Validity of ICD-10-based algorithms to identify patients with influenza in inpatient and outpatient settings.

PURPOSE: To evaluate the validity of ICD-10-CM code-based algorithms as proxies for influenza in inpatient and outpatient settings in the USA. METHODS: Administrative claims data (2015-2018) from the largest commercial insurer in New Jersey (NJ), USA, were probabilistically linked to outpatient and inpatient electronic health record (EHR) data containing influenza test results from a large NJ health system. The primary claims-based algorithms defined influenza as presence of an ICD-10-CM code for influenza, stratified by setting (inpatient/outpatient) and code position for inpatient encounters. Test characteristics and 95% confidence intervals (CIs) were calculated using test-positive influenza as a reference standard. Test characteristics of alternative outpatient algorithms incorporating CPT/HCPCS testing codes and anti-influenza medication pharmacy claims were also calculated. RESULTS: There were 430 documented influenza test results within the study period (295 inpatient, 135 outpatient). The claims-based influenza definition had a sensitivity of 84.9% (95% CI 72.9%-92.1%), specificity of 96.3% (95% CI 93.1%-98.0%), and PPV of 83.3% (95% CI 71.3%-91.0%) in the inpatient setting, and a sensitivity of 76.7% (95% CI 59.1%-88.2%), specificity of 96.2% (95% CI 90.6%-98.5%), PPV of 85.2% (95% CI 67.5%-94.1%) in the outpatient setting. Primary inpatient discharge diagnoses had a sensitivity of 54.7% (95% CI 41.5%-67.3%), specificity of 99.6% (95% CI 97.7%-99.9%), and PPV of 96.7% (95% CI 83.3%-99.4%). CPT/HCPCS codes and anti-influenza medication claims were present for few outpatient encounters (sensitivity 3%-10%). CONCLUSIONS: In a large US healthcare system, inpatient ICD-10-CM codes for influenza, particularly primary inpatient diagnoses, had high predictive value for test-positive influenza. Outpatient ICD-10-CM codes were moderately predictive of test-positive influenza.

Evaluation of disease severity and prediction of severe cases in children hospitalized with influenza A (H1N1) infection during the post-COVID-19 era: a multicenter retrospective study.

BACKGROUND: The rebound of influenza A (H1N1) infection in post-COVID-19 era recently attracted enormous attention due the rapidly increased number of pediatric hospitalizations and the changed characteristics compared to classical H1N1 infection in pre-COVID-19 era. This study aimed to evaluate the clinical characteristics and severity of children hospitalized with H1N1 infection during post-COVID-19 period, and to construct a novel prediction model for severe H1N1 infection. METHODS: A total of 757 pediatric H1N1 inpatients from nine tertiary public hospitals in Yunnan and Shanghai, China, were retrospectively included, of which 431 patients diagnosed between February 2023 and July 2023 were divided into post-COVID-19 group, while the remaining 326 patients diagnosed between November 2018 and April 2019 were divided into pre-COVID-19 group. A 1:1 propensity-score matching (PSM) was adopted to balance demographic differences between pre- and post-COVID-19 groups, and then compared the severity across these two groups based on clinical and laboratory indicators. Additionally, a subgroup analysis in the original post-COVID-19 group (without PSM) was performed to investigate the independent risk factors for severe H1N1 infection in post-COIVD-19 era. Specifically, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select candidate predictors, and logistic regression was used to further identify independent risk factors, thus establishing a prediction model. Receiver operating characteristic (ROC) curve and calibration curve were utilized to assess discriminative capability and accuracy of the model, while decision curve analysis (DCA) was used to determine the clinical usefulness of the model. RESULTS: After PSM, the post-COVID-19 group showed longer fever duration, higher fever peak, more frequent cough and seizures, as well as higher levels of C-reactive protein (CRP), interleukin 6 (IL-6), IL-10, creatine kinase-MB (CK-MB) and fibrinogen, higher mechanical ventilation rate, longer length of hospital stay (LOS), as well as higher proportion of severe H1N1 infection (all P < 0.05), compared to the pre-COVID-19 group. Moreover, age, BMI, fever duration, leucocyte count, lymphocyte proportion, proportion of CD3+ T cells, tumor necrosis factor α (TNF-α), and IL-10 were confirmed to be independently associated with severe H1N1 infection in post-COVID-19 era. A prediction model integrating these above eight variables was established, and this model had good discrimination, accuracy, and clinical practicability. CONCLUSIONS: Pediatric H1N1 infection during post-COVID-19 era showed a higher overall disease severity than the classical H1N1 infection in pre-COVID-19 period. Meanwhile, cough and seizures were more prominent in children with H1N1 infection during post-COVID-19 era. Clinicians should be aware of these changes in such patients in clinical work. Furthermore, a simple and practical prediction model was constructed and internally validated here, which showed a good performance for predicting severe H1N1 infection in post-COVID-19 era.

