Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction.

Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K and serum laboratory potassium measurement (Lab-K) within 1 h were included. ECG-K had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K to predict moderate-to-severe hypokalemia (Lab-K ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K ≥ 6 mmol/L) were >0.85 and >0.95, respectively. The U-shaped relationships between K concentration and adverse outcomes were more prominent for ECG-K than for Lab-K. ECG-K and Lab-K hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K, patients with hypokalemic ECG-K had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K but dyskalemic ECG-K (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.