Vamos Mate, Nyolczas Noemi, Bari Zsolt, Bogyi Peter, Muk Balazs, Szabo Barna, Ancsin Bettina, Kiss Robert G, Duray Gabor Z
University Hospital Frankfurt - Goethe University.
Cardiol J. 2018;25(2):236-244. doi: 10.5603/CJ.a2017.0077. Epub 2017 Jun 27.
The reliability of intrathoracic impedance monitoring for prediction of heart failure (HF) by implantable cardiac devices is controversial. Despite using additional device-based parameters described in the PARTNERS HF study, such as new onset of arrhythmias, abnormal autonomics, low biventricular pacing rate or patient activity level, the predictive power of device diagnostic algorithm is still in doubt. The objective of this study was to compare the device diagnostic algorithm described in the PARTNERS HF study to a newly developed algorithm applying refined diagnostic criteria.
Fourty two patients were prospectively enrolled who had been implanted with an intrathoracic impedance and remote monitoring capable implantable cardiac defibrillator with a cardiac resychroniza-tion therapy (CRT-D) device in this observational study. If a remote OptiVolTM alert occurred, patients were checked for presence of HF symptoms. A new algorithm was derived from the original PARTNERS HF criteria, considering more sensitive cut-offs and changes of patterns of the device-based parameters.
During an average follow-up of 38 months, 722 remote transmissions were received. From the total of 128 transmissions with OptiVol alerts, 32 (25%) corresponded to true HF events. Upon multivariate discriminant analysis, low patient activity, high nocturnal heart rate, and low CRT pacing (< 90%) proved to be independent predictors of true HF events (all p < 0.01). Incorporating these three refined criteria in a new algorithm, the diagnostic yield of OptiVol was improved by increasing specific-ity from 37.5% to 86.5%, positive predictive value from 34.1% to 69.8% and area under the curve from 0.787 to 0.922 (p < 0.01), without a relevant loss in sensitivity (96.9% vs. 93.8%).
A refined device diagnostic algorithm based on the parameters of low activity level, high nocturnal heart rate, and suboptimal biventricular pacing might improve the clinical reliability of OptiVol alerts.
植入式心脏设备通过监测胸内阻抗来预测心力衰竭(HF)的可靠性存在争议。尽管使用了PARTNERS HF研究中描述的基于设备的其他参数,如心律失常的新发、自主神经功能异常、双心室低起搏率或患者活动水平,但设备诊断算法的预测能力仍受到质疑。本研究的目的是将PARTNERS HF研究中描述的设备诊断算法与应用改进诊断标准的新开发算法进行比较。
在这项观察性研究中,前瞻性纳入了42例植入了具有心脏再同步治疗(CRT-D)功能且能进行胸内阻抗和远程监测的植入式心脏除颤器的患者。如果出现远程OptiVolTM警报,检查患者是否存在HF症状。在原始PARTNERS HF标准的基础上推导了一种新算法,考虑了更敏感的临界值和基于设备参数的模式变化。
在平均38个月的随访期间,共收到722次远程传输。在128次带有OptiVol警报的传输中,32次(25%)对应真正的HF事件。多变量判别分析显示,患者活动量低、夜间心率高和CRT起搏率低(<90%)被证明是真正HF事件的独立预测因素(所有p<0.01)。将这三个改进标准纳入新算法后,OptiVol的诊断效率得到提高,特异性从37.5%提高到86.5%,阳性预测值从34.1%提高到69.8%,曲线下面积从0.787提高到0.922(p<0.01),而敏感性没有相关损失(96.9%对93.8%)。
基于低活动水平、高夜间心率和双心室起搏欠佳参数的改进设备诊断算法可能会提高OptiVol警报的临床可靠性。