Osborn Waves: History and Significance

The Osborn wave is a deflection with a dome or hump configuration occurring at the R-ST junction (J point) on the ECG (Fig. 1). In the historical view, different names have been used for this wave in the medical literature, such as “camel-hump sign”, “late delta wave”, “hathook junction”, “hypothermic wave”, “J point wave”, “K wave”, “H wave” and “current of injury”.1 Although there is no definite consensus about terminology of this wave, either “Osborn wave” or “J wave” are the most commonly used names for this wave in the current clinical and experimental cardiology. The Osborn wave can be generally observed in hypothermic patients,1,2,3,4 however, other conditions have been reported to cause Osborn waves, such as hypercalcemia,5 brain injury,6 subarachnoid hemorrhage,7 cardiopulmonary arrest from oversedation,8 vasospastic angina,9 or idiopathic ventricular fibrillation.10,11,12 Our knowledge about the link between the Osborn waves and cardiac arrhythmias remains sparse and the arrhythmogenic potential of the Osborn waves is not fully understood. In this paper, we present a historic review of Osborn waves and discuss their clinical significance in the various clinical settings

Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J-RHYTHM registry

BACKGROUND: Machine learning (ML) has emerged as a promising tool for risk stratification. However, few studies have applied ML to risk assessment of patients with atrial fibrillation (AF). HYPOTHESIS: We aimed to compare the performance of random forest (RF), logistic regression (LR), and conventional risk schemes in predicting the outcomes of AF. METHODS: We analyzed data from 7406 nonvalvular AF patients (median age 71 years, female 29.2%) enrolled in a nationwide AF registry (J‐RHYTHM Registry) and who were followed for 2 years. The endpoints were thromboembolisms, major bleeding, and all‐cause mortality. Models were generated from potential predictors using an RF model, stepwise LR model, and the thromboembolism (CHADS(2) and CHA(2)DS(2)‐VASc) and major bleeding (HAS‐BLED, ORBIT, and ATRIA) scores. RESULTS: For thromboembolisms, the C‐statistic of the RF model was significantly higher than that of the LR model (0.66 vs. 0.59, p = .03) or CHA(2)DS(2)‐VASc score (0.61, p < .01). For major bleeding, the C‐statistic of RF was comparable to the LR (0.69 vs. 0.66, p = .07) and outperformed the HAS‐BLED (0.61, p < .01) and ATRIA (0.62, p < .01) but not the ORBIT (0.67, p = .07). The C‐statistic of RF for all‐cause mortality was comparable to the LR (0.78 vs. 0.79, p = .21). The calibration plot for the RF model was more aligned with the observed events for major bleeding and all‐cause mortality. CONCLUSIONS: The RF model performed as well as or better than the LR model or existing clinical risk scores for predicting clinical outcomes of AF

Prevalence of atrial fibrillation in Asia and the world

Atrial fibrillation (AF) is the most common arrhythmia in persons of advanced age, and it is a potent risk factor for cardiogenic ischemic stroke. The overall prevalence of AF is less than 1%, but in people aged 80 years or older the rate is approximately 7–14% in Western countries and 2–3% in Japan. The number of people with AF has been increasing worldwide as the population has aged, and continued increases in the prevalence and incidence of AF are expected with the aging of society. It is predicted that 5–16 million in the United States and more than 1 million in Japan will be affected by 2050. Therefore, AF is one of important diseases that needs to be managed because it is a common disease in aged populations