COVID-19 vaccines have been used to counteract the global COVID-19 pandemic. While these are effective, adverse reactions have been reported, such as injection-site pain, muscle ache, fever, palpitation, and chest discomfort. The release of inflammatory cytokines, such as interleukin (IL)-6 and IL-1β, is a potential mechanism for post-vaccine side-effects. Chest discomfort after the vaccination, including myocarditis and acute coronary syndrome, is a particularly serious adverse reaction. It is important to be familiar with the differential diagnoses of chest discomfort and organ-specific diseases associated with COVID-19 vaccines as the preparation for booster shots and vaccinations among children aged 5–11 years begins. High-intensity exercise, alcohol, tobacco smoking, and baths promote inflammatory cytokines, such as IL-6, which may exacerbate the adverse reactions after vaccination. Japanese data show that deaths during baths are the most common for several days after mRNA vaccination. Additionally, alcohol and tobacco smoking were identified as predictive factors of lower antibody titers after vaccination. In this review, we aimed to provide a few recommendations to prevent vaccine-associated disease.

A 70-years-old male with a history of hypertension and drug resistant paroxysmal atrial fibrillation (AF) presented to our hospital for catheter ablation to his symptomatic AF. He had no prior surgical or percutaneous procedure to close or exclude the left atrial appendage (LAA). A transesophageal echocardiography (TEE) was performed to rule out intra-cardiac thrombus prior to the ablation procedure. Although the TEE imaging at multiple acquisition angles was obtained, the LAA could not be visualized and an absence of the LAA was suspected. An absence of the LAA was confirmed using cardiac computed tomography (CT), which included 3D reconstruction. Additionally, the LAA was not visualized with left atrium (LA) angiography. During the ablation procedure, 3D voltage mapping in LA was created and no low voltage area or abnormal potential was recorded around the usual root location of the LAA. Successful electrical pulmonary vein isolation was achieved with no major complications. After six months of follow-up, the patient remained in sinus rhythm without any antiarrhythmic drugs and showed no related clinical symptoms. He stopped his anticoagulation therapy due to lack of evidence of AF recurrence and an absence of LAA. Multimodality imaging allowed us to identify the congenital absence of LAA.

Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We also compared the performance of the proposed model with those of classical deep learning- and traditional statistical-based models. The present study enrolled 730 patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2020. A recurrent neural network-based model (RNNSurv) involving time-varying covariates was developed and validated. The proposed RNNSurv showed better prediction performance than those of a deep feed-forward neural network-based model (referred as “DeepSurv”) and a multivariate Cox proportional hazard model in view of discrimination (C-index: 0.839 vs. 0.755 vs. 0.762, respectively), calibration (better fit with a 45-degree line), and ability of risk stratification, especially identifying patients with high risk of mortality. The proposed RNNSurv demonstrated an improved prediction performance in consideration of temporal information from time-varying covariates that could assist clinical decision-making. Additionally, this study found that significant risk and protective factors of mortality were specific to risk levels, highlighting the demand for an individual-specific clinical strategy instead of a uniform one for all patients.