مطالب پر بازدید
Validation of a smartphone-based event recorder for arrhythmia detection
This trial evaluated the diagnostic yield of KardiaMobile versus a 14-30 day external loop recorder (ELR). 38 patients were instructed to transmit ECGs via KardiaMobile and activate the ELR whenever they had symptoms. More patients had a potential diagnosis for their symptoms (i.e., at least one symptomatic recording during the entire monitoring period) with KardiaMobile than with the ELR (KardiaMobile= 34 (89.5%) vs ELR = 26 (68.4%); χ2 = 5.1, p = 0.024). In the per-protocol analysis, all 33 patients (100%) had a potential diagnosis using the KardiaMobile device, which was significantly higher compared to 24 patients (72.2%) using the ELR (χ2 = 10.4, p = 0.001). In summary, KardiaMobile is non-inferior to an ELR for detecting arrhythmias in the outpatient setting. The ease of use and portability of this device make it an attractive option for the detection of symptomatic arrhythmias.
Modified positioning of a smartphone based single-lead electrocardiogram device improves detection of atrial flutter
This study evaluated the use KardiaMobile in the lead II position (right hand to left leg) to improve visualization of flutter waves and clinician diagnosis of atrial flutter (AFL), compared to traditional lead-I tracings. Fifty patients were recruited (25 in sinus rhythm, 14 in AF, 11 in AFL). Lead-I AFL sensitivity was 27% for both electrophysiologists (EP), which individually improved to 73% and 55% in lead-II. KardiaMobile appropriately diagnosed lead-I AFL as unclassified in 18% of cases, compared to 55% in lead-II. Overall clinician agreement (AF, sinus rhythm and AFL) was modest utilizing lead-I position (EP1: κ=0.71, EP2: κ=0.73, p<0.001), which improved with lead-II tracings (EP1: κ=0.87, EP2: κ=0.83, both p<0.001). In summary, lead II position of KardiaMobile improves clinician diagnosis of atrial flutter.
Diagnostic accuracy of a smartphone-operated, single-lead electrocardiography device for detection of rhythm and conduction abnormalities in primary care.
In 10 Dutch general practices, KardiaMobile ECG and AF algorithm were compared with simultaneous 12-lead ECG. Three cardiologists reviewed ECG data from 214 patients (mean age 64.1 y, 54% male). The 12-lead ECG diagnosed AF/AFL, any rhythm abnormality, and any conduction abnormality (AV block, BBB, LAD, LAFB) in 23, 44, and 28 patients, respectively. KardiaMobile ECG as assessed by the 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 Instant Analysis algorithm identified 20 or 23 AF cases and incorrectly classified 4 cases of sinus rhythm as possible AF (sensitivity and specificity of 87.0% (95% CI, 66.4%-97.2%) and 97.9% (95% CI, 94.7%-99.4%)). KardiaMobile recordings 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%). For conduction abnormality, the 15 false negatives were comprised of first-degree AVB (n=6), LAFB (n=8), and RBBB (n=1); on the other hand, cardiologists were able to accurately identify BBB in 13 patients’ KardiaMobile ECGs. The authors concluded that in a primary care population, the KardiaMobile ECG recording showed excellent diagnostic accuracy for AF/AFL and good diagnostic accuracy for other rhythm abnormalities. The 1L-ECG device was less sensitive for left anterior fascicular block and first-degree AV block.
First real-world experience with mobile health telemonitoring in adult patients with congenital heart disease.
This study presents the feasibility of a remote patient monitoring program in the Netherlands for managing arrhythmia, heart failure (weight) and blood pressure in symptomatic adults with congenital heart disease (CHD); the program used KardiaPro for the receipt and transfer of KardiaMobile ECG data. ECGs were assessed daily by trained nurses, under supervision of a cardiologist. Patients (median age 45; 35% male) were contacted by the treating cardiologist to adjust therapy, for surveillance or in order to provide reassurance. From June 2017 to March 2018, 55 symptomatic adult CHD patients participated; mean follow-up was 3 months and adherence was 97%. There were qualitatively fewer emergency room visits and hospitalizations (3) versus historical record (19). Serial patient-reported outcome measure (PROM) questionnaires were available for 12 patients at baseline and six patients after 6 months and showed a nonsignificant change in quality of life during telemonitoring. Nearly 75% of the 176 KardiaMobile ECGs were sinus rhythm; two patients were diagnosed with a new arrhythmia. In summary, a remote patient monitoring program featuring KardiaMobile is feasible with high adherence.
Supraventricular tachycardia diagnosed by smartphone ECG.
This is a case report of paroxysmal supraventricular tachycardia, unable to be diagnosed through typical evaluation with an event monitor despite several years of symptoms. Here, the patient diagnosed himself through purchase of KardiaMobile, capturing an atypical atrioventricular node re-entrant tachycardia (AVNRT). He emailed his cardiologist the tracing, which eventually led to an electrophysiology study and successful ablation procedure.
Wide complex tachycardia recorded with a smartphone cardiac rhythm monitor.
This case report discusses the use of KardiaMobile to diagnose RVOT ventricular tachycardia in diagnosis of a 62-year old man experiencing frequent, sudden episodes of exertional near-syncope and syncope with monomorphic RVOT VT. KardiaMobile may improve diagnostic yield in patients with symptoms of palpitations, light-headedness, or near-syncope. However, the lack of adhesive electrodes and variable contact between the patient and the device can lead to superimposed noise and artifact that may, in some cases, obscure the correct electrocardiographic diagnosis. Further, the device records cardiac rhythms only upon proper activation.
Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
This paper describes the development of fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability). The network was created and tested on 8028 annotated ECGs acquired with the Kardia device. Less than 3% of the ECGs included in the database could not be used in this study due to high levels of noise. Performance of RSL_ANN was very good and very similar in all datasets, with AUC over 90%. The work shows the value of Kardia for providing high volumes of quality data to aid the development of advanced diagnostic algorithms for AF.