Promises and Perils of Consumer Mobile Technologies in Cardiovascular Care: JACC Scientific Statement

Direct-to-consumer (D2C) wearables are becoming increasingly popular in cardiovascular health management because of their affordability and capability to capture diverse health data. Wearables may enable continuous health care provider-patient partnerships and reduce the volume of episodic clinic-based care (thereby reducing health care costs). However, challenges arise from the unregulated use of these devices, including questionable data reliability, potential misinterpretation of information, unintended psychological impacts, and an influx of clinically nonactionable data that may overburden the health care system. Further, these technologies could exacerbate, rather than mitigate, health disparities. Experience with wearables in atrial fibrillation underscores these challenges. The prevalent use of D2C wearables necessitates a collaborative approach among stakeholders to ensure effective integration into cardiovascular care. Wearables are heralding innovative disease screening, diagnosis, and management paradigms, expanding therapeutic avenues, and anchoring personalized medicine.

Elevated fibrosis burden as assessed by MRI predicts cryoballoon ablation failure

INTRODUCTION

Late-gadolinium enhancement magnetic resonance (LGE-MRI) imaging is increasingly used in management of atrial fibrillation (AFib) patients. Here, we assess the usefulness of LGE-MRI-based fibrosis quantification to predict arrhythmia recurrence in patients undergoing cryoballoon ablation. Our secondary goal was to compare two widely used fibrosis quantification methods.

METHODS

In 102 AF patients undergoing LGE-MRI and cryoballoon ablation (mean age 62 years; 64% male; 59% paroxysmal AFib), atrial fibrosis was quantified using the pixel intensity histogram (PIH) and image intensity ratio (IIR) methods. PIH segmentations were completed by a third-party provider as part of the standard of care at our hospital; Image intensity ratio (IIR) segmentations of the same scans were carried out in our lab using a commercially available software package. Fibrosis burdens and spatial distributions for the two methods were compared. Patients were followed prospectively for recurrent arrhythmia following ablation.

RESULTS

Average PIH fibrosis was 15.6 ± 5.8% of the left atrial (LA) volume. Depending on threshold (IIRthr ), the average IIR fibrosis (% of LA wall surface area) ranged from 5.0 ± 7.2% (IIRthr  = 1.2) to 37.4 ± 10.9% (IIRthr  = 0.97). An IIRthr of 1.03 demonstrated the greatest agreement between the methods, but spatial overlap of fibrotic areas delineated by the two methods was modest (Sorenson Dice coefficient: 0.49). Fourty-two patients (41.2%) had recurrent arrhythmia. PIH fibrosis successfully predicted recurrence (HR 1.07; p = .02) over a follow-up period of 362 ± 149 days; regardless of IIRthr , IIR fibrosis did not predict recurrence.

CONCLUSIONS

PIH-based volumetric assessment of atrial fibrosis was modestly predictive of arrhythmia recurrence following cryoballoon ablation in this cohort. IIR-based fibrosis was not predictive of recurrence for any of the IIRthr values tested, and the overlap in designated areas of fibrosis between the PIH and IIR methods was modest. Caution must therefore be exercised when interpreting LA fibrosis from LGE-MRI, since the values and spatial pattern are methodology-dependent.

Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation

Background

Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored.

Objective

The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data.

Methods

We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMobile 6L device. We tested our model on sinus rhythm mECGs within ±0-2 days, ±3-7 days, and ±8-30 days from AF events to determine the optimal screening window. Finally, we tested our model on mECGs from before an AF event to determine whether AF can be predicted prospectively.

Results

We included 73,861 users with 267,614 mECGs (mean age 58.14 years; 35% women). Users with paroxysmal AF contributed 60.15% of mECGs. Model performance on the test set comprising control and study samples across all windows of interest showed an area under the curve (AUC) score of 0.760 (95% confidence interval [CI] 0.759-0.760), sensitivity of 0.703 (95% CI 0.700-0.705), specificity of 0.684 (95% CI 0.678-0.685), and accuracy of 69.4% (95% CI 0.692-0.700). Model performance was better on ±0-2 day samples (sensitivity 0.711; 95% CI 0.709-0.713) and worse on the ±8-30 day window (sensitivity 0.688; 95% CI 0.685-0.690), with performance on the ±3-7 day window falling in between (sensitivity 0.708; 95% CI 0.704-0.710).

Conclusion

Neural networks can predict AF using a widely scalable and cost-effective mobile technology prospectively and retrospectively.

Catheter ablation of atrial fibrillation results in significant QTc prolongation in the postoperative period

Background

The corrected QT interval (QTc) is a measure of ventricular repolarization time, and a prolonged QTc increases risk for malignant ventricular arrhythmias. Pulmonary vein isolation (PVI) may increase QTc but its effects have not been well studied.

Objective

Determine the incidence, risk factors, and outcomes of patients presenting for PVI in sinus and atrial fibrillation with postoperative QTc prolongation in a large cohort.

