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.

The prevalence of IBD is still increasing. Burden of disease was significantly more severe, mainly in Crohn's patients, in the referral centre, highlighting the importance of population-based studies to accurately describe phenotype distribution and disease burden.

ObjectivesTo evaluate the performance of direct-to-consumer pulse oximeters under clinical conditions, with arterial blood gas measurement (SaO2) as reference standard.DesignCross-sectional, validation study.SettingIntensive care.ParticipantsAdult patients requiring SaO2-monitoring.InterventionsThe studied oximeters are top-selling in Europe/USA (AFAC FS10D, AGPTEK FS10C, ANAPULSE ANP 100, Cocobear, Contec CMS50D1, HYLOGY MD-H37, Mommed YM101, PRCMISEMED F4PRO, PULOX PO-200 and Zacurate Pro Series 500 DL). Directly after collection of a SaO2 blood sample, we obtained pulse oximeter readings (SpO2). SpO2-readings were performed in rotating order, blinded for SaO2 and completed <10 min after blood sample collection.Outcome measuresBias (SpO2–SaO2) mean, root mean square difference (ARMS), mean absolute error (MAE) and accuracy in identifying hypoxaemia (SaO2 ≤90%). As a clinical index test, we included a hospital-grade SpO2-monitor (Philips).ResultsIn 35 consecutive patients, we obtained 2258 SpO2-readings and 234 SaO2-samples. Mean bias ranged from −0.6 to −4.8. None of the pulse oximeters met ARMS ≤3%, the requirement set by International Organisation for Standardisation (ISO)-standards and required for Food and Drug Administration (FDA) 501(k)-clearance. The MAE ranged from 2.3 to 5.1, and five out of ten pulse oximeters met the requirement of ≤3%. For hypoxaemia, negative predictive values were 98%–99%. Positive predictive values ranged from 11% to 30%. Highest accuracy (95% CI) was found for Contec CMS50D1; 91% (86–94) and Zacurate Pro Series 500 DL; 90% (85–94). The hospital-grade SpO2-monitor had an ARMS of 3.0% and MAE of 1.9, and an accuracy of 95% (91%–97%).ConclusionTop-selling, direct-to-consumer pulse oximeters can accurately rule out hypoxaemia, but do not meet ISO-standards required for FDA-clearance

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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.

Background: Timely detection of atrial fibrillation (AF) is important because of its increased risk of thromboembolic 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.

ObjectiveTo determine the accuracy of QT measurement in a smartphone-operated, single-lead ECG (1L-ECG) device (AliveCor KardiaMobile 1L).DesignCross-sectional, within-patient diagnostic validation study.Setting/participantsPatients underwent a 12-lead ECG (12L-ECG) for any non-acute indication in primary care, April 2017–July 2018.InterventionSimultaneous 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.Results250 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.ConclusionSmartphone-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.

Background Timely detection of atrial fibrillation (AF) is important because of its increased risk of thrombo-embolic events. Single time point screening interventions for AF are hindered because of AF's often paroxysmal nature. Community based prevalence of paroxysmal AF (pAF), however, is scarcely investigated. To detect pAF, often a Holter for prolonged electrocardiographic monitoring is used. Holter monitoring, however, is less appropriate for screening purposes because of its high burden for asymptomatic patients. Intermittent screening using a single-lead electrocardiogram (1L-ECG) device can offer a less burdensome alternative. Purpose To determine A) the diagnostic yield of two week Holter monitoring in screening for pAF after a negative 12-lead (12L-)ECG, B) the diagnostic accuracy of an intermittently used 1L-ECG device, and C) how often an expert panel is able to accurately diagnose AF by visually assessing the 1L-ECG recordings of patients with ≥1 algorithm-positive 1L-ECG recording(s). We used the Holter monitor as reference standard. Methods Patients of 65 years and older participated in an opportunistic screening study for AF. They received three screening tests (pulse palpation, electronic sphygmomanometer and a 1L-ECG device both with built-in AF detection algorithm). A 12L-ECG was recorded whenever at least one screening test was positive, supplemented with 10% of patients with three negative tests. We invited patients with a negative 12L-ECG to use a 1L-ECG device thrice daily and perform Holter monitoring for two weeks. We report the prevalence of pAF found by Holter monitoring and calculate the diagnostic accuracy of the 1L-ECG device's built-in AF detection algorithm. All 1L-ECG recordings of patients with ≥1 positive algorithm result were subsequently visually assessed by an expert panel of three cardiologists. Results We included 270 patients, of whom four had pAF on a median of 8.5 days of Holter monitoring: prevalence 1.5% (95%-CI: 0.4–3.8%). 205 patients were simultaneously screened with 1L-ECG recordings and Holter monitoring. Sensitivity was 67% (95%-CI: 9–99%), specificity 69% (95%-CI: 62–75%), positive predictive value 3% (95%-CI: 1–7%) and negative predictive value 99% (95%-CI: 97–100%) for diagnosing pAF based on the 1L-ECG device's built-in AF detection algorithm. Out of 65 patients with ≥1 algorithm-positive 1L-ECG recording(s), seven were classified as pAF after visual assessment by the expert panel. Six of these were false-positive. In 24 cases the expert panel could not interpret the 1L-ECGs. Conclusion We found a low prevalence of pAF using Holter monitoring in elderly primary care patients, with a negative 12L-ECG. The diagnostic accuracy of the built-in AF detection algorithm of an intermittently used 1L-ECG device is limited with a high false-positive rate. Cardiologists' assessment of algorithm-positive intermittent 1L-ECGs did not sufficiently improve accuracy to make a reliable diagnosis. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This work was supported by ZonMw (The Netherlands Organisation for Health Research and Development)