Manual QT interval measurement with a smartphone-operated single-lead ECG versus 12-lead ECG: a within-patient diagnostic validation study in primary care

Utility and acceptability of remote 6-lead electrocardiographic monitoring in children with inherited cardiac conditions

OBJECTIVE

This pilot study sought to investigate the utility and acceptability of the KardiaMobile 6-lead ECG (KM6LECG) as a tool for remote monitoring in children with inherited cardiac conditions.

DESIGN

A single-centre prospective cohort study. Children underwent standard clinical evaluation including a 12-lead ECG and a KM6LECG in the clinic. Participants recorded KM6LECGs monthly at home for 3 months. Families completed a questionnaire on their experience.

SETTING

Great Ormond Street Hospital Centre for Inherited Cardiovascular Diseases.

PARTICIPANTS

64 children: 22 with hypertrophic cardiomyopathy (HCM); 22 with long QT syndrome and 20 unaffected siblings (controls).

MAIN OUTCOME MEASURES

Comparison of data extracted from the clinic 12-lead ECG and supervised KM6LECG, and the supervised and unsupervised KM6LECG recording.

RESULTS

Of 64 children (35% female, mean age 12 years), 58 had a baseline 12-lead ECG and appropriate baseline KM6LECG. In children with HCM, abnormalities in ventricular depolarisation/repolarisation in the limb leads of the 12-lead ECG were reliably reproduced. From the whole cohort, there was a strong positive correlation between the corrected QT interval from the 12-lead ECG and baseline KM6LECG (intraclass correlation coefficient=0.839) and baseline KM6LECG with an unsupervised KM6LECG (intraclass correlation coefficient=0.736). Suspected 'lead' misplacement impacted 18% of unsupervised recordings. Overall, the acceptability of the KM6LECG to families was good.

CONCLUSIONS

The KM6LECG provides an accurate tool for assessing some ECG abnormalities associated with paediatric inherited cardiovascular disease and may provide a useful at-home adjunct to face-to-face clinical care of children requiring ECG assessment.

Diagnostic Accuracy of Single-Lead Electrocardiograms Using the Kardia Mobile App and the Apple Watch 4: Validation Study

BACKGROUND

To date, the 12-lead electrocardiogram (ECG) is the gold standard for cardiological diagnosis in clinical settings. With the advancements in technology, a growing number of smartphone apps and gadgets for recording, visualizing, and evaluating physical performance as well as health data is available. Although this new smart technology is innovative and time- and cost-efficient, less is known about its diagnostic accuracy and reliability.

OBJECTIVE

This study aimed to examine the agreement between the mobile single-lead ECG measurements of the Kardia Mobile App and the Apple Watch 4 compared to the 12-lead gold standard ECG in healthy adults under laboratory conditions. Furthermore, it assessed whether the measurement error of the devices increases with an increasing heart rate.

METHODS

This study was designed as a prospective quasi-experimental 1-sample measurement, in which no randomization of the sampling was carried out. In total, ECGs at rest from 81 participants (average age 24.89, SD 8.58 years; n=58, 72% male) were recorded and statistically analyzed. Bland-Altman plots were created to graphically illustrate measurement differences. To analyze the agreement between the single-lead ECGs and the 12-lead ECG, Pearson correlation coefficient (r) and Lin concordance correlation coefficient (CCCLin) were calculated.

RESULTS

The results showed a higher agreement for the Apple Watch (mean deviation QT: 6.85%; QT interval corrected for heart rate using Fridericia formula [QTcF]: 7.43%) than Kardia Mobile (mean deviation QT: 9.53%; QTcF: 9.78%) even if both tend to underestimate QT and QTcF intervals. For Kardia Mobile, the QT and QTcF intervals correlated significantly with the gold standard (rQT=0.857 and rQTcF=0.727; P<.001). CCCLin corresponded to an almost complete heuristic agreement for the QT interval (0.835), whereas the QTcF interval was in the range of strong agreement (0.682). Further, for the Apple Watch, Pearson correlations were highly significant and in the range of a large effect (rQT=0.793 and rQTcF=0.649; P<.001). CCCLin corresponded to a strong heuristic agreement for both the QT (0.779) and QTcF (0.615) intervals. A small negative correlation between the measurement error and increasing heart rate could be found of each the devices and the reference.

CONCLUSIONS

Smart technology seems to be a promising and reliable approach for nonclinical health monitoring. Further research is needed to broaden the evidence regarding its validity and usability in different target groups.