Fatal Viral and Bacterial Septicemia in a Seventeen-Year-Old Woman with Immunodepressive Influenza A H1N1: An Autopsy Case.

Abstract: The Influenza A H1N1 subtype can present with a wide spectrum of severity, from mild symptoms of influenza to severe respiratory distress. The morbidity and mortality connected to influenza are mostly associated with secondary bacterial infections. The influenza syndrome alone can cause a massive release of cytokines with dysregulation of the immune system, and it can act in synergy with other bacteria which can enhance cytokines secretion. This article deals with a case of severe pneumonia of H1N1 in a 17-year-old woman with bacterial superinfection with Staphylococcus aureus characterized by a high level of interleukine-6 (105900 pg/mL) and the appearance of severe leukopenia with immuno-suppression, such that HIV infection and hematological diseases were included in the initial differential diagnosis. After death, the autopsy confirmed the presence of severe pneumonia, in addition to an hepatic steatosis in absence of other risk factors. This case reports the rapid and lethal course of influenza A /H1N1 in a young and healthy subject without comorbidities, in an age group in which mortality is about 0.3 deaths per 100,000. The case underlines the importance of quickly diagnosis of viral infections and the differential diagnoses with other immunosuppressive diseases, which can be fatal even in adolescent and healthy subjects.

Comparative Analysis of Clinical Outcomes Using Propensity Score Matching: Coronavirus Disease 2019 vs. Seasonal Influenza in Korea.

BACKGROUND: The advent of the omicron variant and the formulation of diverse therapeutic strategies marked a new epoch in the realm of coronavirus disease 2019 (COVID-19). Studies have compared the clinical outcomes between COVID-19 and seasonal influenza, but such studies were conducted during the early stages of the pandemic when effective treatment strategies had not yet been developed, which limits the generalizability of the findings. Therefore, an updated evaluation of the comparative analysis of clinical outcomes between COVID-19 and seasonal influenza is requisite. METHODS: This study used data from the severe acute respiratory infection surveillance system of South Korea. We extracted data for influenza patients who were infected between 2018 and 2019 and COVID-19 patients who were infected in 2021 (pre-omicron period) and 2022 (omicron period). Comparisons of outcomes were conducted among the pre-omicron, omicron, and influenza cohorts utilizing propensity score matching. The adjusted covariates in the propensity score matching included age, sex, smoking, and comorbidities. RESULTS: The study incorporated 1,227 patients in the pre-omicron cohort, 1,948 patients in the omicron cohort, and 920 patients in the influenza cohort. Following propensity score matching, 491 patients were included in each respective group. Clinical presentations exhibited similarities between the pre-omicron and omicron cohorts; however, COVID-19 patients demonstrated a higher prevalence of dyspnea and pulmonary infiltrates compared to their influenza counterparts. Both COVID-19 groups exhibited higher in-hospital mortality and longer hospital length of stay than the influenza group. The omicron group showed no significant improvement in clinical outcomes compared to the pre-omicron group. CONCLUSION: The omicron group did not demonstrate better clinical outcomes than the pre-omicron group, and exhibited significant disease severity compared to the influenza group. Considering the likely persistence of COVID-19 infections, it is imperative to sustain comprehensive studies and ongoing policy support for the virus to enhance the prognosis for individuals affected by COVID-19.

Differences of respiratory mechanics in mechanical ventilation of acute respiratory distress syndrome between patients with COVID-19 and Influenza A.

