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To derive a prediction equation for 30-day mortality in sepsis using a multi-marker approach and compare its performance to the Sequential Organ Failure Assessment (SOFA) score.This study included 159 septic patients admitted to an intensive care unit. Leukocytes count, procalcitonin (PCT), interleukin-6 (IL-6), and paraoxonase (PON) and arylesterase (ARE) activities of PON-1 were assayed from blood obtained on ICU presentation. Logistic regression was used to derive sepsis mortality score (SMS), a prediction equation describing the relationship between biomarkers and 30-day mortality.The 30-day mortality rate was 28.9%. The SMS was [elogit(p)/(1 + elogit(p))] × 100; logit(p) = 0.74 + (0.004 × PCT) + (0.001 × IL-6) − (0.025 × ARE) − (0.059 × leukocytes count). The SMC had higher area under the receiver operating characteristic curve (95% Cl) than SOFA score [0.814 (0.736–0.892) vs. 0.767 (0.677–0.857)], but is not statistically significant. When the SMS was added to the SOFA score, prediction of 30-day mortality improved compared to SOFA score used alone [0.845 (0.777–0.899), p = 0.022].A sepsis mortality score using baseline leukocytes count, PCT, IL-6 and ARE was derived, which predicted 30-day mortality with very good performance and added significant prognostic information to SOFA score.A sepsis mortality score to predict 30-day mortality in critically ill patients is proposed from five biomarkers.Logistic regression was used to create the Sepsis Mortality Score.Leukocytes count, procalcitonin, interleukin-6, arylesterase and paraoxonase activities of paraoxonase-1 were used.This multi-marker approach predicted 30-day mortality with very good performance in our sepsis cohort.This added significant prognostic information to the Sequential Organ Failure Assessment score.