Pulse Biosciences achieves 96% success rate in first-year human trials of atrial fibrillation catheter

Pulse Biosciences ($PLSE) announced the initial human feasibility study results for its nPulse cardiac catheter at the “Heart Rhythm 2026” conference. The most noteworthy part of this release is the procedural success rate and safety. At 6 months, 95 out of 95 cases were successful, achieving 100%; at 12 months, 51 out of 53 cases were successful, for a success rate of 96%.

The company said that this study is of great significance as early clinical data for the treatment of atrial fibrillation (AF). The 1-year Kaplan-Meier analysis showed that the proportion of patients free from atrial fibrillation (AF), atrial flutter (AFL), and atrial tachycardia (AT) was 90%. Serious adverse events (SAE) occurred in 3 out of a total of 177 cases, for an incidence rate of 1.7%.

Procedural efficiency metrics were also relatively favorable. The average device dwell time within the left atrium was 18.6±13.0 minutes, and the total procedure time was 60.2±27.7 minutes. Fluoroscopy time was 9.4±5.9 minutes, and the average number of energy applications to achieve pulmonary vein isolation (PVI) was 12.3±2.6 times. In conventional atrial fibrillation procedures, reductions in procedure time and fluoroscopy time can affect healthcare provider fatigue, patient burden, and hospital operational efficiency; therefore, these figures are sufficient to draw market attention.

The result has been interpreted as an early indicator of whether Pulse Biosciences can demonstrate its technological competitiveness in the field of cardiac electrophysiology. Since it is still in the early feasibility study stage, larger-scale follow-up clinical trials and long-term follow-up data are needed. However, based solely on the data that has been published, both the “success rate” and “safety” meet expectations. If subsequent clinical trials achieve similar results, the company’s presence in the atrial fibrillation treatment device market could be strengthened.

TP AI Notice: This article is summarized based on the TokenPost.ai language model. Key textual content may be omitted or may not align with actual facts.

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