BACKGROUND:Recurrence after atrial fibrillation (AF) ablation is frequent. Monitoring with long-term electrocardiograms (ECGs) is constrained by limited monitoring time, measurement dispersion, and cost. Selected photoplethysmography (PPG) smartphone applications have demonstrated excellent accuracy for AF detection and could mitigate these limitations. OBJECTIVE:We aimed to compare the effectiveness of digital follow-up using a PPG-based smartphone application against conventional ECG-based follow-up for the detection of atrial arrhythmia recurrence after ablation. METHODS:Patients undergoing AF ablation were consecutively enrolled and monitored by a 24-hour ECG at 3, 6, and 12 months on top of the ECGs conducted for clinical indications (conventional follow-up). In addition, patients were instructed to perform PPG measurements twice daily or whenever symptoms were perceived during the course of 1 year (digital follow-up). RESULTS:In total, 96 patients (69% male; mean age, 64 ± 9 years) performed 39,895 PPG measurements. The compliance rate (number performed/prescribed) was 92.6% for ECGs and 78.2% for PPG recordings. After 1 year of follow-up, atrial arrhythmia recurrence was detected in 17.7% of patients by conventional follow-up and in 38.5% of patients by digital follow-up (odds ratio, 3.4; 95% confidence interval [CI], 1.7-7.1). The CI lower limit exceeded the predefined noninferiority margin (P for noninferiority > .001). Hence, superiority was tested (P for superiority = .001). The negative predictive value of digital follow-up for atrial arrhythmia detected with conventional follow-up was 98.3% (95% CI, 90.9%-99.9%). CONCLUSION:Digital rhythm follow-up using a smartphone application with PPG was noninferior to conventional follow-up in detecting atrial arrhythmia recurrence between 3 and 12 months after ablation. Moreover, digital follow-up significantly increased the detection of atrial arrhythmia.

Abstract Introduction Predicting atrial fibrillation (AF) recurrence post catheter ablation may help to assess procedural eligibility and determine AF management. While several predictors of AF recurrence post ablation have been established and numerous clinical risk scores have been proposed, their performance remained underwhelming and clinical utility was limited. Hence, new predictors are needed. Artificial intelligence (AI) algorithms using deep neural networks (DNN) to analyze biometrical data might generate such predictors. DNN algorithms have been developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether these algorithms can function as a predictor of AF recurrence after AF ablation remains unknown. Purpose To evaluate the prediction of AF recurrence after ablation using an AI-enabled ECG algorithm trained to predict AF on an ECG in sinus rhythm. Methods This study retrospectively analyzed observational data from the DIGITOTAL study, that monitored AF recurrence with a PPG-based smartphone application in 96 subjects after AF ablation. Patients with a 12-lead ECG in SR available within a timeframe of 3 months before the ablation procedure were included in the analysis. Although all patients had a history of AF, an AF-risk score was calculated by the DNN described elsewhere.1 Results An ECG in sinus rhythm was available in 53 patients (14 women [26.4%]; mean [SD] age, 62.0 [9.7] years) out of the 96 patients followed-up in the DIGITOTAL study. Testing the DNN on the last ECG before the ablation procedure resulted in an area under the receiver operating curve (AUC) of 0.65 (95% CI, 0.49 - 0.80), and an area under the precision recall curve (AUPRC) of 0.56 (95% CI, 0.34 - 0.78). The optimal cutoff score resulted in a sensitivity of 60.0% (95% CI, 36.1% - 80.9%), specificity of 66.7% 66.7% (95% CI, 48.2% - 82.0%), accuracy of 0.64 (95% CI, 0.50 - 0.77), F1-score of 55.8% (95% CI, 38.5% - 74.4%), positive predictive value 52.2% (95% CI, 30.6% - 73.2%) and negative predictive value 73.3% (95% CI, 54.1% - 87.7%). Patients classified in the high-risk group versus low-risk group were more likely to exhibit AF recurrence up to one year after AF ablation (hazard ratio, 2.6; 95% CI, 1.1 - 6.5; P-value = 0.037). Conclusions The AI-enabled ECG algorithm, trained to predict AF on a sinus rhythm ECG, was able to predict AF recurrence after ablation with an accuracy comparable to the existing clinical risk scores. Further studies are needed to determine whether the DNN score can be used as an independent predictor and improve existing risk scores.

