心力衰竭
重症监护医学
医学
心理干预
入射(几何)
人口老龄化
钥匙(锁)
人口
风险分析(工程)
医疗急救
计算机科学
心脏病学
环境卫生
计算机安全
护理部
光学
物理
作者
Massimo Piepoli,Giuseppe Rosano,Ana Abreu,Marianna Adamo,Andrea Attanasio,Ovidiu Chioncel,Alain Cohen-Solal,Gerasimos Filippatos,Eva Gerdts,Andreas Gevaert,Martin Halle,Pardeep Jhund,Donata Kurpas,Brenda Moura,Gianfranco Parati,Mark C Petrie,Eduard Shantsila,Maggie Simpson,Isabella Sudano,Maryia Tokmakova
标识
DOI:10.1093/eurheartj/ehag362
摘要
Heart failure (HF) remains a major cause of morbidity, mortality and costs for the healthcare systems worldwide, despite advances in diagnostic and therapeutic strategies. The enhancement of preventive measures is now a priority, but effective prevention requires a multidisciplinary strategy addressing a broad spectrum of comorbidities and risk factors. It must also consider the changes in the prevailing phenotype of the patients with HF with a lower impact of coronary artery disease and the increasing role of renal and metabolic conditions leading mostly to HF with preserved ejection fraction. Prevention of HF must take into consideration arterial hypertension, chronic kidney disease, diabetes mellitus, sedentary lifestyle, obesity, dyslipidemia, female-specific risk factors, as well as adverse effects of chemotherapy and radiotherapy. Other key factors include infections and the protective role of vaccination, and environmental and socio-economic determinants of health. In 2022, a position paper of the Heart Failure Association and the European Association of Preventive Cardiology of the ESC was published as a complete overview on this topic and as a compendium to the 2021 ESC Guidelines on HF. However, since then, significant evidence has emerged regarding the potential to prevent HF, particularly in the context of metabolic disorders, diabetes and kidney diseases. This scientific statement aims to provide an updated perspective, highlighting the importance of a holistic and tailored approach to managing the multifaceted contributors to this syndrome.
科研通智能强力驱动
Strongly Powered by AbleSci AI