Objective: To determine the incidence, characteristics, and risk factors of pulmonary embolism (PE) among patients hospitalized for COVID-19. Patients and Methods: We performed a prospective observational study of a randomly selected cohort of consecutive patients hospitalized for COVID-19 infection between March 8, 2020 through April 25, 2020. All eligible patients underwent a computed tomography pulmonary angiography independently of their PE clinical suspicion and were pre-screened for a baseline elevated D-dimer level. Results: 119 patients were randomly selected from the 372 admitted to one tertiary hospital in Valencia (Spain) for COVID-19 infection during the period of study. Seventy-three patients fulfilled both the inclusion criteria and none of the exclusion criteria and were finally included in the study. Despite a high level of pharmacological thromboprophylaxis (89%), the incidence of PE was 35.6% (95% confidence interval [CI], 29.6 to 41.6%), mostly with a peripheral location and low thrombotic load (Qanadli score 18.5%). Multivariate analysis showed that heart rate (Hazard Ratio [HR], 1.04), room-air oxygen saturation (spO2) (HR, 0.87), D-dimer (HR, 1.02), and C-reactive protein (CRP) levels (HR, 1.01) at the time of admission were independent predictors of incident PE during hospitalization. A risk score was constructed with these four variables showing a high predictive value of incident PE (AUC-ROC: 0.86; 95% CI: 0.80 to 0.93). Conclusions: Our findings confirmed a high incidence of PE in hospitalized COVID-19 patients. Heart rate, spO2, D-dimer, and CRP levels at admission were associated with higher rates of PE during hospitalization.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

Mastitis obliterans is an uncommon and late manifestation of ductal ectasia. We report a case of a woman with a long-term type 2 diabetes, referred to us because of a palpable right breast mass. Mammography showed an asymmetry in the palpated area. Ultrasonography was consistent with a an irregular, hypoechoic mass with indistinct margins and linear tracts to the skin. The biopsy showed a fibrotic component surrounding dilated galactophore ducts, which were collapsed by an infiltrate of lymphocytes and histiocytes corresponding to mastitis obliterans. The differential diagnosis should be made between diabetic fibrous mastopathy, granulomatous mastitis and lobular carcinoma. In our opinion, the therapeutic approach should depend on the symptomatology and should be individualized for each patient owing to the lack of information on this pathology, adopting therefore a conservative attitude.

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