Silent atrial fibrillation in pacemaker early post-implantation period: an unintentionally provoked situation?

Aims: Atrial high-rate episodes (AHREs) compatible with silent AF detected in pacemakers (PM) are related to an increased risk of stroke and silent ischaemic brain lesions (IBL) on CT scan. AHREs soon after PM implantation could be related with the procedure itself and the prognosis might be different. Methods and results: We analysed the incidence of AHREs >5 min and the presence of silent IBL in 110 patients (56% men, aged 75 ± 9 year-old) with PM and no history of AF, in relation to time from implantation (≤3 months vs. >3 months) and the atrial lead fixation (LF) (active vs. passive). Mean CHADS2 and CHA2DS2VASc scores were 1.9 ± 1.2 and 3.5 ± 1.5, respectively. Time from implantation was ≤3 months in 88 patients (80%). Active LF was used in 55 patients (50%). After 24 ± 9 months, AHREs were present in 40 patients (36.4%). CT-scan showed silent IBL in 26 patients (23.6%). The presence of AHREs at 3 months was more frequent in the patients with recent PM implantation (17% vs. 4.5%, P = 0.09) and significantly related to active LF (OR 5.36, 1.43-20.07; P < 0.05). The presence of silent IBL was related to the detection of AHREs during follow up (OR 3.12, 1.29-7.97; P < 0.05) but not with AHREs at first 3 months (OR 1.58, 0.49-5.05; P = 0.44). Conclusions: AHREs occur frequently during the first 3 months after PM implantation and could be related with procedure itself and the use of active LF. AHREs in this period might not be related to worse outcomes and should be interpreted cautiously.

CorVue algorithm efficacy to predict heart failure in real life: Unnecessary and potentially misleading information?

BACKGROUND: Heart failure (HF) hospitalizations have a negative impact on quality of life and imply important costs. Intrathoracic impedance (ITI) variations detected by cardiac devices have been hypothesized to predict HF hospitalizations. Although Optivol™ algorithm (Medtronic, Minneapolis, MN, USA) has been widely studied, CorVue™ algorithm's (St. Jude Medical, St. Paul, MN, USA) long-term efficacy has not been systematically evaluated in a "real-life" cohort. METHODS: CorVue™ was activated in implantable cardioverter defibrillator (ICD)/cardiac resynchronization therapy defibrillator (CRT-D) patients to store information about ITI measures. Clinical events (new episodes of HF requiring treatment and hospitalizations) and CorVue™ data were recorded every 3 months. Appropriate CorVue™ detection for HF was considered if it occurred in the 4 prior weeks to the clinical event. RESULTS: Fifty-three ICD/CRT-D (26 ICD and 27 CRT-D) patients (67 ± 1 years old, 79% male) were included. Device position was subcutaneous in 28 patients. At inclusion, mean left ventricular ejection fraction was 25 ± 7% and 27 patients (51%) were in New York Heart Association class I, 18 (34%) in class II, and eight (15%) in class III. After a mean follow-up of 17 ± 9 months, 105 ITI drops alarms were detected in 32 patients (60%). Only six alarms were appropriate (true positive) and required hospitalization. Eighteen patients (34%) presented 25 clinical episodes (12 hospitalizations and 13 emergency room/ambulatory treatment modifications). Nineteen of these clinical episodes (76%) remained undetected by the CorVue™ (false negative). Sensitivity of CorVue™ resulted in 24%, specificity was 70%, positive predictive value of 6%, and negative predictive value of 93%. CONCLUSIONS: CorVue™ showed a low sensitivity to predict HF events. Therefore, routinely activation of this algorithm could generate misleading information.

Inappropriate automatic mode switching episodes: What's the mechanism?

We present a case series of five patients reporting abnormal automatic mode switching (AMS) episodes during routinary cardiac defibrillator (ICD) and pacemaker (PM) follow-up. This non-previously described phenomenon was reported to St. Jude Medical (Abbott) Technical Support that confirmed the inappropriate automatic mode switching.

Silent ischaemic brain lesions related to atrial high rate episodes in patients with cardiac implantable electronic devices.

AIMS: Monitoring capabilities of cardiac implantable electronic devices have revealed that a large proportion of patients present silent atrial fibrillation (AF) detected as atrial high rate episodes (AHREs). Atrial high rate episodes >5 min have been linked to increased risk of clinical stroke, but a high proportion of ischaemic brain lesions (IBLs) could be subclinical. METHODS AND RESULTS: We prospectively analysed the incidence of AHRE > 5 min in 109 patients (56% men, aged 74 ± 9 years) and the presence of silent IBL on computed tomography (CT) scan. Mean CHADS2 and CHA2DS2VASc scores were 2.3 ± 1.3 and 3.9 ± 1.6, respectively. Seventy-five patients (69%) had no history of AF or stroke/transient ischaemic attack (TIA). After 12 months, 28 patients (25.7%) showed at least one AHRE. Patients with AHREs were more likely to have history of AF. Computed tomography scan showed silent IBL in 28 (25.7%). The presence of IBL was significantly related to older patients, prior history of AF or stroke/TIA, higher CHADS2 or CHA2DS2VASc scores, and the presence of AHRE. Multivariable analysis demonstrated that AHRE was an independent predictor for silent IBL in overall population [hazard ratio (HR) 3.05 (1.06-8.81; P < 0.05)] but also in patients without prior history of AF or stroke/TIA [HR 9.76 (1.76-54.07; P < 0.05)]. CONCLUSION: Cardiac implantable electronic devices can accurately detect AF as AHRE. Atrial high rate episodes were associated to a higher incidence of silent IBL on CT scan. Atrial high rate episodes represent a kind of silent AF where management recommendations are lacking despite the fact that a higher embolic risk is present.

Limitations of the AutoCapture™ Pacing System in patients with cardiac stimulation devices.

AIMS: AutoCapture (St Jude Medical) is a technological development that confirms ventricular capture analysing the evoked response after a pacing impulse and adjusts the energy output to changes in the stimulation threshold. Although this algorithm is aimed to assure capture minimizing energy consumption, some patients might not benefit from it. The objective of this study is to identify them. METHODS AND RESULTS: Long-term AutoCapture efficiency was assessed using the data recorded in the programmer reports of patients undergoing scheduled pacemaker check-ups during 2012 in our institution. We have evaluated 160 consecutive patients (58% men) aged 78 ± 9 years. Pacemaker stimulation mode was DDD in 116 patients (72.5%) and VVI in 44 patients (27.5%). During the scheduled visits for pacemaker check-up, 73 patients (45.6%) showed abnormalities in the long-term AutoCapture function report (high variability in the AutoCapture stimulation threshold and/or out-of-range values). After multivariate analysis, abnormal AutoCapture pattern was associated to the presence of atrial fibrillation [odds ratio (OR) 3.96 (1.59-9.82; P < 0.05)]; and a ventricular pacing ≤25% of the time [OR 4.80 (2.09-11.05; P < 0.05)]. AutoCapture abnormalities were also described in three (1.8%) patients with very low stimulation threshold. CONCLUSION: Although AutoCapture algorithm has shown both efficacy and safety, our findings suggest that some patients with atrial fibrillation or those requiring ventricular pacing ≤25% of the time may not benefit from it. Activation of the algorithm should be individualized according to the patient's characteristics and long-term AutoCapture pattern checked in the routine follow-up.

How to recognize silent atrial fibrillation in pacemakers and defibrillators-the value of atrial electrograms.

Today's pacemakers and defibrillators include diagnostic tools for detecting and treating cardiac arrhythmias like silent atrial fibrillation as atrial high rate episodes (AHREs). This diagnostic capability is crucial to prevent the potential embolic complications this AHREs are related to. However, sometimes data retrieved from diagnostic counters may be misleading reflecting limitations of detection algorithms, which must follow mathematical rules to classify events on a beat-to-beat basis. The incorporation of stored electrograms has been an important milestone in improving the diagnostic capabilities of these devices confirming the arrhythmia diagnosis.