Abstract Introduction Postoperative atrial fibrillation (POAF) occurring after cardiac surgery is common and associated with adverse outcomes. Systematic monitoring of POAF beyond discharge is cumbersome. The emergence of photoplethysmography (PPG)-based rhythm monitoring with digital consumer devices could potentially mitigate these hurdles. Selected smartphone applications leveraging this technology have demonstrated excellent usability and accuracy for the detection of atrial fibrillation (AF). However, the impact of PPG-detected POAF on real-life clinical practice remains uncertain. Purpose To determine whether intermittent PPG-based smartphone rhythm monitoring, after being discharged home following cardiac surgery, impacts AF management. Methods The SURGICAL-AF 2 study is a pragmatic, investigator-initiated, open-label, multicenter, randomized clinical trial, conducted in three Belgian centers. The intervention group performed one-minute rhythm checks three times daily with a smartphone-based PPG application after hospitalization for cardiac surgery until the first follow-up visit with a cardiologist, scheduled at 21 – 91 days. Rhythm monitoring was not mandated in the usual care group. The primary endpoint was a composite of initiation of oral anticoagulation (OAC), cardioversion, up-titration or initiation of antiarrhythmic drugs (Vaughan-Williams class I or III) or implantation of a cardiac implantable electronic device (CIED). Secondary endpoints were incidence of POAF and actionable POAF, defined as a detection in patients with CHA2DS2-VASc score ≥2 for women or ≥1 for men who are not treated with OAC. Results Of the 450 patients randomized (238 patients in the intervention group and 212 patients in the usual care group; mean [SD] age, 64.1 [9.2] years; 96 women [21.3%]; 130 patients with AF before inclusion [28.9%]; 103 patients on OAC [22.9%], median [interquartile range] CHA2DS2-VASc score, 2 [1-3]), 98.7% completed the trial. In the intent-to-treat analysis, the primary end point occurred in 24 patients (10.3%) in the intervention group versus 5 patients (2.4%) in the usual care group (odds ratio (OR) 4.7, 95% CI, 1.8 - 12.6; P =.002). POAF was detected in 44 patients (18.8%) in the intervention group and was actionable in 25 patients (10.7%) versus 4 patients (1.9%) in the usual care group and actionable in 2 patients (0.9%). (POAF detection, OR 12.0, 95% CI, 4.2 - 34.5; P < .001; actionable POAF, OR 12.5, 95% CI, 2.9 - 52.6; P < .001) Conclusions In unselected patients discharged home following cardiac surgery, PPG-based smartphone monitoring revealed significantly more POAF which led to changes in AF management (OAC initiation, rhythm control therapy or CIED insertion). Longer follow-up is needed to determine whether these changes will lead to improved outcomes.

Abstract Introduction Postoperative atrial fibrillation (POAF) is common after cardiac surgery and is associated with adverse outcomes. Systematic monitoring of POAF is cumbersome, specifically beyond discharge. Therefore, risk stratification may aid to identify patients at high risk of POAF and guide monitoring strategies alongside preventive measures. However, the performance of bedside risk stratification models reliant on clinical risk factors remained underwhelming, necessitating the exploration of more sophisticated models that maintain clinical applicability. Hence, artificial intelligence algorithms (AI) have been suggested to reinforce or replace clinical risk scores. As such, a deep neural network (DNN) algorithm was developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether this algorithm can identify patients at high risk of POAF remains unknown. Purpose To evaluate the usability of an AI-enabled ECG algorithm, that was trained to predict AF in non-surgical conditions, for the prediction of POAF. Methods This study retrospectively analyzed data from the SURGICAL-AF trial that monitored patients after cardiac surgery. The inclusion criteria for this subanalysis comprised: (1) patients without a history of AF prior to cardiac surgery; (2) availability of the raw data of a pre-operative 12-lead ECG in sinus rhythm; and (3) patients with POAF (during hospitalization or up to 91 days after discharge) or patients having completed PPG-based rhythm monitoring per protocol. The AF-risk score was calculated by the DNN described elsewere.1 Results In total, 127 patients (mean [SD] age, 63.4 [8.4] years; 30 women [23.6%];, median [interquartile range] CHA2DS2-VASc score, 2 [1-3]) complied with the inclusion criteria, out of the 450 patients randomized in the SURGICAL-AF trial. Testing the DNN on the last ECG before cardiac surgery resulted in an area under the receiver operating curve (AUC) of 0.66 (95% CI, 0.56 - 0.77) and an area under the precision-recall curve of 0.57 (95% CI, 0.42 -0.72). The optimal cut of score resulted in a sensitivity of 64.3% (95% CI, 48.0%-78.4%), specificity of 64.7% (95% CI, 53.6%-74.8%), accuracy of 0.65 (95% CI, 0.56 - 0.73), F1-score of 54.5% (95% CI, 42.9% - 66.8%), positive predictive value of 47.4% (95% CI, 34.0%-61.0%), and negative predictive value of 78.6% (95% CI, 67.1%-87.5%). POAF occurred within three months after cardiac surgery in 23 patients out of 57 patients classified in the high-risk group (40.4%) versus 15 patients (21.4%) out of 70 patients classified in the low-risk group (hazard ratio, 2.2; 95% CI, 1.2 – 4.3; P-value = 0.020). Conclusions The AI-enabled ECG algorithm, trained to predict AF on a pre-operative sinus rhythm ECG, was able to identify POAF with an accuracy comparable to existing clinical risk scores. Further studies are needed to determine whether the DNN score can be used as an independent predictor and improve existing risk scores.

