| Literature DB >> 34912626 |
NagaSpurthy Reddy Anugu1, Safeera Khan2.
Abstract
Neonatal sepsis remains a significant diagnostic challenge in newborn care. It has the potential to be disastrous, but precise diagnosis is difficult. No biomarker has yet demonstrated sufficient diagnostic accuracy to rule out sepsis when clinical suspicion exists. As a result, neonates with suspected sepsis are treated with empiric antibiotics. These unnecessary antibiotics promote bacterial antibiotic resistance, raise economic costs, and alter the composition of the gut microbiota. This study aimed to determine the diagnostic accuracy of procalcitonin in the prompt diagnosis of neonatal sepsis. Articles were systematically screened in PubMed/MEDLINE, PubMed Central (PMC), and ScienceDirect, using keywords and Medical Subject Heading (MeSH) terms to identify the relevant articles. Additionally, one article from the Indian Journal of Applied Research was also used. Inclusion/exclusion criteria were applied post article screening via title and abstracts. Quality appraisal check was done using the Scale for the Assessment of Narrative Review Articles (SANRA) checklist, A Measurement Tool to Assess Systematic Reviews (AMSTAR) checklist, and Newcastle-Ottawa checklist. Six related articles were strictly reviewed. Procalcitonin is a useful biomarker in the early diagnosis of neonatal sepsis. Because procalcitonin has a better correlation with proven sepsis and is an early biomarker in diagnosing neonatal sepsis, it should be included in the overall sepsis evaluation. Future clinical trials on optimal cut-off levels of procalcitonin with shifting neonatal ages and its use in the post-op setting are needed.Entities:
Keywords: biomarkers; c-reactive protein; neonate; procalcitonin; sepsis
Year: 2021 PMID: 34912626 PMCID: PMC8664372 DOI: 10.7759/cureus.19485
Source DB: PubMed Journal: Cureus ISSN: 2168-8184
Quality Appraisal Tools
| Quality appraisal tools | Articles |
| Assessing the Methodological Quality of Systematic Reviews (AMSTAR) Checklist | Systematic reviews and meta-analysis |
| Newcastle-Ottawa checklist | Observational studies |
| Scale for the Assessment of Narrative Review Articles (SANRA) checklist | Research paper w/out methods section |
Figure 1PRISMA Flow Diagram
Adapted from Page et al. [12].
PRISMA: Preferred Reporting Items for Systematic Review and Meta-Analyses
Summary of Studies Included in Systematic Review
CRP: C-reactive protein; PCT: procalcitonin; NS: neonatal sepsis
| Study and year of publication | Study type | Purpose of study | Result/conclusion |
| Rashwan et al., 2019 [ | Cross-sectional study | To determine the validity of biomarkers in screening for NS | CRP—more valuable in late-onset NS. PCT, presepsin, and hs-CRP, when used together, were early diagnostic markers for NS |
| Eschborn and Weitkamp, 2019 [ | Systematic review | Review of kinetics and performance of PCT and CRP for diagnosis of NS | PCT and CRP perform better when measured serially to be used along with other clinical and laboratory data for initiation/stoppage of antibiotics |
| Ruan et al., 2018 [ | Systematic review and meta-analysis | To evaluate the accuracy of diagnosis of NS using PCT and CRP combined or presepsin alone | PCT and CRP together improves the accuracy of the diagnosis of NS |
| Sharma et al., 2018 [ | Literature review | Review of biomarkers for diagnosis of NS | CRP, PCT—most commonly used as sepsis markers. There is still a need to find an ideal biomarker |
| Liu et al., 2019 [ | Meta-analysis | To assess the accuracy of CRP in neonatal septicemia | CRP can be used in detecting NS. But serum PCT has high specificity and sensitivity in diagnosing early NS |
| Thota et al., 2016 [ | Observational study | To evaluate the role of PCT in the diagnosis of NS | PCT has a better correlation with confirmed sepsis. Therefore, it should be included in a full sepsis evaluation |
Laboratory Tests for Diagnosis of Neonatal Sepsis
ESR: erythrocyte sedimentation rate; C5a: complement component 5a; C5L2: complement 5a-like receptor; IL-1: interleukin-1; IL-6: interleukin-6; IL-8: interleukin-8; IL-1ra: interleukin-1 receptor antagonist; IL-2rs: interleukin-2 receptor subunits; IL-10: interleukin-10; RANTES: regulated on Activation, Normal T cell Expressed and Secreted; TNF-α: tumor necrosis factor-α; IFN-γ: interferon-γ; G-CSF: granulocyte colony-stimulating factor; CSF1: colony stimulating factor 1; SCF: stem cell factor; MIP1-a: macrophage inflammatory protein-1 alpha; sCD14: soluble cluster of differentiation 14; sICAM-1: soluble intercellular adhesion molecule-1; CD11b: cluster of differentiation molecule 11b; CD64: cluster of differentiation 64; CD69: cluster of differentiation 69; CD25: cluster of differentiation 25; CD19: cluster of differentiation 19; CD33: cluster of differentiation 33
| Specific laboratory tests | Hematologic investigations | Biochemical investigations | Cytokines and receptors |
| Blood, cerebrospinal fluid, and urine culture | White blood cell counts | C-reactive protein, procalcitonin | IL-1, IL-6, IL-8, IL-1ra, IL-2rs |
| Direct visualization of bacteria (Gram stain) | Total and differential, platelet counts | ESR, serum amyloid | IL-10, RANTES, TNF-α, IFN-γ |
| Detection of bacterial antigens | other phase reactants: haptoglobin, lactoferrin, neopterin, inter-inhibitor proteins, lipopolysaccharide-binding protein, C5a, C5L2, immunoglobulins | G-CSF, CSF1, SCF, MIP1-a | |
| Polymerase chain reaction (amplification of bacterial DNA) | sCD14, sICAM-1, CD11b, CD64, CD69, CD25, CD19, CD33 |