The Neutrophil-to-Lymphocyte Ratio (NLR) Can Predict Sepsis's Presence and Severity in Malnourished Infants-A Single Center Experience

Predictive Role of NLR, dNLR, PLR, NLPR, and Other Laboratory Markers in Diagnosing SIRS in Premature Newborns

BACKGROUND

Premature newborns are at a significant risk for Systemic Inflammatory Response Syndrome SIRS, a condition associated with high morbidity and mortality. This study aimed to evaluate the predictive and diagnostic capability of laboratory markers like Neutrophil to Lymphocyte Ratio (NLR), derived Neutrophil to Lymphocyte Ratio (dNLR), Platelet-to-Lymphocyte Ratio (PLR), and Neutrophil-to-Lymphocyte-to-Platelet Ratio (NLPR) in diagnosing SIRS in premature newborns.

METHODS

Premature newborns with and without SIRS were evaluated in a prospective design during a one-year period. Among 136 newborns, early and 72 h post-birth analyses were performed.

RESULTS

At 24 h, NLR's cutoff value was 8.69, yielding sensitivity and specificity rates of 52.77% and 83.47% (p = 0.0429), respectively. The dNLR showed a cutoff of 5.61, with corresponding rates of 63.27% and 84.15% (p = 0.0011), PLR had a cutoff of 408.75, with rates of 51.89% and 80.22% (p = 0.1026), and NLPR displayed a cutoff of 0.24, with rates of 75.85% and 86.70% (p = 0.0002). At 72 h, notable sensitivity and specificity improvements were observed, particularly with NLPR having a cutoff of 0.17, showing sensitivity of 77.74% and specificity of 95.18% (p < 0.0001). NLR above the cutoff indicated a 33% increase in SIRS risk, with a hazard ratio (HR)of 1.33. The dNLR was associated with a twofold increase in risk (HR 2.04). NLPR demonstrated a significant, over threefold increase in SIRS risk (HR 3.56), underscoring its strong predictive and diagnostic value for SIRS development.

CONCLUSION

Integrating these findings into clinical practice could enhance neonatal care by facilitating the early identification and management of SIRS, potentially improving outcomes for this vulnerable population.

The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination

Introduction

Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study.

Methods

Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.

Results

Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).

Discussion

The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.