Abstract Funding Acknowledgements Type of funding sources: None. Background Hemostasis in the left atrial (LA) appendage (LAA) is a common cause of stroke, particularly in atrial fibrillation (AF). LAA flow have been determined to quantify LAA function. Objectives To investigate whether LAA peak flow velocities early after cryptogenic stroke have a predictive value for AF occurrence and to determine the determinants of impaired flow. Methods Consecutive cryptogenic stroke patients (139) were enrolled. LAA flow velocities during sinus rhythm were assessed early post-stroke with transesophageal echocardiography. All patients received rhythm monitoring using 7-day Holter and if negative, an implantable cardiac monitor. Results 47 Patients (34%) developed AF during a median follow-up of 554 days, median time to AF diagnosis was 93 days. Both, LAA filling (LAAfv) and emptying (LAAev) velocities were lower in stroke patients who developed AF (resp. 44.3±14.2 cm/s and 50.7±13.3 cm/s) compared with those who didn’t (resp. 59.8±14.0 cm/s and 76.8±17.3 cm/s). LAAev was the strongest predictor of AF with an AUC of 0.878 in ROC curve analysis and with optimal cut-off value of 55 cm/s. Age and mitral regurgitation were independent determinants of reduced LAAev. Conclusions Reduced LAA flow velocities can identify cryptogenic stroke patients at high risk of occult AF, and who may benefit from intensive rhythm monitoring or anticoagulation strategies.

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Special Research Fund (Bijzonder Onderzoeksfonds, BOF) Hasselt University. This study is part of Limburg Clinical Research Center, supported by the foundation Limburg Sterk Merk, province of Limburg, Flemish government, Hasselt University, Ziekenhuis Oost-Limburg and Jessa Hospital. Background Atrial fibrillation (AF) is a major cause of ischaemic stroke. Oral anticoagulation is recommended in stroke survivors with AF to prevent recurrence. Prolonged ECG monitoring using insertable cardiac monitors (ICMs) has been shown to increase the detection rate of AF compared to standard 24-hour ECG monitoring in cryptogenic stroke patients. However, prolonged ambulatory ECG monitoring is underutilized, likely contributing to an underdiagnosis of AF and missed anticoagulation treatment opportunities for secondary stroke prevention. Purpose This study aims to evaluate how our evolving cardiac monitoring strategy affected AF detection rates one year after cryptogenic ischaemic stroke or transient ischaemic attack (TIA) in a tertiary care centre. Methods We retrospectively identified all consecutive cryptogenic stroke or TIA patients admitted to our centre between 1/01/2017 – 1/01/2022. Patients with a pacemaker or implantable cardioverter-defibrillator were excluded from the analysis. Data were collected from the electronic medical record. Available cardiac monitoring modalities included 24-hour Holter monitoring, 7-day Holter monitoring, and insertable cardiac monitors (ICM). After October 2020 (i.e., period 2), the latter became part of our routine diagnostic workup for AF detection in case extended (7-day) Holter monitoring was negative. Results All 691 cryptogenic stroke or TIA patients admitted to our hospital during the inclusion period were considered. These were elderly patients (69.7 ± 13.2 years) with a CHA2DS2-VASc score of 3 [2 - 5]. Figure 1 shows evolving trends in the use of the different cardiac monitoring tools before and after October 2020. In particular, the use of 7-day Holter monitoring and long-term continuous monitoring with ICM increased after implementing our new diagnostic protocol compared to 24-hour Holter monitoring (p Conclusions The increased use of prolonged cardiac monitoring in cryptogenic stroke or TIA patients resulted in a two-fold increase in AF detection one year after stroke. Therefore, our results underscore the need to implement guideline-recommended prolonged rhythm monitoring using ICMs in addition to short-term ECG monitoring to ensure adequate secondary stroke prevention.

