Diabetic Mastopathy: a Case Report

Diabetic mastopathy (DMP) is an uncommon collection of clinical, radiological, and histological features, classically described in premenopausal women with long-term insulin-dependent diabetes mellitus. This entity can mimic breast carcinoma, but, in the appropriate clinical and imaging setting, the diagnosis can be made by core biopsy, avoiding unnecessary surgeries. We report the case of a 34-year-old female, with a 12-year history of type 1 diabetes, who presented with bilateral breast lumps. Mammography, ultrasonography, and magnetic resonance imaging could not exclude the suspicion of malignancy, and a core biopsy was performed showing the typical histologic features of DMP. The literature is briefly reviewed

The Role of a Medical Intermediate Care Unit in the Management of Budd-Chiari Syndrome: Case Series

Budd-Chiari syndrome (BCS) has a wide spectrum of presentations, from an asymptomatic status to acute liver failure (ALF). The therapeutic approach depends on disease severity and related etiology with patients with severe forms of presentation classically managed in intensive care units (ICUs). Here, we report a series of five BCS patients managed in a medical intermediate care unit (IntCU), with three of them presenting with acute liver injury. Progression to ALF was seen in three patients, two of whom died, with one being successfully submitted to liver transplantation. IntCUs allow a 24-h patient surveillance and a prompt management of BCS, with less economic impact when compared to ICUs. Mortality was related to the presence of associated comorbidities that limited therapeutic approach.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry

OBJECTIVE: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. METHODS: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. RESULTS: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. CONCLUSION: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression

Results From the Global Rheumatology Alliance Registry

Funding Information: We acknowledge financial support from the ACR and EULAR. The ACR and EULAR were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Publisher Copyright: © 2022 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.Objective: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. Methods: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. Results: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. Conclusion: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.publishersversionepub_ahead_of_prin

Evaluation of the occurrence of sexual dysfunction and general quality of life in female patients with psoriasis

Background: Psoriasis has a significant impact on quality of life (QoL). Sexual life can also be affected, with sexual dysfunction being reported by 25-70% of patients. Objectives: To determine the occurrence of sexual dysfunction and evaluate QoL in women with psoriasis. Methods: This case-control study included women aged 18-69 years. The validated Brazilian Portuguese versions of the Female Sexual Function Index (FSFI) and of the Medical Outcomes Study 36-Item Short Form Health Survey (SF-36) were administered to all participants to assess sexual function and QoL, respectively. Patients with psoriasis underwent clinical evaluation for the presence of comorbidities, especially psoriatic arthritis and other rheumatic manifestations. Location of lesions and the extent of skin involvement were also assessed. Results: The sample consisted of 150 women, 75 with diagnosis of psoriasis and 75 healthy controls. Prevalence of sexual dysfunction was high in women with psoriasis (58.6% of the sample). Prevalence was statistically higher in women with psoriasis than in controls (P = 0.014). The SF-36 domain scores were also lower in women with psoriasis, with role limitations due to physical health, limitations due to emotional problems, and mental health being the most affected domains. Study limitations: Sample size was calculated to evaluate the association between the occurrence of sexual dysfunction and psoriasis, but it did not include the determination of the possible causes of this dysfunction. Conclusions: QoL and sexual function were altered in women with psoriasis and should be taken into consideration when assessing disease severity