Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza.

Background: The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza. Methods: Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network). Results: We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection. Conclusions: This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.

Effect of mild COVID-19 on health-related quality of life.

Background: Little is known about the effects of a mild SARS-CoV-2 infection on health-related quality of life. Methods: This prospective observational study of symptomatic adults (18-87 years) who sought outpatient care for an acute respiratory illness, was conducted from 3/30/2020 to 4/30/2021. Participants completed the Short Form Health Survey (SF-12) at enrollment and 6-8 weeks later, to report their physical and mental health function levels as measured by the physical health and mental health composite scores (PHC and MHC, respectively). PHC and MHC scores for COVID-19 cases and non-COVID cases were compared using t-tests. Multivariable regression modeling was used to determine predictors of physical and mental health function at follow-up. Results: Of 2301 enrollees, 426 COVID-19 cases and 547 non-COVID cases completed both surveys. PHC improved significantly from enrollment to follow-up for both COVID-19 cases (5.4 ± 0.41; P < 0.001) and non-COVID cases (3.3 ± 0.32; P < 0.001); whereas MHC improved significantly for COVID-19 cases (1.4 ± 0.51; P < 0.001) and decreased significantly for non-COVID cases (-0.8 ± 0.37; P < 0.05). Adjusting for enrollment PHC, the most important predictors of PHC at follow-up included male sex (ß = 1.17; SE = 0.5; P = 0.021), having COVID-19 (ß = 1.99; SE = 0.54; P < 0.001); and non-white race (ß = -2.01; SE = 0.70; P = 0.004). Adjusting for enrollment MHC, the most important predictors of MHC at follow-up included male sex (ß = 1.92; SE = 0.63; P = 0.002) and having COVID-19 (ß = 2.42; SE = 0.67; P < 0.001). Conclusion: Both COVID-19 cases and non-COVID cases reported improved physical health function at 6-8 weeks' convalescence; whereas mental health function improved among COVID-19 cases but declined among non-COVID cases. Both physical and mental health functioning were significantly better among males with COVID-19 than females.

Population-based hospitalization burden estimates for respiratory viruses, 2015-2019.

BACKGROUND: Acute respiratory infections (ARIs) result in millions of illnesses and hundreds of thousands of hospitalizations annually in the United States. The responsible viruses include influenza, parainfluenza, human metapneumovirus, coronaviruses, respiratory syncytial virus (RSV), and human rhinoviruses. This study estimated the population-based hospitalization burden of those respiratory viruses (RVs) over 4 years, from July 1, 2015 to June 30, 2019, among adults ≥18 years of age for Allegheny County (Pittsburgh), Pennsylvania. METHODS: We used population-based statewide hospital discharge data, health system electronic medical record (EMR) data for RV tests, census data, and a published method to calculate burden. RESULTS: Among 26,211 eligible RV tests, 67.6% were negative for any virus. The viruses detected were rhinovirus/enterovirus (2552; 30.1%), influenza A (2,299; 27.1%), RSV (1082; 12.7%), human metapneumovirus (832; 9.8%), parainfluenza (601; 7.1%), influenza B (565; 6.7%), non-SARS-CoV-2 coronavirus (420; 4.9% 1.5 years of data available), and adenovirus (136; 1.6%). Most tests were among female (58%) and White (71%) patients with 60% of patients ≥65 years, 24% 50-64 years, and 16% 18-49 years. The annual burden ranged from 137-174/100,000 population for rhinovirus/enterovirus; 99-182/100,000 for influenza A; and 56-81/100,000 for RSV. Among adults <65 years, rhinovirus/enterovirus hospitalization burden was higher than influenza A; whereas the reverse was true for adults ≥65 years. RV hospitalization burden increased with increasing age. CONCLUSIONS: These virus-specific ARI population-based hospital burden estimates showed significant non-influenza burden. These estimates can serve as the basis for several areas of research that are essential for setting funding priorities and guiding public health policy.

Proposed clinical indicators for efficient screening and testing for COVID-19 infection using Classification and Regression Trees (CART) analysis.

The introduction and rapid transmission of SARS-CoV-2 in the United States resulted in methods to assess, mitigate, and contain the resulting COVID-19 disease derived from limited knowledge. Screening for testing has been based on symptoms typically observed in inpatients, yet outpatient symptoms may differ. Classification and regression trees recursive partitioning created a decision tree classifying participants into laboratory-confirmed cases and non-cases. Demographic and symptom data from patients ages 18-87 years enrolled from March 29-June 8, 2020 were included. Presence or absence of SARS-CoV-2 was the target variable. Of 832 tested, 77 (9.3%) tested positive. Cases significantly more often reported diarrhea (12 percentage points (PP)), fever (15 PP), nausea/vomiting (9 PP), loss of taste/smell (52 PP), and contact with a COVID-19 case (54 PP), but less frequently reported sore throat (-27 PP). The 4-terminal node optimal tree had sensitivity of 69%, specificity of 78%, positive predictive value of 20%, negative predictive value of 97%, and AUC of 76%. Among those referred for testing, negative responses to two questions could classify about half (49%) of tested persons with low risk for SARS-CoV-2 and would save limited testing resources. Outpatient symptoms of COVID-19 appear to be broader than the inpatient syndrome.Initial supplies of anticipated COVID-19 vaccines may be limited and administration of first such available vaccines may need to be prioritized for essential workers, the most vulnerable, or those likely to have a robust response to vaccine. Another priority group could be those not previously infected. Those who screen out of testing may be less likely to have been infected by SARS-CoV-2 virus thus may be prioritized for vaccination when supplies are limited.