Utility of volume, conductivity and scatter parameters for early diagnosis of neonatal sepsis

Abstract

Introduction: Neonatal sepsis comprises a major problem in India. Early detection of neonatal sepsis is challenging as clinical signs are subjective and the gold standard, a positive culture, requires 1–3 days. Volume conductivity scatter (VCS) data were obtained simultaneously with the differential count without additional cost or sample requirement. The present study was undertaken in a tertiary care teaching hospital with the aim of evaluating VCS data and other laboratory parameters for diagnosis of neonatal sepsis. Materials And Methods: Peripheral blood samples of 500 consecutive neonates were analyzed over a period of 2 years for complete blood count (CBC) with band cells, eosinophils, C-reactive protein (CRP), and culture. VCS parameters were obtained simultaneously from Beckman coulter analyzers for neonates aged 0–28 days with suspected sepsis. Controls: Gestation-matched healthy controls with no clinical or laboratory suspicion of infection. The study population was divided into following groups: proven sepsis, clinical sepsis, and no sepsis. A cutoff value of 0.3615 based on CRP, mean neutrophil volume (MNV), and volume distribution width (VDW) was taken to indicate the presence or absence of sepsis; based on the previously published work by Celik et al. Results: A total of 500 consecutive samples of neonates admitted in neonatal Intensive Care Unit of Bharati Hospital were analyzed. All the three groups were analyzedd with respect to CBC values, peripheral blood smear, VCS parameters, CRP values and blood cultures which gave a sensitivity of 76%, specificity of 88%, positive predictive value 77%, and negative predictive value 87%. Conclusions: We analyzed the utility of VCS parameters along with CRP for early diagnosis of neonatal sepsis. The study results indicate that the factor based on MNV, VDW, and CRP with cutoff of 0.3615 is a useful indicator of neonatal sepsis without incurring additional cost, with high specificity (88%), and high negative predictive value (87%).