Hyperkalemia is a common and potentially life-threatening electrolyte disorder, yet there is limited evidence guiding the optimal timing of potassium-lowering therapy in routine clinical practice. Although electrocardiographic (ECG) abnormalities are recommended to inform treatment decisions, such findings are often subtle and difficult to recognize consistently by clinicians.
This study aims to emulate a target trial to evaluate the association between the timing of potassium-lowering therapy (timely versus delayed initiation) and short-term mortality among patients with laboratory-confirmed hyperkalemia presenting to the emergency department. In addition, the study examines whether artificial intelligence-enabled ECG (AI-ECG) stratification identifies patient subgroups that may differentially benefit from earlier treatment.
Using observational electronic health record data from multiple healthcare systems, including publicly available critical care databases and institutionally governed hospital datasets, treatment strategies are compared using causal inference methods designed to approximate randomized assignment. The primary outcome is 90-day all-cause mortality.
The results of this study are intended to inform clinical decision-making regarding treatment timing in hyperkalemia and to evaluate the potential role of AI-ECG as a risk stratification tool in real-world settings.
patients visit to emergency department
serum potassium ≥5.5 mEq/L
had a standard 12-lead ECG performed within 1 hour of the qualifying potassium measurement
potassium-lowering therapy administered before the index blood draw
no potassium-lowering therapy initiated within 4 hours after the index draw
duplicate encounters
missing key covariates