BACKGROUND: Whether COVID-19-induced acute respiratory distress syndrome (ARDS) should be approached differently in terms of mechanical ventilation therapy compared to other virus-induced ARDS is debatable. Therefore, we aimed to ascertain whether the respiratory mechanical characteristics of COVID-19-induced ARDS differ from those of influenza A induced ARDS, in order to establish a rationale for mechanical ventilation therapy in COVID-19-induced ARDS. METHODS: This was a retrospective cohort study comparing patients with COVID-19-induced ARDS and influenza A induced ARDS. We included intensive care unit (ICU) patients with COVID-19 or Influenza A aged ≥ 19, who were diagnosed with ARDS according to the Berlin definition between January 2015 and July 2021. Ventilation parameters for respiratory mechanics were collected at specific times on days one, three, and seven after intubation. RESULTS: The median age of the 87 participants was 71.0 (62.0-78.0) years old, and 63.2% were male. The ratio of partial pressure of oxygen in arterial blood to the fractional of inspiratory oxygen concentration in COVID-19-induced ARDS was lower than that in influenza A induced ARDS during the initial stages of mechanical ventilation (influenza A induced ARDS 216.1 vs. COVID-19-induced ARDS 167.9, p = 0.009, day 1). The positive end expiratory pressure remained consistently higher in the COVID-19 group throughout the follow-up period (7.0 vs. 10.0, p < 0.001, day 1). COVID-19 and influenza A initially showed different directions for peak inspiratory pressure and dynamic compliance; however, after day 3, both groups exhibited similar directions. Dynamic driving pressure exhibited opposite trends between the two groups during mechanical ventilation. CONCLUSIONS: Respiratory mechanics show clear differences between COVID-19-induced ARDS and influenza A induced ARDS. Based on these findings, we can consider future treatment strategies for COVID-19-induced ARDS.

Prospective external validation of the FluScore risk score for influenza in outpatients.

BACKGROUND: Testing for influenza in patients with acute lower respiratory tract infection (LRTI) is common and in some cases is performed for all patients with LRTI. A more selective approach to testing could be more efficient. METHODS: We used data from two prospective studies in the US primary and urgent care settings that enrolled patients with acute LRTI or influenza-like illness. Data were collected in the 2016, 2019, 2021, and 2022 flu seasons. All patients underwent polymerase chain reaction (PCR) testing for influenza and the FluScore was calculated based on patient-reported symptoms at their initial visit. The probability of influenza in each risk group was reported, as well as stratum-specific likelihood ratios (SSLRs) for each risk level. RESULTS: The prevalence of influenza within risk groups varied based on overall differences in flu seasons and populations. However, the FluScore exhibited consistent performance across various seasons and populations based on the SSLRs. The FluScore had a consistent SSLR range of 0.20 to 0.23 for the low-risk group, 0.63 to 0.99 for the moderate-risk group, and 1.46 to 1.67 for the high-risk group. The diagnostic odds ratio based on the midpoints of these ranges was 7.25. CONCLUSIONS: The FluScore could streamline patient categorization, identifying patients who could be exempted from testing, while identifying candidates for rapid influenza tests. This has the potential to be more efficient than a "one size fits all" test strategy, as it strategically targets the use of tests on patients most likely to benefit. It is potentially usable in a telehealth setting.

An electrochemical genomagnetic assay for detection of SARS-CoV-2 and Influenza A viruses in saliva.

Viral respiratory infections represent a major threat to the population's health globally. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease and in some cases the symptoms can be confused with Influenza disease caused by the Influenza A viruses. A simple, fast, and selective assay capable of identifying the etiological agent and differentiating the diseases is essential to provide the correct clinical management to the patient. Herein, we described the development of a genomagnetic assay for the selective capture of viral RNA from SARS-CoV-2 and Influenza A viruses in saliva samples and employing a simple disposable electrochemical device for gene detection and quantification. The proposed method showed excellent performance detecting RNA of SARS-CoV-2 and Influenza A viruses, with a limit of detection (LoD) and limit of quantification (LoQ) of 5.0 fmol L-1 and 8.6 fmol L-1 for SARS-CoV-2, and 1.0 fmol L-1 and 108.9 fmol L-1 for Influenza, respectively. The genomagnetic assay was employed to evaluate the presence of the viruses in 36 saliva samples and the results presented similar responses to those obtained by the real-time reverse transcription-polymerase chain reaction (RT-PCR), demonstrating the reliability and capability of a method as an alternative for the diagnosis of COVID-19 and Influenza with point-of-care capabilities.

Evaluation of Multiplex Rapid Antigen Test for the Detection of SARS-CoV-2 and Influenza A/B in Respiratory Samples.

BACKGROUND: There is little data about the performance of multiplex rapid antigen tests (RATs) on the detection of SARS-CoV-2, influenza A (Flu A), and influenza B (Flu B). This study is to evaluate the performance of Panbio COVID-19/Flu A&B rapid panel (Abbott Diagnostics, Korea) and analyze the factors influencing its sensitivity. METHODS: Nasopharyngeal swabs were collected and stored at the Korea University Anam hospital. In total, 400 residual samples from nasopharyngeal swabs were examined. The diagnostic accuracy of RAT was compared to that of RT-qPCR using the Allplex SARS-CoV-2/FluA/FluB/RSV Assay (Seegene, Seoul, South Korea). RESULTS: Panbio COVID-19/Flu A&B rapid panel showed the sensitivities of 88.0%, 92.0%, and 100% for SARS-CoV-2, Flu A, and Flu B, respectively, and specificities of 100% for all. The agreements with previously licensed single-plex RATs were shown to be high. In the analysis of variables affecting sensitivity, inappropriate sampling time after symptom onset (STASO) and high cycle threshold (Ct value) were shown to negatively affect the sensi-tivity. CONCLUSIONS: In conclusion, the multiplex RAT is useful for diagnosing SARS-CoV-2 and Flu A/B, but more clinical studies are needed.

Burden of Neurologic Health Care and Incident Neurologic Diagnoses in the Year After COVID-19 or Influenza Hospitalization.

BACKGROUND AND OBJECTIVE: Following the outbreak of viral infections from the severe acute respiratory syndrome coronavirus 2 virus in 2019 (coronavirus disease 2019 [COVID-19]), reports emerged of long-term neurologic sequelae in survivors. To better understand the burden of neurologic health care and incident neurologic diagnoses in the year after COVID-19 vs influenza, we performed an analysis of patient-level data from a large collection of electronic health records (EMR). METHODS: We acquired deidentified data from TriNetX, a global health research network providing access to EMR data. We included individuals aged 18 years or older during index event, defined as hospital-based care for COVID-19 (from April 1, 2020, until November 15, 2021) or influenza (from 2016 to 2019). The study outcomes were subsequent health care encounters over the following year for 6 neurologic diagnoses including migraine, epilepsy, stroke, neuropathy, movement disorders, and dementia. We also created a composite of the 6 diagnoses as an incident event, which we call "incident neurologic diagnoses." We performed a 1:1 complete case nearest-neighbor propensity score match on age, sex, race/ethnicity, marital status, US census region patient residence, preindex years of available data, and Elixhauser comorbidity score. We fit time-to-event models and reported hazard ratios for COVID-19 vs influenza infection. RESULTS: After propensity score matching, we had a balanced cohort of 77,272 individuals with COVID-19 and 77,272 individuals with influenza. The mean age was 51.0 ± 19.7 years, 57.7% were female, and 41.5% were White. Compared with patients with influenza, patients with COVID-19 had a lower risk of subsequent care for migraine (HR 0.645, 95% CI 0.604-0.687), epilepsy (HR 0.783, 95% CI 0.727-0.843), neuropathies (HR 0.567, 95% CI 0.532-0.604), movement disorders (HR 0.644, 95% CI 0.598-0.693), stroke (HR 0.904, 95% CI 0.845-0.967), or dementia (HR 0.931, 95% CI 0.870-0.996). Postinfection incident neurologic diagnoses were observed in 2.79% of the COVID-19 cohort vs 4.91% of the influenza cohort (HR 0.618, 95% CI 0.582-0.657). DISCUSSION: Compared with a matched cohort of adults with a hospitalization or emergency department visit for influenza infection, those with COVID-19 had significantly fewer health care encounters for 6 major neurologic diagnoses over a year of follow-up. Furthermore, we found that COVID-19 infection was associated with a lower risk of an incident neurologic diagnosis in the year after infection.

Influenza a H1N1 infection complicated with encephalopathy and acute pancreatitis: a case report.

This paper reports a case of influenza complicated with influenza associated encephalopathy complicated with acute pancreatitis. This kind of disease is relatively rare, we hope to draw people's attention to it in order to improve early detection and prognosis.

The weekly P25 of the age of the influenza-like illness shows a higher correlation with COVID-19 mortality than rapid tests and could predict the evolution of COVID-19 pandemics in sentinel surveillance, Piura, Perú, 2021.

