Electrolyte disturbances are prevalent complications in critically ill patients with sepsis, significantly impacting patient prognosis. However, the specific association between serum potassium levels and mortality risk in this population remains poorly understood. This study aimed to investigate the association between serum potassium levels during hospitalization and the risk of 28-day and 90-day mortality in critically ill patients with sepsis. Data were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV) database, and patients with severe sepsis requiring ICU admission were stratified into quartiles based on their mean serum potassium levels. Outcomes assessed included 28-day and 90-day mortality. A multivariate Cox proportional hazards model was used to investigate the association between serum potassium levels and mortality, with restricted cubic splines to identify potential nonlinear correlations. A dichotomous Cox proportional hazards model was applied to analyze the association further, and Kaplan-Meier analysis assessed the mortality risk across different potassium ranges. A total of 25,203 patients were included, with 28-day and 90-day mortality rates of 27.84% and 40.48%, respectively. Multivariate analysis showed a significant association between serum potassium levels and mortality. Restricted cubic splines identified an inflection point at 4.4 mmol/L, with potassium levels above this threshold associated with higher mortality (28-day mortality: HR 2.96, 95% CI = 2.43-3.60; 90-day mortality: HR 2.19, 95% CI = 1.81-2.64). Kaplan-Meier analysis confirmed a significantly higher risk of death for patients with serum potassium levels above 4.4 mmol/L compared to those within the 3.5-4.4 mmol/L range (P<0.001). In critically ill patients with sepsis, serum potassium levels exceeding 4.4 mmol/L are associated with an increased risk of death. Maintaining the average serum potassium level within the range of 3.5-4.4 mmol/L appears to be safe and may contribute to better outcomes in this patient population.
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