Methods

We performed a single-center retrospective study of consecutive atrial fibrillation ablations. QTc durations using Bazett correction were obtained from electrocardiograms at different postoperative intervals and compared to preoperative QTc. We studied clinical outcomes including clinically significant ventricular arrhythmia and death. A multivariable model was used to identify factors associated with clinically significant QTc prolongation, defined as ΔQTc ≥60 ms or new QTc duration ≥500 ms.

Results

A total of 352 PVIs were included in this study. We observed a statistically significant increase in mean QTc compared to baseline (446.3 ± 37.8 ms) on postoperative day (POD)0 (471.7 ± 38.2 ms, P < .001) and at POD1 (456.5 ± 35.0 ms, P < .001). There was no significant difference at 1 month (452.4 ± 33.5 ms, P = .39) and 3 months (447.3 ± 40.0 ms, P = .78). Sixty-six patients (19.2%) developed ΔQTc ≥60 ms or QTc ≥500 ms on POD0, with 4.1% persisting past 90 days. Female sex (odds ratio [OR] = 1.82, 95% confidence interval [CI] =1.01–3.29, P = .047) and history of coronary artery disease (OR = 2.16, 95% CI = 1.03–4.55, P = .042) were independently predictive of QTc prolongation ≥500 ms or ΔQTc ≥60 ms. There were no episodes of clinically significant ventricular arrhythmia or death attributable to arrhythmia.

Conclusion

QTc duration increased significantly immediately post-PVI and returned to baseline by 1 month. PVI did not provoke significant ventricular arrhythmias in our cohort.

Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram

OBJECTIVE

To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).

METHODS

A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site.

RESULTS

The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%.

CONCLUSION

Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.

Using smart speakers to contactlessly monitor heart rhythms

Heart rhythm assessment is indispensable in diagnosis and management of many cardiac conditions and to study heart rate variability in healthy individuals. We present a proof-of-concept system for acquiring individual heart beats using smart speakers in a fully contact-free manner. Our algorithms transform the smart speaker into a short-range active sonar system and measure heart rate and inter-beat intervals (R-R intervals) for both regular and irregular rhythms. The smart speaker emits inaudible 18–22 kHz sound and receives echoes reflected from the human body that encode sub-mm displacements due to heart beats. We conducted a clinical study with both healthy participants and hospitalized cardiac patients with diverse structural and arrhythmic cardiac abnormalities including atrial fibrillation, flutter and congestive heart failure. Compared to electrocardiogram (ECG) data, our system computed R-R intervals for healthy participants with a median error of 28 ms over 12,280 heart beats and a correlation coefficient of 0.929. For hospitalized cardiac patients, the median error was 30 ms over 5639 heart beats with a correlation coefficient of 0.901. The increasing adoption of smart speakers in hospitals and homes may provide a means to realize the potential of our non-contact cardiac rhythm monitoring system for monitoring of contagious or quarantined patients, skin sensitive patients and in telemedicine settings.

Anran Wang et al. present a contact-free method of monitoring individual heart beats by converting smart-speakers into active sonar systems. Their approach is capable of measuring heart rhythms with high accuracy in both healthy participants and hospitalized patients, and may be a useful healthcare tool for remote diagnosis or patient monitoring.

Hydroxychloroquine with or without azithromycin for treatment of early SARS-CoV-2 infection among high-risk outpatient adults: A randomized clinical trial

BACKGROUND

Treatment options for outpatients with COVID-19 could reduce morbidity and prevent SARS-CoV-2 transmission.

METHODS

In this randomized, double-blind, three-arm (1:1:1) placebo-equivalent controlled trial conducted remotely throughout the United States, adult outpatients with laboratory-confirmed SARS-CoV-2 infection were recruited. Participants were randomly assigned to receive hydroxychloroquine (HCQ) (400 mg BID x1day, followed by 200 mg BID x9days) with or without azithromycin (AZ) (500 mg, then 250 mg daily x4days) or placebo-equivalent (ascorbic acid (HCQ) and folic acid (AZ)), stratified by risk for progression to severe COVID-19 (high-risk vs. low-risk). Self-collected nasal swabs for SARS-CoV-2 PCR, FLUPro symptom surveys, EKGs and vital signs were collected daily. Primary endpoints were: (a) 14-day progression to lower respiratory tract infection (LRTI), 28-day COVID-19 related hospitalization, or death; (b) 14-day time to viral clearance; secondary endpoints included time to symptom resolution (ClinicalTrials.gov: NCT04354428). Due to the low rate of clinical outcomes, the study was terminated for operational futility.

