Rapid Sepsis Detection Using Routine Laboratory Data: A Logistic Regression Approach

Abstract

BackgroundSepsis is a life-threatening condition associated with high morbidity and mortality, in which early diagnosis is crucial for improving clinical outcomes [1] Routine haematological parameters generated by automated analyzers have emerged as promising biomarkers for sepsis detection due to their rapid availability and low cost [2].   AimsThe principal aim was to investigate the predictive value of routine hematochemical parameters for sepsis diagnosis and to construct a validated multivariable logistic regression model. Particular attention was given to variable selection, model validation, and cut-off optimization through the weighted Youden index to maximize sensitivity while maintaining high specificity.   MethodsA total of 597 subjects admitted to the Emergency Department were included and the sample was randomly divided into training (n = 427; 71.5%) and validation datasets (n = 170; 28.5%). Dataset homogeneity was assessed through independent samples t-tests and chi-square tests with effect size estimation (Cohen’s d and Cramer’s V). Multicollinearity was evaluated using variance inflation factors (VIF > 10 threshold) and univariate logistic regression analyses were initially performed to identify candidate predictors associated with sepsis, with variable preselection based on statistical significance and sensitivity > 0.70. A forward stepwise multivariable logistic regression approach was subsequently applied (entry p = 0.05; removal p = 0.10). Model fit was evaluated using Nagelkerke’s R² and Akaike Information Criterion (AIC). Discriminative ability was assessed using ROC curves, area under the curve (AUC), sensitivity, specificity, and weighted Youden index (Jw). Since minimizing false negatives represented the main clinical priority, multiple probability thresholds (0.25–0.75) were tested to optimize classification performance.   ResultsMost hematochemical parameters differed significantly between sepsis and non-sepsis groups. Nine variables demonstrated strong discriminatory performance in univariate analyses (sensitivity > 0.70). The final multivariable model included ten predictors and demonstrated excellent goodness-of-fit (Nagelkerke’s R² = 0.947; AIC = 84.689). At the conventional threshold (0.50), sensitivity and specificity reached 97.12% and 97.26% in the training dataset, and 92.96% and 96.67% in validation. Threshold optimization identified 0.31 as the optimal cut-off (Jw = 0.9796), increasing sensitivity to 99.04% while maintaining specificity at 96.35%, substantially reducing false negatives. ConclusionsThe proposed statistical framework enabled the development of a highly discriminative and robust predictive model for sepsis detection. The prioritization of sensitivity-driven threshold optimization improved clinical applicability by minimizing missed diagnoses while preserving high specificity.