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The main aim of the present study was to psychophysically evaluate smell and taste functions in hospitalized COVID-19 patients and to compare those results with a group of healthy subjects. Another aim of the study was to assess the relationship of changes in patients’ smell and taste functions with a number of clinical parameters, symptoms, and other physiological signs as well as with severity of disease. Olfactory and gustatory functions were tested in 61 hospitalized patients positive for SARS-CoV-2 infection and in a control group of 54 healthy individuals. Overall, we found a significant impairment of olfactory and gustatory functions in COVID-19 patients compared with the control group. Indeed, about 45% of patients self-reported complaints about or loss of either olfactory or gustatory functions. These results were confirmed by psychophysical testing, which showed a significantly reduced performance in terms of intensity perception and identification ability for both taste and smell functions in COVID-19 patients. Furthermore, gustatory and olfactory impairments tended to be more evident in male patients suffering from more severe respiratory failure (i.e., pneumonia with need of respiratory support need during hospitalization).

Currently, pulmonary vein isolation (PVI) is the gold standard in catheter ablation for atrial fibrillation (AF). However, PVI alone may be insufficient in the management of persistent AF, and complementary methods are being explored. One such method takes an anatomical approach—improving both its success rate and lesion durability may lead to improved treatment outcomes. An additional approach complementary to the anatomical one is also attracting attention, one that focuses on epicardial conduction. This involves ethanol ablation of the vein of Marshall (VOM) and can be very effective in blocking epicardial conduction related to Marshall structure; it is becoming incorporated into standard treatment. However, the pitfall of this “Marshall-PLAN”, a method that combines an anatomical approach with ethanol infusion within the VOM (Et-VOM), is that Et-VOM and other line creations are not always successfully completed. This has led to cases of AF and/or atrial tachycardia (AT) recurrence even after completing this lesion set. Investigating effective adjunctive methods will enable us to complete the lesion set with the aim to lower the rates of recurrence of AF and/or AT in the future.

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 ( p  < 0.32). As for ICU admission, the combined model MCC was superior ( p  < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process.

In this study, we assessed the temporal trends of permanent pacemaker implantations in France from 2008 to 2018 using data from the Échantillon Généraliste de Bénéficiaires (EGB) administrative database, a representative sample of the French population. Additionally, we evaluated the impact of transcatheter aortic valve implantations on the overall pacemaker implantation rate. Our data suggest that the incidence of permanent pacemaker implantations in France increased significantly only in patients ≥80 years old, with post–transcatheter aortic valve implantations accounting for at least 2/3 of this increase.

In recent years, machine learning (ML) has been promisingly applied in many fields of clinical medicine, both for diagnosis and prognosis prediction. Aims of this narrative review were to summarize the basic concepts of ML applied to clinical medicine and explore its main applications in the emergency department (ED) setting, with a particular focus on syncope management. Through an extensive literature search in PubMed and Embase, we found increasing evidence suggesting that the use of ML algorithms can improve ED triage, diagnosis, and risk stratification of many diseases. However, the lacks of external validation and reliable diagnostic standards currently limit their implementation in clinical practice. Syncope represents a challenging problem for the emergency physician both because its diagnosis is not supported by specific tests and the available prognostic tools proved to be inefficient. ML algorithms have the potential to overcome these limitations and, in the future, they could support the clinician in managing syncope patients more efficiently. However, at present only few studies have addressed this issue, albeit with encouraging results.

Background: Mitral annular disjunction (MAD) is an anatomical abnormality associated with an increased risk of major arrhythmic events, regardless of the presence of mitral valve prolapse. Cardiac magnetic resonance (CMR) plays a key role in diagnosing MAD and identifying myocardial fibrosis, a marker of arrhythmic vulnerability. Aim: This study reports the experience of the De Gasperis Cardiology Centre at Niguarda Hospital (Milan, Italy) in managing high-risk MAD patients who underwent implantable cardioverter–defibrillator (ICD) implantation and describes their main clinical characteristics. Methods: Between January 2020 and April 2025, five patients with MAD who received ICDs were identified and monitored remotely. Although the small sample size limits generalizability, the objective was to characterize factors associated with arrhythmic susceptibility. Results: Four patients exhibited documented ventricular arrhythmias: two with non-sustained and two with sustained ventricular tachycardia. Notably, CMR did not reveal myocardial fibrosis in two symptomatic cases, suggesting that arrhythmic vulnerability may precede detectable structural abnormalities. The observed coexistence of MAD with arrhythmogenic cardiomyopathies and channelopathies underscores the relevance of comprehensive genetic evaluation in these patients. Conclusions: MAD should be considered a potential arrhythmogenic substrate rather than a benign anatomical variant. A multimodal diagnostic approach and individualized risk stratification—potentially integrating genetic findings—are essential for optimal patient management.

Shortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators. The goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator. We trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case. We developed a VPS called Hepius that allows students to gather clinical information from the patient's medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P<.001) and mean score for core questions (P<.001) when comparing presimulation and postsimulation performance. By combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.

Premature ventricular contractions (PVCs) originating from the left ventricular summit (LVS) present a diagnostic and therapeutic challenge due to their complex anatomical location. The LVS includes an epicardial area of the left ventricle bordered by major coronary arteries, which has been increasingly recognized as an arrhythmic focus. Idiopathic ventricular arrhythmias from this area may exhibit specific electrocardiographic characteristics, making accurate localization essential for effective management. Methods: This narrative review explores the primary features of this arrhythmia, emphasizing key diagnostic and therapeutic aspects, including both pharmacological and interventional approaches, considering the recent technological advances in cardiac mapping and ablations. Conclusions: PVCs originating from the left ventricular summit (LVS) exhibit characteristic electrocardiographic features. Prompt recognition of this arrhythmia may facilitate appropriate referral for targeted treatment.

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Patients with structural heart disease (SHD) are at an increased risk of developing this arrhythmia and are particularly susceptible to the deleterious hemodynamic effects it carries. In the last two decades, catheter ablation (CA) has emerged as a valuable strategy for rhythm control and is currently part of the standard care for symptomatic relief in patients with AF. Growing evidence suggests that CA of AF may have potential benefits that extend beyond symptoms. In this review, we summarize the current knowledge of this intervention on SHD patients.

Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58–83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient’s initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment.