Evaluation of general practitioners' single-lead electrocardiogram interpretation skills: a case-vignette study

BACKGROUND: Handheld single-lead electrocardiograms (1L-ECG) present a welcome addition to the diagnostic arsenal of general practitioners (GPs). However, little is known about GPs' 1L-ECG interpretation skills, and thus its reliability in real-world practice. OBJECTIVE: To determine the diagnostic accuracy of GPs in diagnosing atrial fibrillation or flutter (AF/Afl) based on 1L-ECGs, with and without the aid of automatic algorithm interpretation, as well as other relevant ECG abnormalities. METHODS: We invited 2239 Dutch GPs for an online case-vignette study. GPs were asked to interpret four 1L-ECGs, randomly drawn from a pool of 80 case-vignettes. These vignettes were obtained from a primary care study that used smartphone-operated 1L-ECG recordings using the AliveCor KardiaMobile. Interpretation of all 1L-ECGs by a panel of cardiologists was used as reference standard. RESULTS: A total of 457 (20.4%) GPs responded and interpreted a total of 1613 1L-ECGs. Sensitivity and specificity for AF/Afl (prevalence 13%) were 92.5% (95% CI: 82.5-97.0%) and 89.8% (95% CI: 85.5-92.9%), respectively. PPV and NPV for AF/Afl were 45.7% (95% CI: 22.4-70.9%) and 98.8% (95% CI: 97.1-99.5%), respectively. GP interpretation skills did not improve in case-vignettes where the outcome of automatic AF-detection algorithm was provided. In detecting any relevant ECG abnormality (prevalence 22%), sensitivity, specificity, PPV and NPV were 96.3% (95% CI: 92.8-98.2%), 68.8% (95% CI: 62.4-74.6%), 43.9% (95% CI: 27.7-61.5%) and 97.9% (95% CI: 94.9-99.1%), respectively. CONCLUSIONS: GPs can safely rule out cardiac arrhythmias with 1L-ECGs. However, whenever an abnormality is suspected, confirmation by an expert-reader is warranted

Usefulness, pitfalls and interpretation of handheld single‑lead electrocardiograms

Single‑lead electrocardiograms (1 L-ECGs) are increasingly used in (pre)clinical settings for the detection and monitoring of a range of rhythm and conduction disorders. In this short communication paper, we aim to provide an overview of the usefulness and potential pitfalls when implementing 1 L-ECGs into everyday clinical practice. Moreover, we provide recommendations for improving signal quality, as well as a systematic approach to the interpretation of 1 L-ECGs, which is somewhat different from standard 12‑lead ECGs. Clinicians can use our illustrations and checklist as guidance when recording and interpreting 1 L-ECGs

Diagnostic Accuracy of a Smartphone-Operated, Single-Lead Electrocardiography Device for Detection of Rhythm and Conduction Abnormalities in Primary Care

PURPOSE: To validate a smartphone-operated, single-lead electrocardiography (1L-ECG) device (AliveCor KardiaMobile) with an integrated algorithm for atrial fibrillation (AF) against 12-lead ECG (12L-ECG) in a primary care population. METHODS: We recruited consecutive patients who underwent 12L-ECG for any nonacute indication. Patients held a smartphone with connected 1L-ECG while local personnel simultaneously performed 12L-ECG. All 1L-ECG recordings were assessed by blinded cardiologists as well as by the smartphone-integrated algorithm. The study cardiologists also assessed all 12L-recordings in random order as the reference standard. We determined the diagnostic accuracy of the 1L-ECG in detecting AF or atrial flutter (AFL) as well as any rhythm abnormality and any conduction abnormality with the simultaneously performed 12L-ECG as the reference standard. RESULTS: We included 214 patients from 10 Dutch general practices. Mean ± SD age was 64.1 ± 14.7 years, and 53.7% of the patients were male. The 12L-ECG diagnosed AF/AFL, any rhythm abnormality, and any conduction abnormality in 23, 44, and 28 patients, respectively. The 1L-ECG as assessed by cardiologists had a sensitivity and specificity for AF/AFL of 100% (95% CI, 85.2%-100%) and 100% (95% CI, 98.1%-100%). The AF detection algorithm had a sensitivity and specificity of 87.0% (95% CI, 66.4%-97.2%) and 97.9% (95% CI, 94.7%-99.4%). The 1L-ECG as assessed by cardiologists had a sensitivity and specificity for any rhythm abnormality of 90.9% (95% CI, 78.3%-97.5%) and 93.5% (95% CI, 88.7%-96.7%) and for any conduction abnormality of 46.4% (95% CI, 27.5%-66.1%) and 100% (95% CI, 98.0%-100%). CONCLUSIONS: In a primary care population, a smartphone-operated, 1L-ECG device showed excellent diagnostic accuracy for AF/AFL and good diagnostic accuracy for other rhythm abnormalities. The 1L-ECG device was less sensitive for conduction abnormalities

Diagnostic Accuracy of a Smartphone-Operated, Single-Lead Electrocardiography Device for Detection of Rhythm and Conduction Abnormalities in Primary Care

PURPOSE: To validate a smartphone-operated, single-lead electrocardiography (1L-ECG) device (AliveCor KardiaMobile) with an integrated algorithm for atrial fibrillation (AF) against 12-lead ECG (12L-ECG) in a primary care population. METHODS: We recruited consecutive patients who underwent 12L-ECG for any nonacute indication. Patients held a smartphone with connected 1L-ECG while local personnel simultaneously performed 12L-ECG. All 1L-ECG recordings were assessed by blinded cardiologists as well as by the smartphone-integrated algorithm. The study cardiologists also assessed all 12L-recordings in random order as the reference standard. We determined the diagnostic accuracy of the 1L-ECG in detecting AF or atrial flutter (AFL) as well as any rhythm abnormality and any conduction abnormality with the simultaneously performed 12L-ECG as the reference standard. RESULTS: We included 214 patients from 10 Dutch general practices. Mean ± SD age was 64.1 ± 14.7 years, and 53.7% of the patients were male. The 12L-ECG diagnosed AF/AFL, any rhythm abnormality, and any conduction abnormality in 23, 44, and 28 patients, respectively. The 1L-ECG as assessed by cardiologists had a sensitivity and specificity for AF/AFL of 100% (95% CI, 85.2%-100%) and 100% (95% CI, 98.1%-100%). The AF detection algorithm had a sensitivity and specificity of 87.0% (95% CI, 66.4%-97.2%) and 97.9% (95% CI, 94.7%-99.4%). The 1L-ECG as assessed by cardiologists had a sensitivity and specificity for any rhythm abnormality of 90.9% (95% CI, 78.3%-97.5%) and 93.5% (95% CI, 88.7%-96.7%) and for any conduction abnormality of 46.4% (95% CI, 27.5%-66.1%) and 100% (95% CI, 98.0%-100%). CONCLUSIONS: In a primary care population, a smartphone-operated, 1L-ECG device showed excellent diagnostic accuracy for AF/AFL and good diagnostic accuracy for other rhythm abnormalities. The 1L-ECG device was less sensitive for conduction abnormalities

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

Objective To determine the accuracy of QT measurement in a smartphone-operated, single-lead ECG (1L-ECG) device (AliveCor KardiaMobile 1L). Design Cross-sectional, within-patient diagnostic validation study. Setting/participants Patients underwent a 12-lead ECG (12L-ECG) for any non-acute indication in primary care, April 2017-July 2018. Intervention Simultaneous recording of 1L-ECGs and 12L-ECGs with blinded manual QT assessment. Outcomes of interest (1) Difference in QT interval in milliseconds (ms) between the devices; (2) measurement agreement between the devices (excellent agreement <20 ms and clinically acceptable agreement <40 ms absolute difference); (3) sensitivity and specificity for detection of extreme QTc (short (≤340 ms) or long (≥480 ms)), on 1L-ECGs versus 12L-ECGs as reference standard. In case of significant discrepancy between lead I/II of 12L-ECGs and 1L-ECGs, we developed a correction tool by adding the difference between QT measurements of 12L-ECG and 1L-ECGs. Results 250 ECGs of 125 patients were included. The mean QTc interval, using Bazett's formula (QTcB), was 393±25 ms (mean±SD) in 1L-ECGs and 392±27 ms in lead I of 12L-ECGs, a mean difference of 1±21 ms, which was not statistically different (paired t-test (p=0.51) and Bland Altman method (p=0.23)). In terms of agreement between 1L-ECGs and lead I, QTcB had excellent agreement in 66.9% and clinically acceptable agreement in 93.4% of observations. The sensitivity and specificity of detecting extreme QTc were 0% and 99.2%, respectively. The comparison of 1L-ECG QTcB with lead II of 12L-ECGs showed a significant difference (p=<0.01), but when using a correction factor (+9 ms) this difference was cancelled (paired t-test (p=0.43) or Bland Altman test (p=0.57)). Moreover, it led to improved rates of excellent (71.3%) and clinically acceptable (94.3%) agreement. Conclusion Smartphone-operated 1L-ECGs can be used to accurately measure the QTc interval compared with simultaneously obtained 12L-ECGs in a primary care population. This may provide an opportunity for monitoring the effects of potential QTc-prolonging medications

