ABSTRACT
Background: Thrombosis is a frequent and severe complication in COVID-19 patients admitted to the intensive care unit (ICU). Lupus anticoagulant (LA) is a strong acquired risk factor for thrombosis in various diseases and is frequently observed in COVID-19 patients. Whether LA is associated with thrombosis in patients with severe COVID-19 is currently unclear. Aim(s): To investigate if LA is associated with thrombosis in critically ill COVID-19 patients. Method(s): The presence of LA and other antiphospholipid antibodies was assessed in COVID-19 patients admitted to the ICU. Informed consent was obtained by an opt-out approach and the study was approved by the local medical ethical committee. LA was determined with dilute Russell's Viper Venom Time (dRVVT) and LA-sensitive Activated Partial Thromboplastin Time (aPTT) reagents. Statistical analysis to study the association of LA and other antiphospholipid antibodies with thrombosis occurrence was performed using logistic regression. Result(s): Out of 169 COVID-19 patients, 116 (69%) tested positive for at least one antiphospholipid antibody upon admission to the ICU. Forty (24%) patients tested positive for LA;of whom 29 (17%) tested positive with a dRVVT, 19 (11%) tested positive with an LA-sensitive aPTT and eight (5%) tested positive on both tests. Fifty-eight (34%) patients developed thrombosis after ICU admission. The odds ratio (OR) for thrombosis in patients with LA based on a dRVVT was 2.4 (95%-CI: 1.1-5.4), which increased to 5.1 (95%-CI: 1.7-15.4) in patients on or below the median age of this study population (64 years). LA-positivity based on a dRVVT or LA-sensitive aPTT was only associated with thrombosis in patients younger than 65 years (OR: 4.2, 95%-CI: 1.5-11.7). Conclusion(s): LA on admission is strongly associated with thrombosis in critically ill COVID-19 patients, especially in patients <65 years of age.
ABSTRACT
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.