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The increasing availability of smartphones has enabled rhythm monitoring in large populations using standalone photoplethysmography (PPG) apps or singe-lead electrocardiography (ECG) with add-on devices. Current guidelines note that when atrial fibrillation (AF) is suspected by an automated algorithm, confirmation on an ECG tracing is required. The use of PPG alone to establish the diagnosis is not generally accepted, even when overread. The performance of physicians to discriminate between sinus rhythm (SR) and AF based on PPG alone is unknown. Purpose To study the performance of physicians to detect AF based on PPG vs single-lead ECG vs 12-lead ECG, and to explore the incremental value of a tachogram, Poincaré plot, and algorithm output to the interpretation of the PPG waveform by physicians. Methods PPG, single-lead ECG and 12-lead ECG data were simultaneously recorded in 30 patients. Diagnostic reference was the 12-lead ECG, read by two cardiologists. Cardiologists, electrophysiologists and cardiology fellows were invited to analyse the data of 30 patients (10 in SR, 10 in SR with extrasystoles and 10 in AF) through online surveys and classify the readings as ‘SR’, ‘ectopic/missed beats’, ‘AF’, ‘flutter’ or ‘unreadable’. For dichotomous analysis, ‘unreadable’ was reclassified as incorrect, the other options were reclassified as AF ‘present’ or ‘absent’. In the first survey, PPG data were presented subsequently as a waveform, stepwise adding the tachogram and Poincaré plot, and algorithm information. In the next two surveys, the single-lead and 12-lead ECG traces were presented. Sensitivity and specificity for all presentations were calculated with respect to the reference diagnosis. Diagnostic performances were compared with the Obuchowski-Rockette’s ANOVA approach with Jackknife covariance estimation and Benjamini-Hochberg correction. Results Sixty-five physicians completed the PPG survey and analysed the PPG waveforms with 88.8% sensitivity and 86.3% specificity for AF. The diagnostic metrics significantly increased to 95.5% sensitivity (P < 0.001) and 92.5% specificity (P < 0.001) after providing the tachogram and Poincaré plot. Fifty-seven physicians completed both ECG surveys and analysed the single-lead ECG outputs with 91.2% sensitivity and 93.9% specificity, while 12-lead ECG outputs were analysed with 93.9% sensitivity and 98.6% specificity. Hence, qualitative analysis of a PPG waveform with tachogram and Poincaré plot had a similar diagnostic performance to detect AF compared to single-lead ECG analysis and a similar sensitivity (P = 0.792) but lower specificity (P = 0.035) compared to 12-lead ECG. Conclusions PPG rhythm recordings, analysed by physicians as a waveform in combination with the corresponding tachogram and Poincaré plot, achieve similar diagnostic accuracy as single-lead ECG to detect AF. Abstract Figure.

Abstract Background In the awakening era of mobile health, wearable devices capable of detecting atrial fibrillation (AF) are on the rise. Smartwatches and wristbands are equipped with photoplethysmography (PPG) technology that enables (semi)continuous rhythm monitoring. These devices have been pioneered already in a few screening trials. However, such devices are being spread among consumers at a pace that is not paralleled by the evidence supporting their clinical performance. This imbalance reflects the urgent need for validation studies. Purpose To determine the diagnostic performance of an artificial intelligence algorithm to detect AF using photoplethysmography acquired by a smartwatch. Methods One hundred patients (≥18 years) without a pacemaker-dependent heart rhythm who were referred to a university hospital or a large tertiary hospital for elective 24-hour ECG Holter monitoring were asked to wear a continuous PPG monitoring smartwatch (i.e. Samsung GWA2 or Empatica E4) simultaneously with the Holter. All activities of daily life were allowed. The ECG trace and PPG waveform were synchronised and fragmented in one-minute fragements. The one-minute ECG fragments were labelled as AF, non-AF, or insufficient quality based on the routine clinical interpretation of the 24-hour Holter (i.e. software + physician overreading). The one-minute PPG fragments were analysed by an artificial intelligence (AI) algorithm (i.e. FibriCheck) and were given the same labels. Diagnostic metrics of the PPG AI algorithm were calculated with respect to the ECG interpretation, for all fragments with sufficient quality for both PPG and ECG. Results Four patients had to be excluded due to technical error (3 Holter errors, 1 smartwatch error). The mean age in the remaining study population (n=96) was 59±16 years, 51 (53%) were