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Objective: to assess the utility of peripheral venous lactate (PVL) in Emergency Department patients. Methods: arteriovenous agreement was assessed in three subgroups: PVL <2 mmol/l, PVL ≥ 2 mmol/l to < 4 mmol/l and PVL ≥ 4 mmol/l. The predictive value of PVL to predict arterial lactate (AL) ≥2 mmol/l was assessed at different cut-off values. Results: 74 samples were analysed. The venous-arterial mean difference and 95% limits of agreement for the subgroups were 0.25 mmol/l (-0.18 to 0.68), 0.37 mmol/l (-0.57 to 1.32) and -0.89 mmol/l (-3.75 to 1.97). PVL ≥2 mmol/l predicts AL ≥2 mmol/l with 100% sensitivity. Conclusion: PVL <2 mmol/l rules out arterial hyperlactatemia. As agreement declines in higher levels, arterial sampling should be considered.

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.