Screening for paroxysmal atrial fibrillation in primary care using Holter monitoring and intermittent, ambulatory single-lead electrocardiography

Background: Timely detection of atrial fibrillation (AF) is important because of its increased risk of thrombo-embolic events. Single time point screening interventions fall short in detection of paroxysmal AF, which requires prolonged electrocardiographic monitoring, usually using a Holter. However, traditional 24-48 h Holter monitoring is less appropriate for screening purposes because of its low diagnostic yield. Intermittent, ambulatory screening using a single-lead electrocardiogram (1 L-ECG) device can offer a more efficient alternative. Methods: Primary care patients of ≥65 years participated in an opportunistic screening study for AF. We invited patients with a negative 12 L-ECG to wear a Holter monitor for two weeks and to use a MyDiagnostick 1 L-ECG device thrice daily. We report the yield of paroxysmal AF found by Holter monitoring and calculate the diagnostic accuracy of the 1 L-ECG device's built-in AF detection algorithm with the Holter monitor as reference standard. Results: We included 270 patients, of whom four had AF in a median of 8.0 days of Holter monitoring, a diagnostic yield of 1.5% (95%-CI: 0.4–3.8%). In 205 patients we performed simultaneous 1 L-ECG screening. For diagnosing AF based on the 1 L-ECG device's AF detection algorithm, sensitivity was 66.7% (95%-CI: 9.4–99.2%), specificity 68.8% (95%-CI: 61.9–75.1%), positive predictive value 3.1% (95%-CI: 1.4–6.8%) and negative predictive value 99.3% (95%-CI: 96.6–99.9%). Conclusion: We found a low diagnostic yield of paroxysmal AF using Holter monitoring in elderly primary care patients with a negative 12 L-ECG. The diagnostic accuracy of an intermittently, ambulatory used MyDiagnostick 1 L-ECG device as interpreted by its built-in AF detection algorithm is limited

Screening for paroxysmal atrial fibrillation in primary care using Holter monitoring and intermittent, ambulatory single-lead electrocardiography

BACKGROUND: Timely detection of atrial fibrillation (AF) is important because of its increased risk of thrombo-embolic events. Single time point screening interventions fall short in detection of paroxysmal AF, which requires prolonged electrocardiographic monitoring, usually using a Holter. However, traditional 24-48 h Holter monitoring is less appropriate for screening purposes because of its low diagnostic yield. Intermittent, ambulatory screening using a single-lead electrocardiogram (1 L-ECG) device can offer a more efficient alternative. METHODS: Primary care patients of ≥65 years participated in an opportunistic screening study for AF. We invited patients with a negative 12 L-ECG to wear a Holter monitor for two weeks and to use a MyDiagnostick 1 L-ECG device thrice daily. We report the yield of paroxysmal AF found by Holter monitoring and calculate the diagnostic accuracy of the 1 L-ECG device's built-in AF detection algorithm with the Holter monitor as reference standard. RESULTS: We included 270 patients, of whom four had AF in a median of 8.0 days of Holter monitoring, a diagnostic yield of 1.5% (95%-CI: 0.4-3.8%). In 205 patients we performed simultaneous 1 L-ECG screening. For diagnosing AF based on the 1 L-ECG device's AF detection algorithm, sensitivity was 66.7% (95%-CI: 9.4-99.2%), specificity 68.8% (95%-CI: 61.9-75.1%), positive predictive value 3.1% (95%-CI: 1.4-6.8%) and negative predictive value 99.3% (95%-CI: 96.6-99.9%). CONCLUSION: We found a low diagnostic yield of paroxysmal AF using Holter monitoring in elderly primary care patients with a negative 12 L-ECG. The diagnostic accuracy of an intermittently, ambulatory used MyDiagnostick 1 L-ECG device as interpreted by its built-in AF detection algorithm is limited