Comparison of Red Cell Distribution Width and Serum Lactate as Predictors of Outcomes in Patients with Sepsis

Background: Early risk stratification of sepsis is crucial for improving its management and enhancing outcomes. Serum lactate is an established prognostic biomarker; a growing body of evidence suggests that red cell distribution width (RDW), a routinely measured hematological value, can also be used as a predictor of mortality. The current study aimed to compare RDW with lactate as a predictor of outcome in patients with sepsis. Methods: This prospective observational study was carried out in 40 adult sepsis cases admitted to the ICU. Baseline demographic, clinical, and laboratory values were collected. Measurement of RDW and lactate was performed at admission (T0), and clearance of lactate was done at 24 hours. SOFA scores were determined. The appropriate statistical tests were used to compare the outcomes of the survivors and non-survivors. Logistic regression and receiver operating characteristic (ROC) analysis were conducted to measure the predictive accuracy. Results: The mean age of the cohort was 62.5 ± 1.48 years, with higher age, comorbidity burden, and SOFA scores in non-survivors (p < 0.05). Admission RDW and lactate were significantly elevated in non-survivors (17.5% vs. 14.9%, p < 0.001; 5.1 mmol/L vs. 2.3 mmol/L, p < 0.001). ROC analysis showed strong discriminatory ability for both RDW (AUC 0.86) and lactate (AUC 0.89), with the highest accuracy when combined (AUC 0.93). Logistic regression confirmed RDW >16.5% and lactate >3.8 mmol/L as independent predictors of mortality. Adding these biomarkers to SOFA scores improved risk reclassification (NRI 0.71, p = 0.003). Conclusion: RDW and lactate are reliable and independent predictors of mortality in sepsis. Their combined assessment enhances prognostic accuracy beyond conventional clinical scoring systems. Given their simplicity and accessibility, RDW and lactate should be incorporated into routine sepsis risk stratification to facilitate early identification of high-risk patients and optimize management.