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Plasma Cell-free RNA Signatures of Inflammatory Syndromes in Children
Insights
Pediatric inflammatory syndromes like Kawasaki disease (KD) and Multisystem Inflammatory Syndrome in Children (MIS-C) can now be diagnosed using circulating cell-free RNA (cfRNA) in plasma, improving accuracy and identifying organ damage.
Area of Science:
- Biomarkers
- Molecular Diagnostics
- Pediatric Medicine
Background:
- Inflammatory syndromes are common pediatric hospital admissions, often misdiagnosed due to limited molecular diagnostic tools.
- Accurate differential diagnosis of conditions like Kawasaki disease (KD) and Multisystem Inflammatory Syndrome in Children (MIS-C) is challenging due to overlapping symptoms.
Approach:
- Circulating cell-free RNA (cfRNA) in plasma was profiled from 370 pediatric patients with inflammatory conditions.
- Machine learning models were developed using cfRNA profiles to differentiate between KD, MIS-C, viral, and bacterial infections.
- The study explored cfRNA's utility in characterizing tissue and organ-specific injury.
Key Points:
- Machine learning models achieved high performance (AUC = 0.97) in differentiating KD from MIS-C.
- A multiclass model accurately distinguished between KD, MIS-C, viral, and bacterial infections with 81% accuracy.
- cfRNA profiles effectively quantified injury to organs including the liver, heart, endothelium, nervous system, and respiratory tract.
Conclusions:
- Circulating cell-free RNA (cfRNA) is a versatile analyte for diagnosing and characterizing pediatric inflammatory syndromes.
- This approach offers a promising molecular tool to improve diagnostic accuracy and patient management for children with inflammatory conditions.
Abstract:
Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circulating cell-free RNA (cfRNA) in plasma as an analyte for the differential diagnosis and characterization of pediatric inflammatory syndromes. We profiled cfRNA in 370 plasma samples from pediatric patients with a range of inflammatory conditions, including Kawasaki disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), viral infections and bacterial infections. We developed machine learning models based on these cfRNA profiles, which effectively differentiated KD from MIS-C - two conditions presenting with overlapping symptoms - with high performance (Test Area Under the Curve (AUC) = 0.97). We further extended this methodology into a multiclass machine learning framework that achieved 81% accuracy in distinguishing among KD, MIS-C, viral, and bacterial infections. We further demonstrated that cfRNA profiles can be used to quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and the upper respiratory tract. Overall, this study identified cfRNA as a versatile analyte for the differential diagnosis and characterization of a wide range of pediatric inflammatory syndromes.
