Plasma cell-free RNA signatures of inflammatory syndromes in children.

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.

Plasma Cell-free RNA Signatures of Inflammatory Syndromes in Children.

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.

Clinical Features and Outcomes of Pediatric and Adult Patients Hospitalized for Coronavirus Disease 2019: A Comparison Across Age Strata.

Background: Coronavirus disease 2019 (COVID-19) continues to cause hospitalizations and severe disease in children and adults. Methods: This study compared the risk factors, symptoms, and outcomes of children and adults hospitalized for COVID-19 from March 2020 to May 2023 across age strata at 5 US sites participating in the Predicting Viral-Associated Inflammatory Disease Severity in Children with Laboratory Diagnostics and Artificial Intelligence consortium. Eligible patients had an upper respiratory swab that tested positive for severe acute respiratory syndrome coronavirus 2 by nucleic acid amplification. Adjusted odds ratios (aOR) of clinical outcomes were determined for children versus adults, for pediatric age strata compared to adolescents (12-17 years), and for adult age strata compared to young adults (22-49 years). Results: Of 9101 patients in the Predicting Viral-Associated Inflammatory Disease Severity in Children with Laboratory Diagnostics and Artificial Intelligence cohort, 1560 were hospitalized for COVID-19 as the primary reason. Compared to adults (22-105 years, n = 675), children (0-21 years, n = 885) were less commonly vaccinated (14.3% vs 34.5%), more commonly infected with the Omicron variant (49.5% vs 26.1%) and had fewer comorbidities (P < .001 for most comparisons), except for lung disease (P = .24). After adjusting for confounding variables, children had significantly lower odds of receiving supplemental oxygen (aOR, 0.57; 95% confidence interval, .35-.92) and death (aOR, 0.011; 95% confidence interval, <.01-.58) compa--red to adults. Among pediatric age strata, adolescents 12-17 years had the highest odds of receiving supplemental oxygen, high-flow oxygen, and ICU admission. Among adults, those 50-64 years had the highest odds of mechanical ventilation and ICU admission. Conclusions: Clinical outcomes of COVID-19 differed across pediatric and adult age strata. Adolescents experienced the most severe disease among children, whereas adults 50-64 years experienced the most severe disease among adults.