Arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is considered a progressive cardiomyopathy. However, data on the clinical features of disease progression are limited. The aim of this study was to assess 12-lead surface electrocardiographic (ECG) changes during long-term follow-up, and to compare these findings with echocardiographic data in our large cohort of patients with ARVC/D. Baseline and follow-up ECGs of 111 patients from three tertiary care centers in Switzerland were systematically analyzed with digital calipers by two blinded observers, and correlated with findings from transthoracic echocardiography. The median follow-up was 4 years (IQR 1.9–9.2 years). ECG progression was significant for epsilon waves (baseline 14% vs. follow-up 31%, p = 0.01) and QRS duration (111 ms vs. 114 ms, p = 0.04). Six patients with repolarization abnormalities according to the 2010 Task Force Criteria at baseline did not display these criteria at follow-up, whereas in all patients with epsilon waves at baseline these depolarization abnormalities also remained at follow-up. T wave inversions in inferior leads were common (36% of patients at baseline), and were significantly associated with major repolarization abnormalities (p = 0.02), extensive echocardiographic right ventricular involvement (p = 0.04), T wave inversions in lateral precordial leads (p = 0.05), and definite ARVC/D (p = 0.05). Our data supports the concept that ARVC/D is generally progressive, which can be detected by 12-lead surface ECG. Repolarization abnormalities may disappear during the course of the disease. Furthermore, the presence of T wave inversions in inferior leads is common in ARVC/D.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Background:The clinical role of atrial fibrillation/atrial flutter (AF-AFl) and variables predicting these arrhythmias are not well defined in patients with arrhythmogenic right ventricular dysplasia (ARVD). We hypothesized that transthoracic echocardiography (TTE) and 12-lead electrocardiography (ECG) would be helpful in predicting AF-AFl in these patients.Methods and Results:ECGs and TTEs of 90 patients diagnosed with definite or borderline ARVD (2010 Task Force Criteria) were analyzed. Data were compared in (1) patients with AF-AFl and (2) all other patients. A total of 18 (20%) patients experienced AF-AFl during a median follow-up of 5.8 years (interquartile range 2.0–10.4). Kaplan-Meier analysis revealed reduced times to AF-AFl among patients with echocardiographic RV fractional area change <27% (P<0.001), left atrial diameter ≥24.4 mm/m2(parasternal long-axis, P=0.001), and right atrial short-axis diameter ≥22.1 mm/m2(apical 4-chamber view, P=0.05). From all ECG variables, P mitrale conferred the highest hazard ratio (3.37, 95% confidence interval 0.92–12.36, P=0.067). Five patients with AF-AFl experienced inappropriate implantable cardioverter-defibrillator (ICD) shocks compared with 4 without AF-AFl (36% vs. 9%, P=0.03). AF-AFl was more prevalent in heart-transplant patients and those who died of cardiac causes (56% vs. 16%, P=0.014).Conclusions:AF-AFl is associated with inappropriate ICD shocks, heart transplantation, and cardiac death in patients with ARVD. Evidence of reduced RV function and atrial dilation helps to identify the ARVD patients at increased risk for AF-AFl. (Circ J 2014; 78: 2854–2861)

Birth‐and‐death processes are widely used to model the development of biological populations. Although they are relatively simple models, their parameters can be challenging to estimate, as the likelihood can become numerically unstable when data arise from the most common sampling schemes, such as annual population censuses. A further difficulty arises when the discrete observations are not equi‐spaced, for example, when census data are unavailable for some years. We present two approaches to estimating the birth, death, and growth rates of a discretely observed linear birth‐and‐death process: via an embedded Galton‐Watson process and by maximizing a saddlepoint approximation to the likelihood. We study asymptotic properties of the estimators, compare them on numerical examples, and apply the methodology to data on monitored populations.

Goals: We aimed to assess the impact of very cold days on inflammatory bowel disease (IBD) flares and infectious gastroenteritis (IG). We defined a cold day using the World Meteorological definition of an ice day, which is a day with a maximum temperature below 0°C. Background: Recently, we have shown that heat waves increase the risk for IG and IBD flares. Study: We retrospectively collected data from 738 IBD and 786 IG patients admitted to the University Hospital of Zurich between 2001 and 2005 and from 506 patients with other noninfectious chronic intestinal inflammations as controls. Climate data were received by the Swiss Federal Office for Meteorology and Climatology. Results: There was no evidence for an increased risk of IBD flares (relative risk, RR = 0.99, 95% confidence interval, CI: 0.72-1.33, p = 0.94) or IG flares (RR = 1.16, 95% CI: 087-1.52, p = 0.30) on very cold days. This negative finding was confirmed in alternative formulations with lagged or cumulative (possibly lagged) effects. Conclusion: In this retrospective controlled observational study, no evidence for an increase in hospital admissions due to flares of IBD and IG during cold days was observed. This may be attributed to not relevantly altered bacterial growth conditions during cold days compared to heat waves.