GOAL: To describe the dynamics of syndromic surveillance of ILI cases in seasonal and COVID-19 pandemic scenarios. METHODOLOGY: A descriptive study of the epidemiological behavior of ILI in the seasonal and COVID-19 pandemic scenarios. Of a sample of 16,231 cases of ILI from 2013 to 2021, the features of cases from 68 weeks before and during the pandemic were selected and compared; weekly endemic channels were built; data fluctuations on the trend of ILI cases were analyzed; and estimated weekly correlations between weekly P25 age, cases confirmed by rapid tests, and mortality from COVID-19. To analyze clinical-epidemiological and mortality data, Student's t test, Mann-Whitney U, Chi2, Spearman's Ro, polynomial, and multinomial regression with a 95% confidence interval were used. RESULTS: During the COVID-19 pandemic, those most affected with ILI were: adults and the elderly; higher median age; autochthonous cases predominated; a lower proportion of other syndromes; delays in seeking care; and a higher rate of pneumonia attack than in the seasonal period (p< 0.01). Rapid tests (serological and antigenic) confirmed 52.7% as COVID-19. Two ILI pandemic waves were seasonally consistent with confirmed COVID-19 cases and district mortality with robust correlation (p<0.01) before and during the pandemic, especially the ILI weekly P25 age, which has a more robust correlation with mortality than ILI and rapid tests (p<0.01) whose endemic channels describe and could predict the evolution of the pandemic (p<0.01). CONCLUSIONS: The pandemic changed the clinical and epidemiological behavior of ILI, and the weekly P25 of age is a more robust indicator to monitor the COVID-19 pandemic than a rapid test and could predict its evolution.

Structural and organizational determinants of the capacity for COVID-19 testing and diagnoses in children: Insights from the 2009 influenza and COVID-19 pandemics.

BACKGROUND: This study explored factors associated with testing and diagnoses for children with COVID-19 at the hospital level and investigated whether the capacity of testing and diagnoses during the 2009 influenza pandemic was associated with that during COVID-19 pandemic. METHODS: In this observational study, we analyzed data obtained from the Japan Medical Data Center database, comprising 4906 medical facilities and 1.7 million infectious disease-related visits among children aged <20 years in 2020-2021. Multivariable generalized linear models were used to explore determinants of testing and diagnoses capacity for COVID-19 and investigate the association between the capacity during the 2009 influenza and COVID-19 pandemics. RESULTS: Public hospitals (adjusted incidence rate ratio [aIRR], 1.52; 95%CI, 1.26-1.82) and university hospitals (aIRR, 1.44; 95%CI, 1.14-1.80) were more likely to perform testing for COVID-19 among children, compared to clinics. The highest testing rate was observed in the department of internal medicine (aIRR, 1.64; 95%CI, 1.32-2.04), followed by pediatrics (aIRR, 1.40; 95%CI, 1.10-1.78) and otolaryngology (aIRR, 1.21; 95%CI, 0.89-1.64). Cubic spline models demonstrated the dose-response relationships between testing rate for influenza in 2009 and testing rates for COVID-19. Compared to the medical facilities in the lowest quartile of testing rate for influenza in 2009, those in the highest quartile were more likely to perform testing for COVID-19 (aIRR, 1.62; 95%CI, 1.43-1.83). CONCLUSIONS: Our study provides insights into the capacity of testing and diagnoses for children, highlighting the dose-response relationship between the 2009 influenza and COVID-19 pandemics, which could be valuable in preparing healthcare systems for future pandemics.

Deep-learning assisted zwitterionic magnetic immunochromatographic assays for multiplex diagnosis of biomarkers.

Magnetic nanoparticle (MNP)-based immunochromatographic tests (ICTs) display long-term stability and an enhanced capability for multiplex biomarker detection, surpassing conventional gold nanoparticles (AuNPs) and fluorescence-based ICTs. In this study, we innovatively developed zwitterionic silica-coated MNPs (MNP@Si-Zwit/COOH) with outstanding antifouling capabilities and effectively utilised them for the simultaneous identification of the nucleocapsid protein (N protein) of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) and influenza A/B. The carboxyl-functionalised MNPs with 10% zwitterionic ligands (MNP@Si-Zwit 10/COOH) exhibited a wide linear dynamic detection range and the most pronounced signal-to-noise ratio when used as probes in the ICT. The relative limit of detection (LOD) values were achieved in 12 min by using a magnetic assay reader (MAR), with values of 0.0062 ng/mL for SARS-CoV-2 and 0.0051 and 0.0147 ng/mL, respectively, for the N protein of influenza A and influenza B. By integrating computer vision and deep learning to enhance the image processing of immunoassay results for multiplex detection, a classification accuracy in the range of 0.9672-0.9936 was achieved for evaluating the three proteins at concentrations of 0, 0.1, 1, and 10 ng/mL. The proposed MNP-based ICT for the multiplex diagnosis of biomarkers holds substantial promise for applications in both medical institutions and self-administered diagnostic settings.

