A Narrative Review of Diagnostic Strategies in Necrotizing Soft Tissue Infections: From Clinical Scores to Multi-Omics and Machine Learning

Abstract

Necrotizing soft tissue infections (NSTIs) represent a group of rapidly progressing, life-threatening infections characterized by widespread tissue necrosis, systemic inflammation, and multiorgan failure. Early diagnosis remains a clinical challenge because of nonspecific initial manifestations and overlapping symptoms with other soft tissue infections. Diagnostic scoring systems such as the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score and its variants have been widely utilized to facilitate early recognition but are limited by variable sensitivity and insufficient predictive value across diverse clinical populations. Recent advances in multi-omics technologies and machine learning approaches have enabled the identification of molecular biomarkers and predictive patterns associated with NSTI onset and progression. Integration of high-dimensional omics data with clinical and imaging parameters holds potential for dynamic, real-time diagnostic support and individualized risk stratification in the intensive care setting. This review summarizes the evolution of diagnostic strategies for NSTIs, critically appraises the limitations of conventional clinical scoring systems, and examines emerging omics-based and machine learning-driven approaches. Finally, we propose an integrated diagnostic roadmap that aligns clinical assessment, imaging, microbiologic evaluation, host-response biomarkers, and multi-omics data to guide future research and clinical translation.