Pathogen-Specific Hypoalbuminemia in Patients with Sepsis: A Retrospective Study with Propensity Score Analysis

Hypoalbuminemia has been proven to be an independent risk factor for poor prognosis in sepsis. This study attempts to provide a better understanding of the differences in sepsis-related hypoalbuminemia to facilitate efficient identification of high-risk patients by clinicians.

We collected and analyzed data on 1827 patients with sepsis from the intensive care unit (ICU) retrospectively. Based on their serum albumin levels, the patients were classified into two groups: hypoalbuminemia (n=1405) and non-hypoalbuminemia (n=422). The pathogens were identified based on the results of the blood cultures. We conducted propensity score matching (PSM) to balance baseline characteristics across pathogen groups and minimize confounding factors.

Fourteen species of the most frequently detected pathogens were identified based on blood culture results from patients with sepsis. Eight species of pathogens, including Candida, Klebsiella, Acinetobacter baumannii, Staphylococcus, Pseudomonas, Streptococcus, Enterococcus, and Escherichia coli, caused a significantly higher risk of developing hypoalbuminemia than other pathogen infections, all with statistical differences (p<0.05). Restricted cubic spline (RCS) analysis showed a U- or L-shaped relationship between serum albumin levels and 28-day all-cause mortality, with critical values that differed across the different species of pathogens. The L-shaped forms of Candida, Staphylococcus, Enterococcus, and Escherichia coli had albumin cut-off values of 30.36 g/L, 32.27 g/L, 30.46 g/L, and 30.95 g/L, respectively. While Bacteroides fragilis exhibited a U-shaped relationship with albumin cut-off values of 33.41 g/L and 39.42 g/L.

Our findings highlight the significance of meticulous surveillance of serum albumin levels in septic patients exposed to different species of pathogens. It is essential to pay attention to the pathogen-specific albumin threshold, not only for the efficient identification of high-risk patients, but also for prompt therapeutic interventions that can improve patient prognosis.