosis or latent tuberculosis infection. The diagnosis and treatment of tuberculosis are effectively the same in patients with or without an HIV infection. The diagnosis is more complex in a patient with an HIV infection due to the effect of the immunodeficiency on diagnostic parameters. Concomitant treatment of tuberculosis and HIV is complicated by drug interactions and overlapping adverse effects. In patients with tuberculosis and an HIV infection, the tuberculosis is preferably treated before antiretroviral therapy is started. The nurse or nurse practitioner in the organisation where the tuberculosis is diagnosed is responsible for supporting the HIV patient with tuberculosi

Effects of Decontamination of the Oropharynx and Intestinal Tract on Antibiotic Resistance in ICUs A Randomized Clinical Trial

IMPORTANCE Selective decontamination of the digestive tract (SDD) and selective oropharyngeal decontamination (SOD) are prophylactic antibiotic regimens used in intensive care units (ICUs) and associated with improved patient outcome. Controversy exists regarding the relative effects of both measures on patient outcome and antibiotic resistance. OBJECTIVE To compare the effects of SDD and SOD, applied as unit-wide interventions, on antibiotic resistance and patient outcome. DESIGN, SETTING, AND PARTICIPANTS Pragmatic, cluster randomized crossover trial comparing 12 months of SOD with 12 months of SDD in 16 Dutch ICUs between August 1, 2009, and February 1, 2013. Patients with an expected length of ICU stay longer than 48 hours were eligible to receive the regimens, and 5881 and 6116 patients were included in the clinical outcome analysis for SOD and SDD, respectively. INTERVENTIONS Intensive care units were randomized to administer either SDD or SOD. MAIN OUTCOMES AND MEASURES Unit-wide prevalence of antibiotic-resistant gram-negative bacteria. Secondary outcomes were day-28 mortality, ICU-acquired bacteremia, and length of ICU stay. RESULTS In point-prevalence surveys, prevalences of antibiotic-resistant gram-negative bacteria in perianal swabs were significantly lower during SDD compared with SOD; for aminoglycoside resistance, average prevalence was 5.6%(95% CI, 4.6%-6.7%) during SDD and 11.8%(95% CI, 10.3%-13.2%) during SOD (P <.001). During both interventions the prevalence of rectal carriage of aminoglycoside-resistant gram-negative bacteria increased 7% per month (95% CI, 1%-13%) during SDD (P = .02) and 4% per month (95% CI, 0%-8%) during SOD (P = .046; P = .40 for difference). Day 28-mortality was 25.4% and 24.1% during SOD and SDD, respectively (adjusted odds ratio, 0.96 [95% CI, 0.88-1.06]; P = .42), and there were no statistically significant differences in other outcome parameters or between surgical and nonsurgical patients. Intensive care unit-acquired bacteremia occurred in 5.9% and 4.6% of the patients during SOD and SDD, respectively (odds ratio, 0.77 [95% CI, 0.65-0.91]; P = .002; number needed to treat, 77). CONCLUSIONS AND RELEVANCE Unit-wide application of SDD and SOD was associated with low levels of antibiotic resistance and no differences in day-28 mortality. Compared with SOD, SDD was associated with lower rectal carriage of antibiotic-resistant gram-negative bacteria and ICU-acquired bacteremia but a more pronounced gradual increase in aminoglycoside-resistant gram-negative bacteria

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome

OBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients. DESIGN: Multicenter retrospective cohort study. SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020. PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded. MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio2, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves. CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials

Some Patients Are More Equal Than Others:Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome

OBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients. DESIGN: Multicenter retrospective cohort study. SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020. PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded. MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio2, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves. CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials

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

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