Relative Contribution of Diagnostic Testing to the Diagnosis of Respiratory Syncytial Virus in Hospitalized Adults in the United States.

BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of acute respiratory illness (ARI) in older adults. Optimizing diagnosis could improve understanding of RSV burden. METHODS: We enrolled adults ≥50 years of age hospitalized with ARI and adults of any age hospitalized with congestive heart failure or chronic obstructive pulmonary disease exacerbations at two hospitals during two respiratory seasons (2018-2020). We collected nasopharyngeal (NP) and oropharyngeal (OP) swabs (n=1558), acute and convalescent sera (n=568), and expectorated sputum (n=153) from participants, and recorded standard-of-care (SOC) NP results (n=805). We measured RSV antibodies by two immunoassays and performed BioFire testing on respiratory specimens. RESULTS: Of 1,558 eligible participants, 92 (5.9%) tested positive for RSV by any diagnostic method. Combined NP/OP PCR yielded 58 positives, while separate NP and OP testing identified 11 additional positives (18.9% increase). Compared to Study NP/OP PCR alone, the addition of paired serology increased RSV detection by 42.9% (28 vs 40) among those with both specimen types, while the addition of SOC swab RT-PCR results increased RSV detection by 25.9% (47 vs 59). CONCLUSIONS: The addition of paired serology testing, SOC swab results, and separate testing of NP and OP swabs improved RSV diagnostic yield in hospitalized adults.

Long-term Health Outcomes After Hospital Discharge Among Children Hospitalized for MIS-C or COVID-19, September 29, 2021, to June 21, 2022.

BACKGROUND: The long-term effects of children hospitalized with multisystem inflammatory syndrome in children (MIS-C) or acute COVID-19 are not well known. Our objective was to determine long-term outcomes. METHODS: Children hospitalized with MIS-C or COVID-19 at 3 US hospitals from March 2020, through February 2021 were followed to assess health through 2 years post-hospitalization using medical records and patient surveys. RESULTS: Medical record abstraction was performed for 183 patients hospitalized with MIS-C, 53 of whom participated in surveys, and 97 patients hospitalized with COVID-19, 35 of whom participated in surveys. Patients with MIS-C were younger (median, 9 vs. 14 years of age for COVID-19 patients; P = 0.004), more frequently male (62% vs. 39%; P < 0.001) and had more cardiac (14% vs. 2%; P = 0.001) and neurologic sequelae (8% vs. 1%; P = 0.023). Children with COVID-19 more often had other comorbidities (59% vs. 19%; P < 0.001). Full mental recovery at the time of survey 2 (median, 16 months post-hospitalization for patients with MIS-C and 20 months for patients with COVID-19) was 85% and 88%, respectively; full physical recovery was 87% and 81%, respectively; and nearly all had resumption of normal activities. Patients with MIS-C reported more frequent headache at 1 month (45% vs. 20%; P = 0.037). Patients with COVID-19 were more likely to report cough at 1 month (37% vs. 17%; P = 0.045). Fatigue persisted >1 year in 15%-20% of patients in both groups. CONCLUSIONS: Approximately 20% of children with MIS-C and COVID-19 continued to have symptoms including fatigue and headache >1 year after hospital discharge. The duration of these findings emphasizes the importance of providers following patients until sequelae have resolved.

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.