Prognostic Value of Low-Cost White Blood Cell Indices and Procalcitonin for Mortality in Rwandan Sepsis Patients: A Prospective Intensive Care Unit Study

Background In resource-limited settings, early identification of sepsis and low-cost mortality predictors is critical for ICU triage. This study evaluated the prognostic value of baseline sociodemographic factors, routine haematological indices, and procalcitonin (PCT) levels for 40-day mortality among adult ICU patients meeting Sepsis-3 criteria in Rwanda. Methods A prospective cohort of 125 intensive care unit (ICU) patients was followed for 40 days. Baseline variables included sex, age, PCT, total white blood cell (WBC) count, differential counts (neutrophils, basophils, eosinophils, monocytes, lymphocytes), and neutrophil-to-lymphocyte ratio (NLR). Survival probabilities were estimated using Kaplan-Meier curves and log-rank tests. Cox proportional hazards models identified independent mortality predictors, with assumptions tested via Schoenfeld residuals and multicollinearity assessed using variance inflation factors. Time-dependent ROC analysis evaluated model performance at days 6, 10, and 15 using AUC values. Results Of 125 patients, 56 (44.8%) were female. Median age was 41 years for survivors and 50 years for non-survivors (p = 0.097). In multivariable Cox regression, elevated neutrophil counts (centered at the mean) were independently associated with increased mortality (aHR 1.07; 95% CI: 1.03–1.11; p = 0.002), indicating a 7% rise in hazard per unit increase. No significant associations were found for sex, age, PCT, monocyte counts, or NLR. ROC analysis showed that models integrating neutrophils and total WBC achieved the highest predictive accuracy, with AUCs ranging from 67–70% across all time points, outperforming simpler models. Conclusions Elevated neutrophil counts at ICU admission are independently linked to increased mortality. Integrating neutrophil and WBC data into predictive models enhances early risk stratification. These findings underscore the value of routine biomarkers and robust modelling to guide timely interventions in resource-constrained ICU settings.