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Intell Based Med ; 6: 100071, 2022.
Article in English | MEDLINE | ID: covidwho-1977322


Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

International Journal of Antimicrobial Agents ; 57(3), 2021.
Article in English | CAB Abstracts | ID: covidwho-1628024


A study was conducted to investigate the pharmacokinetics and cardiac safety of chloroquine in hospitalized patients with COVID-19 who received this treatment in five hospitals in The Netherlands. A total of 83 patients were included, with a median (interquartile range [IQR]) age of 65 (57-73) years. None of the measured concentrations of chloroquine, nor the sum concentration of chloroquine and its metabolite reached the best-case antiviral in vitro IC90 on any treatment day. Furthermore, the median (IQR) protein-unbound fractions of chloroquine and desethylchloroquine in clinical samples were 18.3 (16.2-22.4)% and 20.2 (18.2-25.2)%, respectively. The median (IQR) unbound fractions of chloroquine and desethylchloroquine in cell medium with 10% fetal bovine serum at the 1 ..M concentration were 62.0 (58.4-633)% and 71 (IQR 69.1-73.0)%, respectively. At 10 ..M these were 84.1 (83.8-84.4)% and 88.3 (87.6-88.7)%, respectively. QTc measurements at baseline and during chloroquine treatment were available in 41 patients. In 14 of these patients a ?QTc >60 ms was observed after initiation of chloroquine. QTc measurements during chloroquine treatment were available in 69 patients and a QTc =500 ms during treatment was observed in 32 (46%) of these patients. Torsade de pointes was not reported. No statistically significant differences in plasma concentrations of chloroquine were observed in patients with or without QTc >500 ms.

Research and Practice in Thrombosis and Haemostasis ; 5(SUPPL 2), 2021.
Article in English | EMBASE | ID: covidwho-1509141


Background : Critically ill patients with COVID-19 are at high risk of thromboembolic events, despite thromboprophylaxis with lowmolecular weight heparins (LWMH), while increased-intensity thromboprophylaxis in this patient population is associated with bleeding. This raises the question whether pharmacokinetic (PK) effects of LMWHs are predictable in these patients. Aims : To investigate whether a dosing algorithm for dalteparin administration could be designed based on clinical parameters, using PK modeling with anti-Xa levels as readout. Methods : In this explorative, observational study, we prospectively included 15 adult COVID-19 patients admitted to the intensive care unit receiving dalteparin in prophylactic-intensity (5000 IU dalteparin once daily (OD) for those <100 kg, 5000 IU dalteparin bidaily (BD) for those ≥100 kg) and therapeutic-intensity (100 IU/kg BD). A minimum of 4 anti-Xa samples per day were collected on regular timepoints over 1-3 days. PK analysis of dalteparin was performed by nonlinear mixed-effect modeling (NONMEM v7.4). The final model was used to perform Monte Carlo simulations to predict anti-Xa levels with different dalteparin regimens. The study was approved by the local medical ethics committee. Results : The data were well-fitted to a linear one compartment model. Wide interindividual variation in the parameters absorption (78%) and clearance (34%) of dalteparin was observed, not explained by clinical covariates such as creatinine clearance for elimination rate. Simulations show that prophylactic dosing in individuals <100 kg result in anti-Xa levels within generally used prophylactic targets, while increased-prophylactic dosing in those ≥100 kg result in supraprophylactic levels in 40% of patients. With therapeuticintensity dosing in secondary thromboprophylaxis, 22% of patients would be subtherapeutically, and 19% patients supratherapeutically dosed. Conclusions : Anti-Xa levels during dalteparin treatment in the critically ill COVID-19 patient are difficult to predict and often off-target. Until data from randomized clinical trials conclude on the best dosing, this suggests that anti-Xa measurements are needed to guide high-intensity dosing in the individual patient.

European Journal of Clinical Pharmacology ; 77(SUPPL 1):10-11, 2021.
Article in English | Web of Science | ID: covidwho-1312257