Optimization of diagnostical algorithms for pediatric polyneuropathy: etiological classification and immunological assessment in genetic and acquired types

Abstract

Introduction. The timely diagnosis of polyneuropathies in children is a pressing issue in modern pediatric neurology, where etiological differentiation and immunological assessment significantly increase diagnostic accuracy. The aim of the study was to develop a highly sensitive, combined diagnostic algorithm based on the evaluation of etiological factors and immunological status in children with inflammatory and hereditary polyneuropathies. Materials and Methods. To optimize the diagnostic algorithm, a combined scoring system was developed. An integrative diagnostic model, based on the z-standardized sum of CSF protein, IgG, CRP, and motor NCV indicators (z-CSF + z-IgG + z-CRP − z-NCV), was established to reliably differentiate between inflammatory and hereditary polyneuropathies. Statistical analysis was performed using GraphPad Prism 10 software. The normality of distribution was assessed using the Shapiro-Wilk test. This prospective-retrospective study was conducted from 2022 to 2025. A total of 105 children (aged 1–18) were divided into three groups: inflammatory polyneuropathy (n=45), hereditary polyneuropathy (n=30), and control (n=30). Clinical, immunological (IgG, IgM, IgA, CD3+/CD4+/CD8+, TNF-α, IL-6), electroneuromyography (ENMG), and genetic methods were employed. Results and Discussion. In the inflammatory group, a decreased CD4+/CD8+ ratio (1.20±0.35) was observed, along with a significant increase in IgG (14.58±3.33 g/L), TNF-α, and IL-6 levels (p<0.0001). No immunological changes were observed in the hereditary group. The combined diagnostic algorithm demonstrated high accuracy, with an AUC of 0.986. Conclusion. The developed combined algorithm allows for the reliable differentiation of inflammatory and hereditary polyneuropathies and is recommended for implementation in regional clinical practice.