Mace Matthew I, Lala Anuradha, Fendler Timothy J, Sauer Andrew J
Academy for Health Care Science (AHCS), 6 The Terrace, Rugby Road, Lutterworth, Leicestershire, LE17 4BW, UK.
, 54 State St, STE 804 #13308, Albany, NY, 12207, USA.
Heart Fail Rev. 2025 May 9. doi: 10.1007/s10741-025-10513-2.
Unplanned admissions for worsening heart failure (WHF) are the largest resource cost in heart failure (HF) management. Despite advances in pharmacological agents and interventional therapy, HF remains a global epidemic. One crucial-and costly-gap in HF management is the inability to obtain objective information to identify and quantify congestion and personalize treatment plans to effectively manage WHF events without resorting to expensive, invasive methods. Although the causes of WHF are varied and complex, the universal effect of HF decompensation is the significant decline in quality of life due to symptoms of hypervolemic congestion and the resultant reduction in cardiac output, which can be quantified via increased pulmonary venous congestion due to high intracardiac filling pressures. Accessible and reliable markers of congestion could more precisely quantify the severity of WHF events and stabilize patients earlier by interrupting and reversing this process with timely introduction or modification of evidence-based treatments. Pulmonary artery and cardiac pressure sensing tools have gained evidential credence and increased clinical uptake in recent years for the prevention and treatment of WHF, as studies of implantable hemodynamic devices have iteratively and reliably demonstrated substantial reductions in WHF events. Recent advances in sensing technologies have ranged from single-parameter invasive pulmonary artery monitors to completely non-invasive multi-parameter devices incorporating multi-sensor concept technologies aided by machine learning or artificial intelligence, although many remain investigational. This review aims to evaluate the potential for novel pulmonary artery and cardiac pressure sensing technology to reshape the management of WHF from within the hospitalized and ambulatory care environments.
因心力衰竭加重（WHF）导致的非计划住院是心力衰竭（HF）管理中最大的资源成本。尽管药物治疗和介入治疗取得了进展，但HF仍然是一种全球流行病。HF管理中一个关键且代价高昂的差距在于，无法获得客观信息来识别和量化充血情况，以及在不采用昂贵的侵入性方法的情况下制定个性化治疗方案以有效管理WHF事件。尽管WHF的病因多样且复杂，但HF失代偿的普遍影响是由于高血容量充血症状导致生活质量显著下降，以及心输出量随之减少，而这可通过因高心内充盈压导致的肺静脉充血增加来量化。可获取且可靠的充血标志物能够更精确地量化WHF事件的严重程度，并通过及时引入或调整循证治疗来中断和逆转这一过程，从而更早地使患者病情稳定。近年来，肺动脉和心脏压力传感工具在预防和治疗WHF方面已获得证据支持并在临床上得到更多应用，因为对植入式血流动力学装置的研究反复且可靠地证明了WHF事件大幅减少。传感技术的最新进展涵盖了从单参数侵入性肺动脉监测器到完全非侵入性的多参数设备，后者采用了机器学习或人工智能辅助的多传感器概念技术，不过许多仍处于研究阶段。本综述旨在评估新型肺动脉和心脏压力传感技术在重塑住院和门诊护理环境中WHF管理方面的潜力。
