Section of Cardiology, Department of Medicine, San Francisco Veterans Affairs Health Care System, San Francisco, California; University of California San Francisco School of Medicine, San Francisco, California.
Section of Cardiology, Department of Medicine, San Francisco Veterans Affairs Health Care System, San Francisco, California.
Am J Cardiol. 2024 Dec 15;233:74-82. doi: 10.1016/j.amjcard.2024.09.030. Epub 2024 Oct 11.
Cardiovascular implantable electronic devices (CIEDs) monitor physiologic variables that could identify subacute heart failure (HF) decompensation and impending HF hospitalization. One such algorithm uses measurements from the previous 30 days of CIED remote monitoring data to predict low-, medium-, or high-probability of HF hospitalization in the next 30 days. We sought to understand how to prospectively implement the use of such algorithms in routine HF care. From January 18, 2024 to April 19, 2024, HF risk categories were predicted from scheduled remote transmissions every 30 days and from unscheduled transmissions for all patients at 2 distinct cardiology clinics. Clinicians contacted and assessed patients at high risk regarding symptoms and then provided an empiric 3-day diuretic intervention (initiation or dose augmentation), adjusted guideline-directed medical therapy, or performed other clinical action as appropriate. Among 358 patients with 1,140 remote transmissions, 72 (20%) had ≥1 transmission categorized as high-risk. The mean patient age was 72.8 years, 346 (97%) were male, and 221 (62%) had a pre-existing diagnosis of HF. Of these 72 patients, 67 (93%) were successfully contacted, 34 (51%) had no HF symptoms, 24 (36%) had mild to moderate symptoms, and 2 (3%) had severe symptoms. A total of 46 patients (69%) had clinical action taken, including 28 (42%) with a diuretic intervention and 12 (18%) with guideline-directed medical therapy augmented. In this implementation study, clinicians contacted and assessed nearly all patients at high risk for HF decompensation based on CIED remote monitoring data and intervened in more than 2/3s. A randomized clinical trial is needed to determine whether this algorithm and subsequent intervention improves clinical outcomes.
心血管植入式电子设备（CIEDs）可监测生理变量，有助于识别亚急性心力衰竭（HF）失代偿和即将发生的 HF 住院情况。有一种算法利用 CIED 远程监测数据前 30 天的测量值，预测未来 30 天 HF 住院的低、中、高概率。我们试图了解如何在 HF 常规护理中前瞻性地使用这些算法。从 2024 年 1 月 18 日至 2024 年 4 月 19 日，每隔 30 天通过计划的远程传输预测 HF 风险类别，对 2 家不同心脏病诊所的所有患者进行非计划传输。临床医生根据症状对高危患者进行联系和评估，然后提供为期 3 天的经验性利尿剂干预（起始或剂量增加），调整指南指导的药物治疗，或根据需要采取其他临床措施。在 358 例接受 1140 次远程传输的患者中，有 72 例（20%）有≥1 次传输被归类为高危。患者平均年龄为 72.8 岁，346 例（97%）为男性，221 例（62%）有 HF 的既往诊断。在这 72 例患者中，67 例（93%）成功联系上，34 例（51%）无 HF 症状，24 例（36%）有轻度至中度症状，2 例（3%）有严重症状。共对 46 例患者（69%）采取了临床措施，包括 28 例（42%）使用利尿剂干预和 12 例（18%）使用指南指导的药物治疗增强。在这项实施研究中，临床医生根据 CIED 远程监测数据联系并评估了几乎所有有 HF 失代偿风险的高危患者，并对超过 2/3 的患者进行了干预。需要进行一项随机临床试验来确定这种算法和随后的干预是否能改善临床结果。
