Self-monitoring for atrial fibrillation recurrence in the discharge period post-cardiac surgery using an iPhone electrocardiogram

Lowres, Nicole; Mulcahy, Georgina; Gallagher, Robyn; Freedman, Ben; Marshman, David; Kirkness, Ann; Orchard, Jessica; Neubeck, Lis

doi:10.1093/ejcts/ezv486

Cited by 60 publications

(57 citation statements)

References 23 publications

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“…The good diagnostic properties that we found for the 1L-ECG device for AF/AFL, when assessed by cardiologists or by the smartphone application algorithm, coincide with a number of prior studies 10,11,19,20,[22][23][24][25][26][27] (see Supplemental Table 3, and Supplemental Table 4, http://www.AnnFamMed.org/content/17/5/403/suppl/ DC1/ for an overview of prior studies that validated the KardiaMobile 1L-ECG device for rhythm and/or conduction abnormalities). Notable exceptions are 2 studies by Chan et al 8,28 and 1 study by Desteghe et al 29 that reported sensitivities of 71.4%, 66.7%, and 65.9%, respectively, for the KardiaMobile algorithm to detect AF.…”

Section: Previous Worksupporting

confidence: 82%

“…Although a number of studies have assessed the presence of ectopic beats on 1L-ECG recordings, none have validated 1L-ECG for ectopy alone or as part of a composite outcome. 8,10,11,[22][23][24]28,29 One study validated 1L-ECG against 12L-ECG for conduction abnormalities. That study, by Haberman et al, 19 found high specificity but sensitivities of 77.3% and 72.4%, respectively, for AV block and BBB.…”

Section: Previous Workmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Previous Worksupporting

confidence: 82%

Section: Previous Workmentioning

confidence: 99%

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

et al. 2019

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“…Overall, the results from initial and pilot studies are encouraging in terms of increasing the detection of AF cases through mobile ECG devices [63,[382][383][384][385]; optimising the use of antithrombotic therapy for stroke prevention in AF via computerised decision support [386][387][388]; improving the time in therapeutic range for patients using warfarin via point-of-care monitoring of anticoagulation; increasing adherence to cardiovascular pharmacotherapy via smartphone text messaging [389][390][391]; and increasing access to information for patients and their carers [392][393][394][395].…”

Section: Ehealth To Support Atrial Fibrillation Managementmentioning

confidence: 99%

National Heart Foundation of Australia and the Cardiac Society of Australia and New Zealand: Australian Clinical Guidelines for the Diagnosis and Management of Atrial Fibrillation 2018

Brieger

Amerena

Attia

et al. 2018

Heart, Lung and Circulation

236

235

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“…Since many AF episodes can occur asymptomatically, data on long term monitoring (Holter ECG), either on drug therapy or not, may be able to partially identify the real AF burden. A critical point is the intensity of ECG recording during follow-up [123].…”

Section: Long Term Implications Of Pafmentioning

confidence: 99%

Incidence, Management, Prevention and Outcome of Post-Operative Atrial Fibrillation in Thoracic Surgical Oncology

Fabiani

Colombo

Bacchiani

et al. 2019

JCM

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Atrial fibrillation (AF) is a common supraventricular arrhythmia, a recognized risk factor for ischemic stroke, as a potential driver for heart failure (HF). Cancer patients have an increased risk for AF, even not including any cancer-specific treatment, as surgery or chemotherapy. The mechanism is multifactorial, with inflammation and changes in autonomic tone as critical actors. Commonly, AF is a recurrent complication of the post-operative period in cancer surgery (especially thoracic). Recent papers confirmed a significant incidence of post-operative (non-cardiac surgery) AF (PAF), partially mitigated by the use of prophylactic (rate o rhythm control) treatments. A relevant difference, in terms of mean hospitalization time, emerges between patients developing PAF and those who do not, while long term impact remains a matter of debate, due to several potential confounding factors. Besides clinical predictors, structural (i.e., echocardiographic) and bio-humoral findings may help in risk prediction tasks. In this respect, pre-operative natriuretic peptides (NPs) concentrations are nowadays recognized as significant independent predictors of perioperative cardiovascular complications (including PAF), while elevated post-operative levels may further enhance risk stratification. The aim of the present paper is to trace the state of the art in terms of incidence, management, prevention, and outcome of PAF in the field of thoracic surgical oncology.

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Self-monitoring for atrial fibrillation recurrence in the discharge period post-cardiac surgery using an iPhone electrocardiogram

Cited by 60 publications

References 23 publications

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

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

National Heart Foundation of Australia and the Cardiac Society of Australia and New Zealand: Australian Clinical Guidelines for the Diagnosis and Management of Atrial Fibrillation 2018

Incidence, Management, Prevention and Outcome of Post-Operative Atrial Fibrillation in Thoracic Surgical Oncology

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