Recent studies suggest that thrombotic complications are a common phenomenon in the novel SARS-CoV-2 infection. The main objective of our study is to assess cumulative incidence of pulmonary embolism (PE) in non critically ill COVID-19 patients and to identify its predicting factors associated to the diagnosis of pulmonary embolism. We retrospectevely reviewed 452 electronic medical records of patients admitted to Internal Medicine Department of a secondary hospital in Madrid during Covid 19 pandemic outbreak. We included 91 patients who underwent a multidetector Computed Tomography pulmonary angiography(CTPA) during conventional hospitalization. The cumulative incidence of PE was assessed ant the clinical, analytical and radiological characteristics were compared between patients with and without PE. PE incidence was 6.4% (29/452 patients). Most patients with a confirmed diagnosed with PE recieved low molecular weight heparin (LMWH): 79.3% (23/29). D-dimer peak was significatly elevated in PE vs non PE patients (14,480 vs 7230 mcg/dL, p = 0.03). In multivariate analysis of patients who underwent a CTPA we found that plasma D-dimer peak was an independen predictor of PE with a best cut off point of > 5000 µg/dl (OR 3.77; IC95% (1.18–12.16), p = 0.03). We found ninefold increased risk of PE patients not suffering from dyslipidemia (OR 9.06; IC95% (1.88–43.60). Predictive value of AUC for ROC is 75.5%. We found a high incidence of PE in non critically ill hospitalized COVID 19 patients despite standard thromboprophylaxis. An increase in D-dimer levels is an independent predictor for PE, with a best cut-off point of > 5000 µg/ dl.

The value of serial lung ultrasound (LUS) in patients with COVID-19 is not well defined. In this multicenter prospective observational study, we aimed to assess the prognostic accuracy of serial LUS in patients admitted to hospital due to COVID-19. The serial LUS protocol included two examinations (0–48 h and 72–96 h after admission) using a 10-zones sequence, and a 0 to 5 severity score. Primary combined endpoint was death or the need for invasive mechanical ventilation. Calibration (Hosmer–Lemeshow test and calibration curves), and discrimination power (area under the ROC curve) of both ultrasound exams (SCORE1 and 2), and their difference (DIFFERENTIAL-SCORE) were performed. A total of 469 patients (54.2% women, median age 60 years) were included. The primary endpoint occurred in 51 patients (10.9%). Probability risk tertiles of SCORE1 and SCORE2 (0–11 points, 12–24 points, and ≥25 points) obtained a high calibration. SCORE-2 showed a higher discrimination power than SCORE-1 (AUC 0.72 (0.58–0.85) vs. 0.61 (0.52–0.7)). The DIFFERENTIAL-SCORE showed a higher discrimination power than SCORE-1 and SCORE-2 (AUC 0.78 (0.66–0.9)). An algorithm for clinical decision-making is proposed. Serial lung ultrasound performing two examinations during the first days of hospitalization is an accurate strategy for predicting clinical deterioration of patients with COVID-19.

PURPOSE: Recent studies have suggested that machine learning (ML) could be used to predict venous thromboembolism (VTE) in cancer patients with high accuracy. METHODS: We aimed to evaluate the performance of ML in predicting VTE events in patients with cancer. PubMed, Web of Science, and EMBASE to identify studies were searched. RESULTS: Seven studies involving 12,249 patients with cancer were included. The combined results of the different ML models demonstrated good accuracy in the prediction of VTE. In the training set, the global pooled sensitivity was 0.87, the global pooled specificity was 0.87, and the AUC was 0.91, and in the test set 0.65, 0.84, and 0.80, respectively. CONCLUSION: The prediction ML models showed good performance to predict VTE. External validation to determine the result’s reproducibility is necessary.

Background: To compare the severity of pulmonary embolism (PE) and the long-term complications between patients with and without COVID-19, and to investigate whether the tools for risk stratification of death are valid in this population. Methods: We retrospectively included hospitalized patients with PE from 1 January 2016 to 31 December 2022. Comparisons for acute episode characteristics, risk stratification of the PE, outcomes, and long-term complications were made between COVID and non-COVID patients. Results: We analyzed 116 (27.5%) COVID patients and 305 (72.4%) non-COVID patients. In patients with COVID-19, the traditional risk factors for PE were absent, and the incidence of deep vein thrombosis was lower. COVID patients showed significantly higher lymphocyte count, lactate dehydrogenase, lactic acid, and D-dimer levels. COVID patients had PE of smaller size (12.3% vs. 25.5% main pulmonary artery, 29.8% vs. 37.1% lobar, 44.7% vs. 29.5% segmental and 13.2% vs. 7.9% subsegmental, respectively; p < 0.001), less right ventricular dysfunction (7.7% vs. 17.7%; p = 0.007) and higher sPESI score (1.66 vs. 1.11; p < 0.001). The need for mechanical ventilation was significantly higher in COVID patients (8.6% vs. 1.3%; p < 0.001); However, the in-hospital death was less (5.2% vs. 10.8%; p = 0.074). The incidence of long-term complications was lower in COVID cohort (p < 0.001). PE severity assessed by high sPESI and intermediate and high-risk categories were independently associated with in-hospital mortality in COVID patients. Conclusion: The risk of in-hospital mortality and the incidence of long-term complications were lower in COVID-19. The usual tools for risk stratification of PE are valid in COVID patients.