Remote monitoring technologies for measuring cardiovascular functions in community-dwelling adults: a systematic review

Remote monitoring technologies (RMTs) allow continuous, unobtrusive, and real-time monitoring of the cardiovascular system. An overview of existing RMTs measuring cardiovascular physiological variables is lacking. This systematic review aimed to describe RMTs measuring cardiovascular functions in community-dwelling adults. An electronic search was conducted via PubMed, EMBASE, and Cochrane Library from January 1, 2020, to April 7, 2022. Articles reporting on non-invasive RMTs used unsupervised in community-dwelling adults were included. Reviews and studies in institutionalized populations were excluded. Two reviewers independently assessed the studies and extracted the technologies used, cardiovascular variables measured, and wearing locations of RMTs. Validation of the RMTs was examined based on the COSMIN tool, and accuracy and precision were presented. This systematic review was registered with PROSPERO (CRD42022320082). A total of 272 articles were included representing 322,886 individuals with a mean or median age from 19.0 to 88.9 years (48.7% female). Of all 335 reported RMTs containing 216 distinct devices, photoplethysmography was used in 50.3% of RMTs. Heart rate was measured in 47.0% of measurements, and the RMT was worn on the wrist in 41.8% of devices. Nine devices were reported in more than three articles, of which all were sufficiently accurate, six were sufficiently precise, and four were commercially available in December 2022. The top four most reported technologies were AliveCor KardiaMobile®, Fitbit Charge 2, and Polar H7 and H10 Heart Rate Sensors. With over 200 distinct RMTs reported, this review provides healthcare professionals and researchers an overview of available RMTs for monitoring the cardiovascular system.

Comparison of AliveCor KardiaMobile Six-Lead ECG with Standard ECG in Pediatric Patients

The AliveCor KardiaMobile (ACKM) is a remote electrocardiogram (ECG) monitoring device. Little research has been conducted on its accuracy with pediatric patients. This prospective study aims to compare the ACKM six-lead device with a standard fifteen-lead ECG in measuring the QTc, QRS, and axis in pediatric patients. Pediatric patients ages 5 to 21 years were enrolled prospectively to have their ECG recorded using an ACKM six-lead device following a recording with the standard 15-lead ECG. A pediatric electrophysiologist measured the QTc, QRS interval, and QRS axis for both ECGs. Bland-Altman analysis was performed to assess agreement among measurements. The study included 141 patients. The mean age was 12.3 ± 4.4 years. Average heart rate was 79 ± 16 bpm. The mean difference in the QTc measurements for a paired standard ECG and ACKM was - 0.6 ms [95% confidence interval - 48 to 47 ms]. Of the ACKM QTc measurements, 117 (83%) were within 30 ms of the standard ECG. The mean difference in paired QRS measurements was - 1.3 ms [95% confidence interval - 23 to 21 ms]. Of the ACKM QRS measurements, 134 (95%) were within 20 ms of the standard ECG. The measured axis was the same for 84% of ACKM and standard ECGs. Over 80% of the ACKM six-lead ECGs produced QTc, QRS, and axis deviation measurements within a clinically useful range of the standard ECG. However, it is not accurate enough to be used consistently in place of a standard ECG for QTc and QRS measurement for pediatric patients.

Comparison Between QT and Corrected QT Interval Assessment by an Apple Watch With the AccurBeat Platform and by a 12‑Lead Electrocardiogram With Manual Annotation: Prospective Observational Study

Background

Abnormal prolongation or shortening of the QT interval is associated with increased risk for ventricular arrhythmias and sudden cardiac death. For continuous monitoring, widespread use, and prevention of cardiac events, advanced wearable technologies are emerging as promising surrogates for conventional 12‑lead electrocardiogram (ECG) QT interval assessment. Previous studies have shown a good agreement between QT and corrected QT (QTc) intervals measured on a smartwatch ECG and a 12-lead ECG, but the clinical accuracy of computerized algorithms for QT and QTc interval measurement from smartwatch ECGs is unclear.

Objective

The prospective observational study compared the smartwatch-recorded QT and QTc assessed using AccurKardia’s AccurBeat platform with the conventional 12‑lead ECG annotated manually by a cardiologist.

Methods

ECGs were collected from healthy participants (without any known cardiovascular disease) aged >22 years. Two consecutive 30-second ECG readings followed by (within 15 minutes) a 10-second standard 12-lead ECG were recorded for each participant. Characteristics of the participants were compared by sex using a 2-sample t test and Wilcoxon rank sum test. Statistical comparisons of heart rate (HR), QT interval, and QTc interval between the platform and the 12-lead ECG, ECG lead I, and ECG lead II were done using the Wilcoxon sign rank test. Linear regression was used to predict QTc and QT intervals from the ECG based on the platform’s QTc/QT intervals with adjustment for age, sex, and difference in HR measurement. The Bland-Altman method was used to check agreement between various QT and QTc interval measurements.

Results

A total of 50 participants (32 female, mean age 46 years, SD 1 year) were included in the study. The result of the regression model using the platform measurements to predict the 12-lead ECG measurements indicated that, in univariate analysis, QT/QTc intervals from the platform significantly predicted QT/QTc intervals from the 12-lead ECG, ECG lead I, and ECG lead II, and this remained significant after adjustment for sex, age, and change in HR. The Bland-Altman plot results found that 96% of the average QTc interval measurements between the platform and QTc intervals from the 12-lead ECG were within the 95% confidence limit of the average difference between the two measurements, with a mean difference of –10.5 (95% limits of agreement –71.43, 50.43). A total of 94% of the average QT interval measurements between the platform and the 12-lead ECG were within the 95% CI of the average difference between the two measurements, with a mean difference of –6.3 (95% limits of agreement –54.54, 41.94).

Conclusions

QT and QTc intervals obtained by a smartwatch coupled with the platform’s assessment were comparable to those from a 12-lead ECG. Accordingly, with further refinements, remote monitoring using this technology holds promise for the identification of QT interval prolongation.