What is the clinical evidence from this study?

Study design: Observational. Population: Arrhythmia (n=233). Intervention: AliveCor Kardia Mobile (KM) device and algorithm vs. Manual interpretation by a dedicated arrhythmia team (cardiologist). Primary outcome: Detection of atrial fibrillation (Sensitivity 92%, Specificity 95%).

What does this research mean for the field?

The AliveCor Kardia Mobile algorithm has high negative predictive value for atrial fibrillation but poor positive predictive value and recognition of other arrhythmias, making it inadequate as a stand-alone application and necessitating manual ECG review. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

December 6, 2018Open Access

A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs

Key Result

The AliveCor Kardia Mobile algorithm detected atrial fibrillation with 92% sensitivity and 95% specificity, but its 80% positive predictive value and poor recognition of other arrhythmias necessitate manual ECG review.

Study Design

Type

Observational (n=233)

Structured PICO

Does the AliveCor Kardia Mobile algorithm accurately detect cardiac arrhythmias compared to manual cardiologist assessment in ambulatory patients?

Population

233 ambulatory patients in the Hartwacht Arrhythmia program (mean age 58.4 years, 52% male) who submitted a Kardia Mobile ECG between January 2017 and March 2018.

Intervention

AliveCor Kardia Mobile (KM) device and its automated algorithm for remote arrhythmia detection

Comparator

Manual ECG analysis by the Hartwacht team led by a cardiologist

Outcome

Accuracy of the KM algorithm in detecting sinus rhythm, atrial fibrillation, and other arrhythmias compared to manual assessment by a cardiologist-led teamsurrogate

Main Result

Effect estimate: Sensitivity 92%, Specificity 95%

Limitations

Retrospective analysis with potential selection bias
Predictive values vary with prevalence and should be interpreted with caution
Reference standard was a 30-second one-lead ECG interpreted by a cardiologist, not a simultaneously obtained 12-lead ECG, which may overestimate specificity

Abstract

BACKGROUND: In recent years many mobile devices able to record health-related data in ambulatory patients have emerged. However, well-organised programs to incorporate these devices are sparse. Hartwacht Arrhythmia (HA) is such a program, focusing on remote arrhythmia detection using the AliveCor Kardia Mobile (KM) and its algorithm. OBJECTIVES: The aim of this study was to assess the benefit of the KM device and its algorithm in detecting cardiac arrhythmias in a real-world cohort of ambulatory patients. METHODS: All KM ECGs recorded in the HA program between January 2017 and March 2018 were included. Classification by the KM algorithm was compared with that of the Hartwacht team led by a cardiologist. Statistical analyses were performed with respect to detection of sinus rhythm (SR), atrial fibrillation (AF) and other arrhythmias. RESULTS: 5,982 KM ECGs were received from 233 patients (mean age 58 years, 52% male). The KM algorithm categorised 59% as SR, 22% as possible AF, 17% as unclassified and 2% as unreadable. According to the Hartwacht team, 498 (8%) ECGs were uninterpretable. Negative predictive value for detection of AF was 98%. However, positive predictive value as well as detection of other arrhythmias was poor. In 81% of the unclassified ECGs, the Hartwacht team was able to provide a diagnosis. CONCLUSIONS: This study reports on the first symptom-driven remote arrhythmia monitoring program in the Netherlands. Less than 10% of the ECGs were uninterpretable. However, the current performance of the KM algorithm makes the device inadequate as a stand-alone application, supporting the need for manual ECG analysis in HA and similar programs.

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