FINDINGS

Between 15th April and 27th July 2020, 231 participants were enrolled and 219 initiated medication a median of 5.9 days after symptom onset. Among 129 high-risk participants, incident LRTI occurred in six (4.7%) participants (two control, four HCQ/AZ) and COVID-19 related hospitalization in seven (5.4%) (four control, one HCQ, two HCQ/AZ); no LRTI and two (2%) hospitalizations occurred in the 102 low-risk participants (one HCQ, one HCQ/AZ). There were no deaths. Among 152 participants with viral shedding at enrollment, median time to clearance was 5 days (95% CI=4-6) in HCQ, 6 days (95% CI=4-8) in HCQ/AZ, and 8 days (95% CI=6-10) in control. Viral clearance was faster in HCQ (HR=1.62, 95% CI=1.01-2.60, p = 0.047) but not HCQ/AZ (HR=1.25, p = 0.39) compared to control. Among 197 participants who met the COVID-19 definition at enrollment, time to symptom resolution did not differ by group (HCQ: HR=1.02, 95% CI-0.63-1.64, p = 0.95, HCQ/AZ: HR=0.91, 95% CI=0.57-1.45, p = 0.70).

INTERPRETATION

Neither HCQ nor HCQ/AZ shortened the clinical course of outpatients with COVID-19, and HCQ, but not HCQ/AZ, had only a modest effect on SARS-CoV-2 viral shedding. HCQ and HCQ/AZ are not effective therapies for outpatient treatment of SARV-CoV-2 infection.

FUNDING

The COVID-19 Early Treatment Study was funded by the Bill & Melinda Gates Foundation (INV-017062) through the COVID-19 Therapeutics Accelerator. University of Washington Institute of Translational Health Science (ITHS) grant support (UL1 TR002319), KL2 TR002317, and TL1 TR002318 from NCATS/NIH funded REDCap. The content is solely the responsibility of the authors and does not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated. PAN and MJA were supported by the Mayo Clinic Windland Smith Rice Comprehensive Sudden Cardiac Death Program.Trial registration ClinicalTrials.gov number NCT04354428.

Dynamic voltage threshold adjusted substrate modification technique for complex atypical atrial flutters with varying circuits

BACKGROUND

Atypical atrial flutter (AFL) is common in patients with postsurgical atrial scar, with macro- or microscopic channels in the scar acting as substrate for reentry. Heterogeneous atrial scarring can cause varying flutter circuits, which makes mapping and ablation challenging, and recurrences common.

AIM

We hypothesize that dynamically adjusting voltage thresholds can identify heterogeneous atrial scarring, which can then be effectively homogenized to eliminate atypical AFLs.

METHODS

We studied consecutive patients who presented to Electrophysiology laboratory for atypical AFL ablation with history of atriotomy and included the patients with multiple, varying flutter circuits during mapping in our study. We excluded patients with stable flutter circuit that was sustained and could be localized using traditional entrainment and activation mapping strategy. In the included patients, we performed detailed high-density voltage map of the atrium of interest. We adjusted voltage thresholds as needed to identify heterogeneity and channels in the scarred regions. A thorough scar homogenization was performed with irrigated smart-touch ablation catheter. Re-inducibility of tachycardia, and immediate and long-term outcomes were studied.

RESULTS

Of five studied cases, one was female; age 66 ± 10 years. All five had prior surgical substrate. All the patients had multiple flutter morphologies, which varied as we mapped the AFL. After scar homogenization, tachycardia was not inducible in any patient. No recurrence of flutter was noted during a mean follow-up duration of 450 ± 27 days.

CONCLUSION

High-density voltage mapping and homogenization of the scar can be an effective strategy in eliminating complex scar-mediated atypical AFL with multiple circuits.

QT interval and arrhythmic safety of hydroxychloroquine monotherapy in coronavirus disease 2019

BACKGROUND

Observational studies have suggested increased arrhythmic and cardiovascular risk with the combination use of hydroxychloroquine (HCQ) and azithromycin in patients with coronavirus disease 2019 (COVID-19).

OBJECTIVE

The arrhythmic safety profile of HCQ monotherapy, which remains under investigation as a therapeutic and prophylactic agent in COVID-19, is less established and we sought to evaluate this.

METHODS

In 245 consecutive patients with COVID-19 admitted to the University of Washington hospital system between March 9, 2020, and May 10, 2020, we identified 111 treated with HCQ monotherapy. Patients treated with HCQ underwent a systematic arrhythmia and QT interval surveillance protocol including serial electrocardiograms (ECG) (baseline, following second HCQ dose). The primary endpoint was in-hospital sustained ventricular arrhythmia or arrhythmic cardiac arrest. Secondary endpoints included clinically significant QTc prolongation.

RESULTS

A total of 111 patients with COVID-19 underwent treatment with HCQ monotherapy (mean age 62 ± 16 years, 44 women [39%], serum creatinine 0.9 [interquartile range 0.4] mg/dL). There were no instances of sustained ventricular arrythmia or arrhythmic cardiac arrest. In 75 patients with serial ECGs, clinically significant corrected QT (QTc) prolongation was observed in a minority (n = 5 [7%]). In patients with serial ECGs, there was no significant change in the QTc interval in prespecified subgroups of interest, including those with prevalent cardiovascular disease or baseline use of renin-angiotensin-aldosterone axis inhibitors.

CONCLUSIONS

In the context of a systematic monitoring protocol, HCQ monotherapy in hospitalized COVID-19 patients was not associated with malignant ventricular arrhythmia. A minority of patients demonstrated clinically significant QTc prolongation during HCQ therapy.