Eiichi Watanabe

Recently, some clinicians have been diagnosing and treating arrhythmias on the basis of electrocardiogram (ECG) devices with low accuracy. In Europe and the US, several statements on the use of ECGs have already been published by related academic societies. In addition, with the relaxation of regulations on media advertising ambulatory/wearable ECG devices, the frequency of use of simple ECG devices by the general public will increase in the future. Therefore, this statement describes the functions and features of non-invasive ambulatory or wearable ECG devices that have been approved as medical devices in Japan (and that can record ECGs remotely), as well as points to note when using them; provides an overview of data storage and security for ambulatory/wearable ECG devices and implantable loop recorders (ILRs), as well as discussing differences between their use and the use of non-invasive ambulatory/wearable ECG devices; and provides classes of recommendation for the use of these devices and their evaluation for each arrhythmia type or condition. We describe lead-based ambulatory ECG devices (classical 24-h Holter ECG monitoring), handheld ECG devices, handheld-based ECG devices using a smartphone, wearable ECG devices (smartwatch and garment ECG devices), and patch ECG devices. In addition, we provide information on methods that are not based on the original ECG, such as photoplethysmography and oscillometric blood pressure measurement, and describe the limitations of their use. We hope that the publication of this statement will lead to the appropriate use of ambulatory/wearable ECG devices in Japan.

Acute heart failure is an important cause of unplanned hospitalizations and poses a significant burden through increased mortality and frequent hospitalizations. Heart failure with preserved ejection fraction (HFpEF) presents as a diverse condition characterized by complex cardiovascular and non-cardiovascular pathology. This study aimed to identify distinct clinical phenotypes in acute decompensated HFpEF (ADHF) using cluster analysis and assess their prognostic significance. We applied a latent class analysis to 1,281 ADHF patients admitted to a single cardiac intensive care unit between 2008 and 2022 with a left ventricular ejection fraction ≥ 50%. We used 83 factors obtained at hospitalization. We evaluated the association between phenogroups and clinical outcomes using either Cox regression model or Fine-Gray competing risk model. We identified 4 phenogroups: Phenogroup 1 (n = 133, 10%) included younger patients with metabolic disorders and a low level of B-type natriuretic peptide (BNP); Phenogroup 2 (n = 346, 27%) had systemic congestion and high BNP levels; Phenogroup 3 (n = 514, 40%) had multiple comorbidities and vascular disorders; Phenogroup 4 (n = 288, 22%) included older patients with bradyarrhythmia and atrial fibrillation. After adjusting for age, sex, and Get with the Guidelines-Heart Failure risk score, Phenogroup 2 had the highest risk of all-cause death and cardiac death. In conclusion, we identified 4 clinically relevant phenogroups of ADHF patients, each associated with different adverse outcomes. Phenotyping may provide a better understanding of the underlying mechanisms involved in the heterogeneity of ADHF and decompensation. Furthermore, it may facilitate the search for phenotype-specific therapeutic strategies.