Influenza-like illness surveillance may underestimate the incidence of respiratory syncytial virus in adult outpatients.

OBJECTIVES: Although respiratory syncytial virus (RSV) is a leading cause of acute respiratory infections (ARIs), it is unclear which of the case definitions that prompt swab collection predicts RSV best. We aimed to profile RSV-positive adults and to identify possible RSV case definitions. METHODS: This individual-based pooled analysis was based on influenza-like illness (ILI) surveillance conducted among Italian outpatient adults. All samples were tested for influenza, RSV and other respiratory viruses. RESULTS: RSV was detected in 5.2% of the 1240 ILI adults tested. The prevalence of fever/feverishness was significantly lower (83.3%) in individuals positive for RSV and those negative for both viruses (79.4%) than in influenza-positive subjects (96.2%). Conversely, 98.3% of RSV-positive adults reported cough. Compared with subjects who tested negative, the adjusted relative risk ratio of cough in RSV-positive subjects was much higher than in influenza-positive subjects (6.89 vs 2.79). Using ARI with cough as the RSV case definition increased specificity. CONCLUSION: As fever/feverishness is more common among influenza than RSV cases, ILI-based surveillance may underestimate RSV incidence in adult outpatients. While broad ARI definitions are useful for routine RSV surveillance, their low specificity may hamper vaccine effectiveness studies. The use of further ARI qualifiers like cough increases specificity.

An algorithm for the characterization of influenza A viruses from various host species and environments.

Due to the extensive host range of influenza A viruses, it is difficult to determine the best diagnostic algorithm to efficiently screen samples from a variety of host species for influenza A viruses. While there are some influenza diagnostic algorithms that are specific to host species, to our knowledge, no single algorithm exists for the characterization of influenza A viruses across multiple host species. In this paper, we propose an algorithm that can serve as a guide for screening human, animal, and environmental samples for influenza A viruses of high human and animal health importance.

A cost-consequence analysis of the Xpert Xpress CoV-2/Flu/RSV plus test strategy for the diagnosis of influenza-like illnesses.

AIMS: Influenza-like illnesses (ILI) affect millions each year in the United States (US). Determining definitively the cause of symptoms is important for patient management. Xpert Xpress CoV-2/Flu/RSV plus (Xpert Xpress) is a rapid, point-of-care (POC), multiplex real-time polymerase chain reaction (RT-PCR) test intended for the simultaneous qualitative detection and differentiation of SARS-CoV-2, influenza A/B, and respiratory syncytial virus (RSV). The objective of our analysis was to develop a cost-consequence model (CCM) demonstrating the clinico-economic impacts of implementing PCR testing with Xpert Xpress compared to current testing strategies. METHODS: A decision tree model, with a 1-year time horizon, was used to compare testing with Xpert Xpress alone to antigen POC testing and send-out PCR strategies in the US outpatient setting from a payer perspective. A hypothetical cohort of 1,000,000 members was modeled, a portion of whom develop symptomatic ILIs and present to an outpatient care facility. Our main outcome measure is cost per correct treatment course. RESULTS: The total cost per correct treatment course was $1,131 for the Xpert Xpress strategy compared with a range of $3,560 to $5,449 in comparators. POC antigen testing strategies cost more, on average, than PCR strategies. LIMITATIONS: Simplifying model assumptions were used to allow for modeling ease. In clinical practice, treatment options, costs, and diagnostic test sensitivity and specificity may differ from what is included in the model. Additionally, the most recent incidence and prevalence data was used within the model, which is not reflective of historical averages due to the SARS-CoV-2 pandemic. CONCLUSION: The Xpert Xpress CoV-2/Flu/RSV plus test allows for rapid and accurate diagnostic results, leading to reductions in testing costs and downstream healthcare resource utilization compared to other testing strategies. Compared to POC antigen testing strategies, PCR strategies were more efficient due to improved diagnostic accuracy and reduced use of confirmatory testing.

