Atrial fibrillation (AF) is the most common arrhythmia, increasing with age and comorbidities. Obstructive sleep apnea (OSA) is a chronic sleep disorder more common in older men. It has been shown that OSA is linked to AF. Nonetheless, the prevalence of OSA in patients with AF remains unknown because OSA is significantly underdiagnosed. This review, including 54,271 patients, carried out a meta-analysis to investigate the association between OSA and AF. We also performed a meta-regression to explore cofactors influencing this correlation. A strong link was found between these two disorders. The incidence of AF is 88% higher in patients with OSA. Age and hypertension independently strengthened this association, indicating that OSA treatment could help reduce AF recurrence. Further research is needed to confirm these findings. Atrial Fibrillation (AF) is the most common arrhythmia, increasing with age and comorbidities. Obstructive sleep apnea (OSA) is a regulatory respiratory disorder of partial or complete collapse of the upper airways during sleep leading to recurrent pauses in breathing. OSA is more common in older men. Evidence exists that OSA is linked to AF. Nonetheless, the prevalence of OSA in patients with AF remains unknown because OSA is underdiagnosed. In order to investigate the incidence of AF in OSA patients, we carried out a meta-analysis including 20 scientific studies with a total of 54,271 subjects. AF was present in 4801 patients of whom 2203 (45.9%) had OSA and 2598 (54.1%) did not. Of a total of 21,074 patients with OSA, 2203 (10.5%) had AF and 18,871 (89.5%) did not. The incidence of AF was 88% higher in patients with OSA. We performed a meta-regression to explore interacting factors potentially influencing the occurrence of AF in OSA. Older age and hypertension independently strengthened this association. The clinical significance of our results is that patients with OSA should be referred early to the cardiologist. Further research is needed for the definition of the mechanisms of association between AF and OSA.

It is not clear as to which is the best treatment among surgery and stereotactic radiotherapy (SBRT) for lung oligometastases. A systematic review of literature with a priori selection criteria was... Lay abstract Patients with metastatic cancer with a limited number of deposits may benefit from surgical removal or irradiation of tumor nodules in addiction to chemotherapy. Surgical resection has...

Osteopetrosis (from the Greek “osteo”: bone; “petrosis”: stone) is a clinically and genetically heterogeneous group of rare diseases of the skeleton, sharing the same main characteristic of an abnormally increased bone density. Dense bones in radiological studies are considered the hallmark of these diseases, and the reason for the common term used: “Marble bone disease”. Interestingly, a radiologist, Dr. Albers-Schonberg, described this disease for the first time in Germany in 1904. Indeed, radiology has a key role in the clinical diagnosis of osteopetrosis and is fundamental in assessing the disease severity and complications, as well as in follow-up controls and the evaluation of the response to treatment. Osteopetrosis includes a broad spectrum of genetic mutations with very different clinical symptoms, age onset, and prognosis (from mild to severe). This diversity translates into different imaging patterns related to specific mutations, and different disease severity. The main recognized types of osteopetrosis are the infantile malignant forms with autosomal recessive transmission (ARO—including the rarer X-linked recessive form); the intermediate autosomal recessive form (IAO); and the autosomal dominant ones ADO, type I, and type II, the latter being called ‘Albers-Schonberg’ disease. Imaging features may change among those distinct types with different patterns, severities, skeletal segment involvement, and speeds of progression. There are several classical and well-recognized radiological features related to osteopetrosis: increased bone density (all types with different degrees of severity assuming a ‘Marble Bone Appearance’ especially in the ARO type), different metaphyseal alterations/enlargement including the so-called ‘Erlenmeyer flask deformity’ (particularly of femoral bones, more frequent in ADO type 2, and less frequent in ARO and IAO), ‘bone in bone’ appearance (more frequent in ADO type 2, less frequent in ARO and IAO), and ‘rugger-jersey spine’ appearance (typical of ADO type 2). After conducting an overview of the epidemiological and clinical characteristic of the disease, this review article aims at summarizing the main radiological features found in different forms of osteopetrosis together with their inheritance pattern.

Background: This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors. Methods: Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models. Results: Random Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score. Conclusions: The use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG.