Why to monitor AF ablation success?

CASTLE-AF Primary Endpoint All-cause mortality Worsening heart failure admissions Secondary Endpoints All-cause mortality Worsening of heart failure admissions Cerebrovascular accidents Cardiovascular mortality Unplanned hospitalization due to cardiovascular reason All-cause hospitalization Quality of Life: Minnesota Living with Heart Failure and EuroQoL EQ-5D Exercise tolerance (6 minutes walk test) Number of delivered ICD shocks, and ATPs (appropriate/inappropriate) LVEF Time to first ICD shock, and time to first ATP Number of device detected VT/VF AF burden: cumulative duration of AF episodes AF free interval: time to first AF recurrence after 3 months blanking period post ablation Marrouche et al. ESC 2017

Survival Probability 1 0.8 0.6 0.4 0.2 0 Results-CASTLE AF All-Cause Mortality HR, 0.53 (95% CI, 0.32-0.86); P=0.011 Log-rank test: P=0.009 Ablation Conventional Risk Reduction: 47% 0 12 24 36 48 60 Follow-Up Time (Months) Patients at Risk Ablation 179 154 130 94 71 27 Conventional 184 168 138 97 63 19

Results-CASTLE AF Proportion of patients in sinus rhythm at follow-up

Results-CASTLE AF AF Burden Derived from Memory of Implanted Devices 70 60 Percent (%) in Time 50 40 30 20 10 0 Baseline 3M 6M 12M 24M 36M 48M 60M AF Burden Ablation Conventional Marrouche et al. ESC 2017

Best Monitoring tool? Holter/Event/MCT Wearabels Wavelet /Apple Watch Implantable loop recorders Smartphone ECG ECG Check/AliveCor

Extended (eight-day) Holter Monitoring Improves Detection of Recurrence Following Atrial Fibrillation Ablation 68% 32% Windfeleder et al HRS 2010

Extended (eight-day) Holter Monitoring Improves Detection of Recurrence Following Atrial Fibrillation Ablation Number of Patients 67% Windfeleder et al HRS 2010

Case 58F with s/p AF ablation presented with fatigue Symptoms of fatigue correlate with bradycardia on Apple Watch No syncope, normal ECG, good exertional tolerance

Consumer-based tools often not rigorously studied Pitfalls Results may falsely concern (or reassure) Implications for treatment / management Image courtesy Wayne (@Toaster_Pastry)

Wearables Photoplethysmography (PPG) Signals Non-invasive optical sensing of blood volume changes at the skin surface PPG during sinus rhythm PPG during AF event* Beat-to-beat signal qualification with sensor fusion Pleth signal

Arrhythmia detected using Wavelet PPG Sinus rhythm Sinus tachycardia with PVC S Atrial fibrillation

Long Term Patterns of PP Curves Normal Rhythm Atrial fibrillation

Utah AF Burden Assessment with Wavelet Exploratory-Arm: AF Burden Assessment HRS 2016 Investigated recurrent AF burden following cardioversion 26 AF patients wore Wavelet wristband Web-based patient monitoring to identify and reach out to not-adherent patients. Overall 30,000 hours of PPG data and 2750 days of activity data. Europace 2017 Weekly surveillance and follow up phone calls (n=10 patients) improved the compliance to 56±4 days/nights (94±7%). Patients with clinical AF episodes (n = 4) correctly detected by the wrist-worn sensor algorithm.

Case 68F s/p Ablation of ablation, s/p Linq implant No symptoms Routine follow up

ECG Check at the same time ILR -Linq recording

How well Implantable Loop Recorders Perform? 100 80 83.5 83.4 96.7 92.8 86.6 84 83.7 81.5 72.5 91.6 95 84.5 77.7 90.6 2 min 6 min 10 min 30 min 1 hour Longest S S S S S S Episode PPV (%) 60 40 25.7 39.4 46.1 46.7 20 0 Syncope Known AF Cryptogenic Stroke AT/AF Burden: accuracy challenged by poor specificity of AT algorithm and false positive episodes (sinus arrhythmia, atrial and ventricular ectopy) Mittal S. et al. Heart Rhythm 2016; 13: 1624 1630

ECG Check

Why Monitor? Circulation. Jun 7 2005;111(22):2875 2880 J Cardiovasc Electrophysiol. Apr 2011;22(4):369 375 Circ Arrhythm Electrophysiol. Apr 1 2010;3(2):141 147

UTAH ECG-Check study

UTAH ECG-Check study Patient Transmission E-mail Rhythm tracing ECG check database Health care provider Intervention

Sinus Rhythm Transmission

Atrial Fibrillation Transmission

Monitoring drugs affecting QT Patients using QT prolongation drugs Dose adjusted in patient based on finding Abnormal

Cryptogenic Stroke Patient AF detected

Phone call with MD Drug prescribed

Patient treating his own Atrial Fibrillation

Study Demopgraphics TABLE 1. Patient characteristics (n=90) Age (years, ±SD) 66.2±11.2 Ejection fraction (%, ±SD) 56±9.9 Body mass index (kg/m2, ±SD) 30.4±6.1 CHADS2 score (±SD) 2.6±1.6 Male gender (n, %) 71% Obstructive sleep apnea (n, %) 21% Prior AF ablation (n, %) 73%

Transmission Correlation with Conventional Monitoring System Number of transmissions (average, ±SD) 258±366 (1-1480) Correlation with ECG/monitor 100%

Reducing ER Visits

Heart Rhythm 2015; 12:554-59

Ease of Use Heart Rhythm 2015; 12:554-59

Limitations of Smartphone Monitoring

Overview of Cardiac Apps/Devices in 2017 High Low Clinical Utility Treadmill HR measurements Validated Patient- Reported Outcome Tools Wavelet Apple Watch FitBit Smartphone Apps Clinical 12L ECG ETT TTE ECGCheck AliveCor Regulatory/Clinical Testing Not Rigorously Evaluated FDA Approved

Conclusions Monitoring for AF Detection Treatment monitoring fopr burden Emerging technologies have potential to greatly impact care of AF #Smartphone Apps Wearables

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