Retroperitoneal perivascular epithelioid cell tumor in a 47-years old woman

Perivascular epithelioid cell tumors (PEComa) are rare neoplasms defined as mesenchymal tumors with distinctive histology and immunohistochemistry. They tend to occur in middle-aged patients and mostly in women. The tumours cells show an association with the walls of blood vessels. Most PEComas are benign although a proportion of these tumours behave aggressively. Case report of 47-years-old woman with retroperitoneal mass and laparoscopic resection that pathological anatomy showed retroperitoneal tumour compatible with PEComas. We describe the main characteristics of PEComas as well as their therapeutic approach. Resumen: Los tumores de células epitelioides perivasculares (PEComa) son neoplasias raras definidas como tumores mesenquimales con histología e inmunohistoquímica distintivas. Suelen aparecer en pacientes de mediana edad y principalmente en mujeres. Las células tumorales muestran una asociación con las paredes de los vasos sanguíneos. La mayoría de los PEComas son benignos, aunque una proporción de estos tumores se comporta de forma agresiva. Se presenta el caso de una mujer de 47 años con una masa retroperitoneal y resección laparoscópica cuya anatomía patológica mostró un tumor retroperitoneal compatible con PEComa. Describimos las principales características de los PEComas así como su abordaje terapéutico

Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model

Objectives: We aimed to develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of coronavirus disease 2019 (COVID-19), to identify patients at risk of critical outcomes. Methods: We used data from the SEMI-COVID-19 Registry, a cohort of consecutive patients hospitalized for COVID-19 from 132 centres in Spain (23rd March to 21st May 2020). For the development cohort, tertiary referral hospitals were selected, while the validation cohort included smaller hospitals. The primary outcome was a composite of in-hospital death, mechanical ventilation, or admission to intensive care unit. Clinical signs and symptoms, demographics, and medical history ascertained at presentation were screened using least absolute shrinkage and selection operator, and logistic regression was used to construct the predictive model. Results: There were 10 433 patients, 7850 in the development cohort (primary outcome 25.1%, 1967/7850) and 2583 in the validation cohort (outcome 27.0%, 698/2583). The PRIORITY model included: age, dependency, cardiovascular disease, chronic kidney disease, dyspnoea, tachypnoea, confusion, systolic blood pressure, and SpO2 ≤93% or oxygen requirement. The model showed high discrimination for critical illness in both the development (C-statistic 0.823; 95% confidence interval (CI) 0.813, 0.834) and validation (C-statistic 0.794; 95%CI 0.775, 0.813) cohorts. A freely available web-based calculator was developed based on this model (https://www.evidencio.com/models/show/2344). Conclusions: The PRIORITY model, based on easily obtained clinical information, had good discrimination and generalizability for identifying COVID-19 patients at risk of critical outcomes