men and 15 (15.6%) were known with permanent AF. In this population, simultaneous ECG and PPG monitoring was recorded for 115,245 one-minute fragments. Fragments of insufficient quality for ECG (n=1,454; 1.3%), PPG (n=25,704; 22.3%) or both (n=15,362; 13.3%) were excluded. PPG fragments were more frequently of insufficient quality (p<0.001). AF was present in 10,255 (14.1%) of the resulting 72,725 high-quality one-minute fragments. The sensitivity of PPG to detect AF was 93.4% (CI 92.9% - 93.8%). The specificity of PPG to exclude AF was 98.4% (CI 98.3% - 98.5%). As a result, the overall accuracy of the PPG algorithm on one-minute fragment level was 97.7% (CI 97.6%- 97.8%). Conclusion Continuous out-of-hospital PPG monitoring using a smartwatch in combination with an AI algorithm can accurately discriminate between AF and non-AF rhythms in a heterogenous patient population. PPG quality is more often affected than ECG quality during daily life activities. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Research Foundation-Flanders, Strategic Basic Research Fund

Abstract Funding Acknowledgements Type of funding sources: None. Background Smartphone apps using photoplethysmography (PPG) technology enable digital heart rhythm monitoring through their built-in camera, without the need for additional, specific, or costly hardware. This may positively impact the availability and scalability of remote monitoring. However, the diversity of smartphone specifications on the consumer market may raise concerns regarding the robustness of AF detection algorithms between various devices. Purpose To study the device independency of AF detection performance by a PPG-based smartphone application. Methods Patients from the cardiology department were consecutively enrolled. Patients were handed 7 iOS models and 1 Android model and were asked to consecutively perform one PPG measurement per device. A 12-lead electrocardiogram (ECG) was collected during the same consultation and interpreted by a cardiologist as reference diagnosis. To allow an objective comparison across the devices, patients who failed to perform one successful measurement on each device were excluded. Additional exclusions were atrial flutter rhythms and insufficient quality results. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was used for the head-to-head comparison of the sensitivity and specificity of the proprietary algorithm on the different smartphone devices. Results A total of 150 patients participated in the study with a median CHA2DS2-VASc score of 3 (interquartile range: 1-5). The median age of the study population was 70 (interquartile range: 56-79) years. In total, 54.7% of the population was male and the AF-prevalence was 35.3%. After the exclusion of patients with atrial flutter (n = 14) and patients who did not successfully perform a PPG measurement on each device (n = 5), diagnostic-grade results of 131 patients were used to calculate the performance of the proprietary algorithm. The sensitivity and specificity of the AF detection algorithm ranged from 90.9% (95% CI 75.7-98.1) to 100.0% (95% CI 91.0-100) and 94.5% (95% CI 86.6-98.5) to 100.0% (95% CI 94.6-100), respectively. The overall accuracy across the devices ranged from 94.4% (95% CI 88.3-97.9) to 99.0% (95% CI 94.6-100). Head-to-head comparisons of the results did not reveal significant differences in sensitivity (P = 0.125-1.000) or specificity (P = 0.375-1.000) of the proprietary AF detection algorithm among the different devices. Conclusion This study demonstrated the device-independent nature of the PPG-deriving smartphone application with respect to 12-lead ECG diagnosis.

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Limburg Clinical Research Center OnBehalf Mobile Health Unit & Future Health Background Cryptogenic stroke (CS) and transient ischemic attack (TIA) patients have no determined aetiology at discharge. A possible cause for stroke is atrial fibrillation (AF). AF occurs in 20%-40% of the CS patients and diagnosis is highly dependent on monitoring duration. A long-term monitoring method is the insertable loop recorder (ILR), recommended by the European Society of Cardiology. However, this is not routinely used in Belgium despite reimbursement. Purpose This study aims to determine the AF detection rates of different methods used in clinical practice, ranging from short-term monitoring (monitoring in a stroke unit, 12-lead electrocardiogram (ECG), and 24-hour ECG), seven-day Holter, and long-term monitoring (smartphone application and ILR). Methods A monocentric, retrospective study was conducted in adults with CS or TIA between 1/01/17 - 1/01/20. Data were collected from the electronic medical record. The primary endpoint was the detection rate and time until first AF detection. Results A total of 368 patients suffered from a CS or TIA. Most of them were monitored in the stroke unit (96%) or with a 12-lead ECG (93%). A 24-hour ECG was used in 26%, a seven-day Holter in 38%. For long-term monitoring, a smartphone application was used in 3%. ILRs were inserted in 6%, with a median time of 102 days after stroke (IQR: 48-321). One year after ILR insertion, AF was detected in 23%. AF detection increased with monitoring duration, as shown in the figure, except for 24-hour ECG, which detected no AF. Therefore, the AF detection rate was different between short-term monitoring (5%) and seven-day Holter (10%; p=.034), and short- and long-term monitoring (16%; p=.01). The age of CS patients without AF (Mdn = 71yr) was lower than those with AF (Mdn = 79yr; p=.001). The National Institutes of Health Stroke Scale (NIHSS) and the CHA2DS2-VASc score of patients without AF (Mdn = 3) was lower than those with AF (Mdn = 6, p<.001; Mdn = 4, p=.004 respectively). The one-year mortality of patients with AF was 15% compared to 8% for patients without AF. No patients with an ILR deceased within one year after the stroke. Conclusions Detection of AF was associated with higher age, NIHSS, and CHA2DS2-VASc scores. These variables can be used to select patients for the insertion of ILRs. The detection rate of AF was significantly higher with long-term monitoring and seven-day Holter compared to short-term monitoring. However, only 38% of the patients were monitored for a week, and only 6% had an ILR inserted. Therefore, despite guideline recommendations, long-term cardiac monitoring is underutilised in this population of CS patients. Nevertheless, AF was still detected in 14% of CS patients within one year after the stroke. These findings emphasise the need for more monitoring with a seven-day Holter, smartphone app, and ILR in this patient population. Consequently, this will result in more accurate treatment of AF as secondary prevention of CS. Abstract Figure. Time to first AF detection after stroke

Abstract Funding Acknowledgements Type of funding sources: None. Background Population based screening for atrial fibrillation (AF) has been suggested to reduce stroke. Photoplethysmography (PPG) deriving smartphone apps and single-lead electrocardiography (ECG) tools are attractive devices for screening due to their low cost, convenience, and accessibility. Automated algorithm analysis can serve as pre-screening or remote monitoring for AF, while confirmation on an ECG trace >30s is required to establish the diagnosis. This work directly compares the performance of proprietary algorithms on PPG vs single-lead ECG for the detection of AF. Purpose To evaluate and compare the diagnostic performance of a PPG-deriving smartphone app and a single-lead ECG-deriving handheld device for AF detection. Methods Patients were recruited from the cardiology ward. After obtaining written informed consent, demographic and medical information were collected. Patients were instructed to perform one measurement using a pulse-deriving smartphone app and one via a single-lead ECG handheld device. A 12-lead electrocardiogram (ECG) was collected and interpreted by a cardiologist as gold standard. Patients with atrial flutter were excluded, with additional exclusions for insufficient quality measurements and unsuccessful measurements resulting due to technical errors. Unclassified single-lead ECG measurements were handled as test-negative. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was performed to compare the sensitivity and specificity of the proprietary PPG and single-lead ECG AF detection algorithms. Results The median age in the study population (n = 300) was 70 years (interquartile range: 51-78), 56.3% were men, and the median CHA2DS2-VASc was 3 (interquartile range: 1-4) with an AF-prevalence of 32.3%. PPG signal and single‑lead ECG quality was sufficient in 272/300 (91.0%) and 278/298 (93.3%) participants, respectively. After excluding atrial flutter patients (n = 25) and insufficient quality measurements, the sensitivity and specificity were 97.6% (95% CI 93.8 to 99.3) and 94.1% (95% CI 86.8 to 98.1) for the PPG signal versus 95.7% (95% CI 91.4 to 98.3) and 91.1% (95% CI 83.2 to 96.1) for the single‑lead ECG signal, respectively. Results demonstrated a 96.4% (95% CI 93.2 to 98.3) accuracy for PPG and 94.1% (95% CI 90.4 to 96.6) for single-lead ECG. No significant differences in sensitivity (P = 0.453) or specificity (P = 0.219) between the proprietary PPG and single-lead ECG algorithms were found. Conclusion This study demonstrated equivalent diagnostic performance of PPG and single-lead ECG proprietary AF detection algorithms in smartphone apps.

Abstract Background In the awakening era of mobile health, wearables equipped with photoplethysmography (PPG) technology to monitor the heart rate (HR) and rhythm are on the rise. Smartwatches and wristbands enable HR monitoring for consumers at massive scale. Unfortunately, once consumers become patients, physicians are limited by insufficient evidence to support the clinical use of PPG based wearables. Accurate identification of heartbeats is the first step in the interpretation of PPG traces and should be validated. Purpose To assess the agreement between continuous PPG monitoring using a smartwatch and continuous ECG Holter monitoring in the identification of heartbeats and calculation of the HR. Methods One hundred patients (≥18 years) without a pacemaker-dependent heart rhythm who were referred to a university hospital and a large tertiary hospital for elective 24-hour ECG Holter monitoring were asked to wear a continuous PPG monitoring smartwatch (i.e. Samsung GWA2 or Empatica E4) simultaneously with the 24-hour Holter monitor. All activities of daily life were allowed. The ECG trace and PPG waveform were synchronised and fragmented in one-minute fragments. The one-minute ECG fragments were labelled as AF, non-AF, or insufficient quality based on the routine clinical interpretation (i.e. software + physician overreading), and the average HR during each fragment was calculated by Holter algorithm. The PPG fragments were analysed by an artificial intelligence (AI) algorithm (i.e. FibriCheck) that labelled fragments as sufficient or insufficient quality, identified the number of heartbeats and calculated the HR. The agreement between the HR on ECG and PPG in sufficient quality tracings was analysed with linear regression, Pearson's product-moment correlation and Bland-Altman analysis. A subanalysis was performed for AF rhythm and non-AF rhythms. Results A total of 72,725 simultaneous ECG and PPG one-minute fragments were recorded in 96 patients, after excluding 4 patients (due to 3 Holter and 1 smartwatch technical error) and 42,520 minutes (36.9%) of insufficient quality (ECG 1,454 (1.3%); PPG 25,704 (22.3%), ECG and PPG 15,362 (13.3%)). The correlation (r=0.935) between ECG and PPG HR was statistically significant (CI 0.934–0.936; P<0.001), with a mean difference between ECG and PPG of 0.8bpm. The lower and upper limit boundary (LLB and ULB; defined as ±1.96 SD) were −8.0bpm and 9.7bpm, respectively, i.e. 95% of PPG measurements identified the HR within 8bpm below or 10bpm above the ECG reference. The mean difference between ECG and PPG HR in the AF subgroup (n=10,255 (14.1%)) was 0.9bpm (LLB −8.4bpm; ULB 10.2bpm) and 0.8bpm in the non-AF subgroup (LLB −0.8bpm; ULB 9.6bpm). Conclusion The AI algorithm analysing continuous out-of-hospital PPG tracings can annotate heartbeats and assess HR without a clinically significant bias compared to continuous ECG monitoring, both during AF and non-AF rhythms in a heterogenous patient population. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation-Flanders, Strategic Basic Research Fund Correlation plot & Bland-Altman plot

Percutaneous mitral valve repair using MitraClip offers symptomatic benefit and improves rest and exercise hemodynamics in patients with severe functional mitral regurgitation (MR). Recent randomized trials have shown contradictory results regarding the impact of MitraClip on mid-term survival in functional MR. It is unknown whether improved hemodynamics are related to patients" outcome. To assess whether residual MR and altered resting and exercise hemodynamics are predictors of outcome in patients with functional MR treated with MitraClip. Consecutive patients (n = 45, 72 ± 10years, left ventricular ejection fraction (LVEF) 34 ± 9%) with symptomatic severe functional MR were prospectively evaluated by Doppler echocardiography at rest and during symptom-limited exercise on a semi-supine bicycle pre- and 6 months post-MitraClip procedure. LVEF, MR severity, cardiac output (CO), systolic pulmonary artery pressure (SPAP) and a flow-corrected SPAP/CO ratio were assessed at rest and peak exercise. 2-year follow-up clinical data were collected from patient records. During 2-year follow-up post-MitraClip, 15 patients (33%) experienced major cardiac events (hospitalization for heart failure (n = 14) and/or cardiac death (n = 5)). Age, gender, a history of coronary artery disease, diabetes, baseline MR severity and baseline SPAP/CO ratio at rest and during exercise were not related to a worse event-free survival. In contrast, patients with events at 2-year follow up had more often a history of hospitalization for heart failure (73 vs. 37%, p = 0.029), lower baseline LVEF (30 ± 8 vs. 36 ± 10%, p = 0.041), more residual MR at 6 months post-MitraClip (MR jet area/left atrial area 27 ± 14 vs. 15 ± 10%, p = 0.004) and higher SPAP/CO ratios at rest and during exercise 6 months post-MitraClip (13.9 ± 5.3 vs. 9.9 ± 3.4mmHg/L/min, p = 0.007 and 13.6 ± 4.9 vs. 9.4 ± 4.6mmHg/L/min, p = 0.009, respectively). When corrected for baseline LVEF, residual MR 6 months post-MitraClip remained an independent predictor for worse 2-year outcome. Residual MR was moderately correlated to a worse SPAP/CO ratio 6 months post-MitraClip (Pearson Rho 0.518, p < 0.001). In patients with functional MR treated with MitraClip, residual MR at 6-month follow-up is associated with impaired hemodynamics, and is an independent predictor of cardiac events at 2-year follow-up.

Abstract Background Smartphone applications using photoplethysmography (PPG) technology through their camera are becoming an attractive alternative for atrial fibrillation (AF) screening due to their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy, robustness and device independent nature remain to be answered. Purpose This study evaluated the diagnostic accuracy of a PPG-based pulse-deriving smartphone application with respect to handheld single-lead ECG and 12-lead ECG. In addition, the device dependent nature and robustness of the performance of the application was assessed. Methods 300 Patients who are scheduled for a regular consultation or procedure (i.e. ablation or cardioversion) will be recruited from the cardiology ward. Additionally, patients hospitalized for continuous cardiac monitoring will be recruited to enrich the database with AF measurements. After obtaining written informed consent, the patients fill in a questionnaire collecting demographic and medical information. The pulse-deriving application will be tested on total of 14 different smartphones, 7 iOS devices and 7 Android devices. In total, each device will be measured with 150 times. The patients will additionally perform a single-lead ECG measurement with a handheld device. Subsequently, a 12-lead ECG will be recorded to obtain the reference diagnosis. Results A total of 164 patients already participated in the study. The mean age was 64 (±19) years, 58% was male. The AF-prevalence was 37%. On average, patients in AF had a higher CHA2DS2-VASc score; 3.93 (±1.80) compared to 2.02 (±1.63) for non-AF patients. The amount of insufficient quality measurements recorded with the pulse-deriving smartphone application ranged from 4% (iOS) to 13% (Android). Averaged for all the smartphone devices, the pulse-deriving application scored 81.2% (±5%) sensitivity, 97.1% (±1%) specificity, 88.8% (±2%) NPV, 95.0% (±1%) PPV, and 90.9% (±2%) accuracy. The handheld single-lead ECG device had 78.2% sensitivity, 95.5% specificity, 87.6% NPV, 91.5% PPV, and 88.9% accuracy. The same calculations were preformed after excluding regular atrial flutter measurements. On average, the pulse-deriving application scored 90.1% (±2%) sensitivity, 97.1% (±1%) specificity, 95.2% (±1%) NPV, 94.0% (±1%) PPV, and 94.8% (±1%) accuracy. The handheld single-lead ECG device had 90.2% sensitivity, 97.7% specificity, 97.7% NPV, 95.1% PPV, and 96.9% accuracy. Conclusion The diagnostic accuracy of the pulse-deriving smartphone application and the handheld single-lead ECG device was strongly influenced by the presence of regular atrial flutters, stressing the importance of further thorough validation. For the pulse-deriving smartphone application, there was no significant influence from device type in terms of diagnostic accuracy for the detection of AF. Insufficient quality measurements were more frequently performed on Android devices.

Introduction: Left atrial appendage (LAA) is a complex structure responsible for 90% of thrombus formation in patients with non-valvular atrial fibrillation (NVAF).In case of NVAF and contraindications to anticoagulation therapy, the percutaneous LAA closure is a treatment strategy to reduce the cardioembolic risk.This procedure is particularly difficult because of LAA anatomical and flow dynamics complexity and further related investigation would enhance the feasibility and efficacy of the intervention.Purpose: The aim of this study is to investigate the correlation between LAA morphology and corresponding flow dynamics for the planning of percutaneous LAA closure to prevent thromboembolic events.Methods: 4 CT dataset from patients scheduled for percutaneous LAA closure procedure have been segmented and processed through a specific tool to define the corresponding 3D anatomical model of left atrium, including LAA, pulmonary veins (PVs) and mitral valve (MV).A custom plug-in software was developed to measure the LAA volume variations during cardiac cycle directly from the 3D models.Computational fluid dynamics (CFD) simulations based on 4 patientspecific anatomies, each falling in one of the four LAA shape groups (chicken wing, cactus, windsock and cauliflower), were carried out to analyze the flow dynamics inside the LAA during the entire cardiac cycle.A time-dependent velocity profile was assigned on the 4 PVs as inlet condition, while the outlet condition imposed on the MV switched from closed to open configuration, to simulate systolic and diastolic ventricular phases, respectively.Results: CFD simulations' results showed that velocity strongly decrease from the ostium to the distal tip of the LAA during the entire cardiac cycle (Figure 1).This could suggest that the LAA site is more prone to fluid stagnation, and therefore to a higher risk of thrombus formation which may cause thromboembolic events.No significant differences were found between the 4 different LAA analyzed shapes, suggesting that all are equally suitable for thrombus formation.However a slight reduction of flow velocity (6.74%) was observed for cactus and cauliflower LAA shapes, according to the more complex geometry.Conclusions: This study showed the influence of both morphological and fluid dynamics aspects in the cardioembolic risk, pointing out the importance of insilico simulations to extrapolate data not available at clinical level.Further investigations should be conducted to better understanding the LAA related issues, in order to improve patient-specific modeling of percutaneous LAA closure procedure and other cardiovascular interventions.