Monitoring serum potassium concentration in patients with severe hyperkalemia: the role of bloodless artificial intelligence-enabled electrocardiography.

Abstract

Background: Severe hyperkalemia is a life-threatening emergency requiring prompt management and close surveillance. Although artificial intelligence-enabled electrocardiography (AI-ECG) has been developed to rapidly detect hyperkalemia, its application to monitor potassium (K⁺) levels remains unassessed. This study aimed to evaluate the effectiveness of AI-ECG for monitoring K⁺ levels in patients with severe hyperkalemia.

Methods: This retrospective study was performed at an emergency department of a single medical center over 2.5 years. Patients with severe hyperkalemia defined as Lab-K⁺ ≥6.5 mmol/l with matched ECG-K⁺ ≥5.5 mmol/l were included. ECG-K⁺ was quantified by ECG12Net analysis of the AI-ECG system. The following paired ECG-K⁺ and Lab-K⁺ were measured at least twice, almost simultaneously, during and after K⁺-lowering therapy in 1 day. Clinical characteristics, pertinent intervention, and laboratory data were analyzed.

Results: Seventy-six patients fulfilling the inclusion criteria exhibited initial Lab-K⁺ 7.4 ± 0.7 and ECG-K⁺ 6.8 ± 0.5 mmol/l. Most of them had chronic kidney disease (CKD) or were on chronic hemodialysis (HD). The followed Lab-K⁺ and ECG-K⁺ measured with a mean time difference of 11.4 ± 5.6 minutes significantly declined in parallel both in patients treated medically (n = 39) and with HD (n = 37). However, there was greater decrement in Lab-K⁺ (mean 7.3 to 4.1) than ECG-K⁺ (mean 6.6 to 5.0) shortly after HD. Three patients with persistent ECG-K⁺ hyperkalemia despite normalized Lab-K⁺ exhibited concomitant acute cardiovascular comorbidities.

Conclusions: AI-ECG for K⁺ prediction may help monitor K⁺ level for severe hyperkalemia and reveal more severe cardiac disorders in the patients with persistent AI-ECG hyperkalemia.