Screening for asymptomatic deep vein thrombosis in COVID-19 patients admitted to the medical ward: a cross-sectional study.

PURPOSE: Patients with COVID-19 have an increased risk for venous thrombo-embolism (VTE), especially pulmonary embolism. The exact prevalence of asymptomatic DVT is not known, as is the usefulness of screening for DVT in patients admitted to ward with COVID-19. We have studied the prevalence of asymptomatic DVT. METHODS: We performed a cross-sectional observational multi-center study at four university medical centers in The Netherlands. All adult patients admitted with COVID-19 to a medical ward were eligible for inclusion, including patients who were transferred back from the ICU to the ward. The study protocol consisted of weekly cross-sectional rounds of compression ultrasound. RESULTS: In total, 125 patients were included in the study. A significant proportion of patients (N = 34 (27%)) had developed a VTE during their admission for COVID-19 before the study ultrasound was performed. In most VTE cases (N = 27 (79%)) this concerned pulmonary embolism. A new asymptomatic DVT was found in 5 of 125 patients (4.0%; 95% CI 1.3-9.1%) (Table 2). Nine patients (7.2%; 95% CI 3.3-13.2%) developed a VTE (all PE) diagnosed within 28 days after the screening US was performed. CONCLUSION: We have shown a low prevalence (4%) of newly discovered asymptomatic DVT outside the ICU-setting in COVID-19 patients. Despite this low prevalence, nine patients developed PE (7%) within 28 days after ultrasound. This favors the hypothesis of local thrombus formation in the lungs. Based on our findings and literature, we do not recommend US-screening of asymptomatic patients with COVID-19 admitted to the ward.

Lung ultrasound to predict gas-exchange response to prone positioning in COVID-19 patients: A prospective study in pilot and confirmation cohorts.

PURPOSE: To examine whether lung ultrasound prior to prone positioning can predict the resulting gas-exchange response. MATERIALS AND METHODS: This is a prospective observational study on critically-ill COVID-19 patients with a pilot and confirmation cohort. Lung ultrasound examinations were performed before prone positioning and gas-exchange parameters were recorded before and after position change. RESULTS: A total of 79 patients, 36 in the pilot cohort and 43 in the confirmation cohort, were included. In the pilot cohort, a moderate correlation between pre-turn lung ultrasound score index (LUSI) and change in PaO2/FiO2 after prone positioning was found. These findings were corroborated and extended upon in the confirmation cohort. The confirmation cohort found that anterior LUSI had the strongest correlation with follow-up time-points 1, 6, 12, and 24 h after prone positioning, with strength of correlation gradually increasing up to 24 h. In a multivariate model anterior aeration loss (odds ratio 0.035; 95%CI 0.003-0.319 for anterior LUSI >50%) and higher pre-turn PaCO2 (odds ratio 0.479 95% CI 0.235-0.979) were negatively predictive of a PaO2/FiO2 increase ≥20 mmHg. CONCLUSIONS: Anterior LUSI, in addition to other clinical parameters, may be used to aid COVID-19 respiratory strategy and a clinician's decision to prone.

[Lung ultrasound in patients with symptoms of covid-19]

The SARS-CoV-2 pandemic presents a challenge for healthcare worldwide. In this context, rapid, correct diagnosis and early isolation of infected persons is of great importance. Pneumonia as an expression of COVID-19 is responsible for the most part of morbidity and mortality. Lung ultrasound can provide valuable information about the diagnosis of a COVID-19 pneumonia in daily practice. A normal ultrasound excludes COVID-19 pneumonia. Conversely, finding abnormalities matching with a COVID-19 pneumonia can be useful for isolation policy. Follow up lung ultrasound visualizes the development of the pneumonia and a possible alternative diagnosis can thereby be determined in the event of a deviating clinical course.

Predicting poor outcome in patients with suspected COVID-19 presenting to the Emergency Department (COVERED) - Development, internal and external validation of a prediction model

BACKGROUND: A recent systematic review recommends against the use of any of the current COVID-19 prediction models in clinical practice. To enable clinicians to appropriately profile and treat suspected COVID-19 patients at the emergency department (ED), externally validated models that predict poor outcome are desperately needed. OBJECTIVE: Our aims were to identify predictors of poor outcome, defined as mortality or ICU admission within 30 days, in patients presenting to the ED with a clinical suspicion of COVID-19, and to develop and externally validate a prediction model for poor outcome. METHODS: In this prospective, multi-center study, we enrolled suspected COVID-19 patients presenting at the EDs of two hospitals in the Netherlands. We used backward logistic regression to develop a prediction model. We used the area under the curve (AUC), Brier score and pseudo-R2 to assess model performance. The model was externally validated in an Italian cohort. RESULTS: We included 1193 patients between March 12 and May 27 2020, of whom 196 (16.4%) had a poor outcome. We identified 10 predictors of poor outcome: current malignancy (OR 2.774;95%CI 1.682-4.576), systolic blood pressure (OR 0.981;95%CI 0.964-0.998), heart rate (OR 1.001;95%CI 0.97-1.028), respiratory rate (OR 1.078;95%CI 1.046-1.111), oxygen saturation (OR 0.899;95%CI 0.850-0.952), body temperature (OR 0.505;95%CI 0.359-0.710), serum urea (OR 1.404;95%CI 1.198-1.645), C-reactive protein (OR 1.013;95%CI 1.001-1.024), lactate dehydrogenase (OR 1.007;95%CI 1.002-1.013) and SARS-CoV-2 PCR result (OR 2.456;95%CI 1.526-3.953). The AUC was 0.86 (95%CI 0.83-0.89), with a Brier score of 0.32 and, and R2 of 0.41. The AUC in the external validation in 500 patients was 0.70 (95%CI 0.65-0.75). CONCLUSION: The COVERED risk score showed excellent discriminatory ability, also in an external validation. It may aid clinical decision making, and improve triage at the ED in health care environments with high patient throughputs.