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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.
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OBJECTIVES: Thoracic endovascular aortic repair is associated with postoperative spinal cord ischemia in approximately 1 to 12.5% of all cases. S100beta is a protein that is released during acute damage of the central nervous system. This study was performed to determine the concentration of S100beta in cerebrospinal fluid during and after stenting of the thoracic aorta in patients at high risk for spinal cord ischemia. DESIGN: Prospective clinical study. MATERIALS AND METHODS: Eight patients who underwent elective thoracic aortic stent grafting underwent lumbar spinal fluid drainage. These patients were at high risk to develop spinal cord ischemia. METHODS: CSF samples for analysis of S100beta protein were drawn after induction of anesthesia, during stenting, once every hour the following six hours and 20 hours after repair. RESULTS: No significant increase in S100beta protein could be detected in CSF and no neurological deficits were detected postoperatively. CONCLUSIONS: The results of this study show us that there is no significant release of S100beta protein in CSF during stenting of the thoracic aorta in this subgroup of patients at high risk for spinal cord ischemia, consistent with clinical exam that there was no significant damage to the central nervous system.