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Published on: May 10, 2017
Plasma cell-free RNA signatures of inflammatory syndromes in children
Conor J Loy1, Venice Servellita2, Alicia Sotomayor-Gonzalez2
1Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850.
Insights
This study shows circulating cell-free RNA (cfRNA) can diagnose pediatric inflammatory syndromes like Kawasaki disease (KD) and multisystem inflammatory syndrome in children (MIS-C). cfRNA profiling aids in differentiating infections and assessing organ injury.
Area of Science:
- Molecular Diagnostics
- Pediatric Inflammatory Syndromes
- Bioinformatics
Background:
- Pediatric inflammatory syndromes are common causes of hospitalization and often misdiagnosed due to limited molecular diagnostic tools.
- Accurate differential diagnosis is crucial for effective treatment and management of these conditions in children.
Purpose of the Study:
- To investigate the utility of circulating cell-free RNA (cfRNA) in plasma for diagnosing and characterizing pediatric inflammatory syndromes.
- To develop and validate machine learning models using cfRNA profiles for differential diagnosis.
Main Methods:
- cfRNA was profiled in 370 plasma samples from pediatric patients with Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C), viral, and bacterial infections.
- Machine learning models were developed to differentiate between conditions based on cfRNA profiles.
- The models were evaluated for their accuracy in multiclass classification and organ injury quantification.
Main Results:
- Machine learning models effectively differentiated KD from MIS-C with high performance (test AUC = 0.98).
- A multiclass framework achieved 80% accuracy in distinguishing KD, MIS-C, viral, and bacterial infections.
- cfRNA profiles indicated tissue and organ-specific injury, including to the liver, heart, endothelium, nervous system, and respiratory tract.
Conclusions:
- Circulating cell-free RNA (cfRNA) is a promising analyte for the differential diagnosis of pediatric inflammatory syndromes.
- cfRNA profiling combined with machine learning offers a powerful tool for distinguishing between similar conditions and assessing disease severity.
- This approach has the potential to improve diagnostic accuracy and guide treatment strategies for pediatric 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 = 0.98]. We further extended this methodology into a multiclass machine learning framework that achieved 80% 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.
