Athletes commonly exhibit a series of electrical, structural, and functional physiological changes which may overlap with cardiac pathology. The last two decades have witnessed a progressive improvement in understanding what can be considered benign for athletes and what may be deemed as potentially pathological and require further investigations. However, diagnostic uncertainties in the cardiac assessment of athletes are often encountered. In particular, the clinical significance of some electrocardiogram (ECG) findings may be uncertain. While uncommon and suggestive of an underlying cardiac condition, they may be identified among healthy athletes without additional pathological findings to support a unifying clinical diagnosis. This creates significant dilemmas for clinicians charged with determining sports eligibility and those who have the responsibility to help athletes in the decision-making process regarding future competitive sports participation. Current guidelines, recommendations, and position papers provide a roadmap for the differential diagnosis between 'athlete's heart' and cardiac disease. However, managing ECG findings of uncertain clinical significance, especially when initial diagnostic evaluation reveals no supportive signs of pathology, has received comparatively less attention, in particular, the type of cardiac investigations, the extent of diagnostic work-up and the need for follow-up require clarification. This document aims to provide guidance based on published evidence and expert opinions to assist in the clinical decision-making regarding ECG anomalies that are common sources of uncertainty when managing asymptomatic athletes.
Understanding human behavior represents a paramount challenge in modern social systems. This task must be tackled with tools that both explain the mechanisms underlying the social dynamics and efficiently handle vast amounts of data. While agent-based models (ABMs) are generally used as simulating tools to describe social dynamics, their connection to data is lackluster. Instead, here we adopt a probabilistic machine learning approach for fitting ABMs to real data. To this end, we propose a variational inference (VI) framework that estimates the macroscopic and microscopic parameters of several opinion dynamics models. Our methodology encompasses three steps: (i) translation of the opinion dynamics models into probabilistic generative models (PGMs), (ii) relaxation of discrete variables to make the models differentiable, and (iii) estimation of the parameters and latent variables via stochastic VI (SVI). Experiments show that VI improves over existing methods in estimating discrete and continuous variables, both at the microscopic and macroscopic scales, in all four different categories of rules opinion dynamics models. Moreover, VI effectively estimates high-dimensional variables, up to 400 agent-level attributes, and is faster than the alternatives.
PURPOSE:This analysis evaluated the influence of tissue and liquid biopsy concordance on outcomes in patients enrolled in the ROME trial. PATIENTS AND METHODS:The ROME trial, a phase II multicenter study, enrolled 1,794 patients with advanced solid tumors. Next-generation sequencing was performed on tissue and liquid biopsies using FoundationOne CDx and FoundationOne Liquid CDx. A centralized molecular tumor board reviewed results to identify actionable alterations, with 400 patients randomly assigned to tailored therapy (TT) or standard-of-care groups. TT improved objective response rate and progression-free survival (PFS) in the intention-to-treat population. Concordance was defined as the detection of the same druggable alteration in both biopsy types; discordance indicated detection in only one. RESULTS:Concordance was present in 49% of cases, with alterations detected exclusively in tissue (35%) or liquid (16%) biopsies. Patients in the concordant group receiving TT experienced improved survival outcomes. The median overall survival was 11.05 versus 7.70 months in the standard-of-care group [HR = 0.74; 95% confidence interval, 0.51-1.07], and the median PFS was 4.93 versus 2.80 months (HR = 0.55; 95% confidence interval, 0.40-0.76), respectively. In contrast, the survival benefit of TT was less pronounced or absent in patients with discordant results. Overall survival was higher in the T + L group (11.05 months), followed by tissue-only (9.93 months) and liquid-only (4.05 months) groups. PFS followed a similar pattern, with the longest PFS in the T + L group (4.93 months) versus 3.06 months in tissue-only and 2.07 months in liquid-only groups. CONCLUSIONS:The study highlights the potential value of integrating both biopsy modalities in selected clinical contexts. See related commentary by Saldanha and Siu, p. 7.
STUDY OBJECTIVES:Large muscle group movements during sleep (LMMS) have recently been recognized as a prevalent feature in patients with restless legs syndrome (RLS), yet their autonomic profile remains insufficiently characterized. This study aimed to compare heart rate (HR) changes associated with LMMS to those accompanying short-interval (SILMS), periodic (PLMS), and isolated leg movements (ISOLMS) during non-REM sleep in RLS. METHODS:Thirty drug-free RLS patients (20 women, mean age 57.6 ± 12.73 years) underwent full-night polysomnography. For each subject, five arousal-associated events per movement type were selected, provided they were isolated by at least 30 seconds of motor/arousal-free sleep. HR changes were analyzed by computing R-R intervals and expressing them as a percentage of baseline, synchronized to movement onset. The area under the curve (AUC,-10 to +20 s), HR change peak, and movement durations were statistically compared using non-parametric tests. RESULTS:LMMS were significantly longer than other movement types (mean duration: 9.3 s vs. <3.0 s for others) and induced the highest HR response (peak: 129.6%, AUC: 369.3%), followed by SILMS (peak: 125.4%, 266.3%), ISOLMS (peak: 118.2%, 173.4%), and PLMS (peak: 118.5%, 166.9%). SILMS and LMMS were associated with rapid and sustained HR increases, without post-peak bradycardia, while PLMS and ISOLMS showed a modest transient bradycardia following the peak. CONCLUSIONS:LMMS are associated with strong autonomic activation indicating parasympathetic withdrawal and/or sympathetic activation, distinguishing them from other sleep-related leg movements in RLS. The absence of post-peak bradycardia suggests reduced parasympathetic buffering, potentially reflecting more sustained arousal mechanisms. Statement of Significance This study provides the first detailed characterization of the heart rate dynamics associated with large muscle group movements during sleep (LMMS) in patients with restless legs syndrome (RLS). By comparing LMMS with established motor patterns such as periodic, isolated, and short-interval leg movements during sleep, we show that LMMS induce the strongest and most sustained autonomic responses. These responses are likely driven by sympathetic activation and/or parasympathetic withdrawal due to sustained arousal-related central autonomic commands. These findings support the hypothesis that LMMS represent a physiologically distinct class of sleep-related motor events with unique implications for cardiovascular and sleep disruption risk in RLS.
Background: Administrative burdens have been identified as a major issue impacting patient care, professional practice, and the overall efficiency of healthcare systems. The aim of this study is to assess the administrative burden faced by Italian hematologists. Methods: A cross-sectional survey that included both closed-ended quantitative questions and open-ended free text answer options was administered to 1,570 hematologists working with malignancies and members of Italian GIMEMA Foundation – Franco Mandelli ONLUS and the Italian Linfomi Foundation (FIL). The survey was conducted online from May 24 to June 30, 2023. Descriptive statistics were computed for the quantitative data to clearly summarize the responses and descriptive analysis of free text responses was carried out. Results: Surveyed hematologists spend an average of 47.07% of their time on administrative tasks, with 63.22% (n = 110) of respondents reporting spending at least half of their time on these activities. More than half (57.47%, n = 100) reported that “Patient care” is the medical task most affected by a lack of time. Additionally, 55.17% (n = 96) reported experiencing burnout in the past 6 months, with filling out “Forms” being identified as the top contributing administrative task by 27.59% (n = 48) of respondents, followed by “Scheduling” (24.71%, n = 43) and “Managing IT system failures” (21.84%, n = 38). Nearly half of the surveyed hematologists (45.40%, n = = 79) identified patient care as the top priority requiring more time. Conclusions: The study confirms that the administrative workload of hematologists has a significant impact on patient care, communication, and burnout risk, reducing the time available for patient care, leading to exhaustion and concern about clinical errors.