Influenza-like illness symptoms due to endemic human coronavirus reinfections are not influenced by the length of the interval separating reinfections.

After 3 years of its introduction to humans, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared as endemic. Little is known about the severity of the disease manifestation that future infections may cause, especially when reinfections occur after humoral immunity from a previous infection or vaccination has waned. Such knowledge could inform policymakers regarding the frequency of vaccination. Reinfections by endemic human coronaviruses (HCoVs) can serve as a model system for SARS-CoV-2 endemicity. We monitored 44 immunocompetent male adults with blood sampling every 6 months (for 17 years), for the frequency of HCoV (re-)infections, using rises in N-antibodies of HCoV-NL63, HCoV-29E, HCoV-OC43, and HCoV-HKU1 as markers of infection. Disease associations during (re-)infections were examined by comparison of self-reporting records of influenza-like illness (ILI) symptoms, every 6 months, by all participants. During 8,549 follow-up months, we found 364 infections by any HCoV with a median of eight infections per person. Symptoms more frequently reported during HCoV infection were cough, sore throat, and myalgia. Two hundred fifty-one of the 364 infections were species-specific HCoV-reinfections, with a median interval of 3.58 (interquartile range 1.92-5.67) years. The length of the interval between reinfections-being either short or long-had no influence on the frequency of reporting ILI symptoms. All HCoV-NL63, HCoV-229E, HCoV-OC43, and HCoV-HKU1 (re-)infections are associated with the reporting of ILIs. Importantly, in immunocompetent males, these symptoms are not influenced by the length of the interval between reinfections. IMPORTANCE: Little is known about the disease following human coronavirus (HCoV) reinfection occurring years after the previous infection, once humoral immunity has waned. We monitored endemic HCoV reinfection in immunocompetent male adults for up to 17 years. We found no influence of reinfection interval length in the disease manifestation, suggesting that immunocompetent male adults are adequately protected against future HCoV infections.

Accurate detection of influenza A virus by use of a novel cross-priming isothermal amplification-based point-of-care assay.

Influenza virus is known to cause respiratory tract infections of varying severity in individuals of all ages. The EasyNAT Rapid Flu assay is a newly developed in vitro diagnostic test that employs cross-priming isothermal amplification (CPA) to detect and differentiate influenza A and B viruses in human nasopharyngeal (NP) swabs. The aim of this study is to determine the performance characteristics of the EasyNAT Rapid Flu assay for rapid detection of influenza virus. The limit of detection (LOD) and cross-reactivity of the EasyNAT Rapid Flu assay were assessed. The clinical performance of the assay was evaluated using NP swab samples that were tested with real-time reverse-transcription polymerase chain reaction (RT-PCR) and Xpert Xpress Flu/RSV assay. The LOD for the detection of influenza A and B using the EasyNAT Rapid Flu assay was found to be 500 copies/mL. Furthermore, the assay exhibited no cross-reactivity with other common respiratory viruses tested. For the 114 NP swab samples tested for influenza A using both the EasyNAT Rapid Flu assay and real-time RT-PCR, the two assays demonstrated a high level of agreement (κ = 0.963, P < 0.001), with a positive percentage agreement (PPA) of 97.7% and a negative percentage agreement (NPA) of 98.6%. Similarly, for the 43 NP swab samples tested for influenza A and B using both the EasyNAT Rapid Flu assay and Xpert Xpress Flu/RSV assay, the two assays showed a high level of agreement (κ = 0.933, P < 0.001), with the overall rate of agreement (ORA) of 97.7% for influenza A and 100% for influenza B. The EasyNAT Rapid Flu assay demonstrates excellent performance in the detection of influenza A, highlighted by its strong agreement with RT-PCR-based assays.IMPORTANCEThe newly developed EasyNAT Rapid Flu assay is an innovative cross-priming isothermal amplification-based method designed for detecting influenza A and B viruses at point-of-care settings. This study aims to thoroughly assess the analytical and clinical performance of the assay, offering valuable insights into its potential advantages and limitations. The findings of this research hold significant implications for clinical practice.

Clinical and epidemiological evaluation of influenza and SARS-CoV-2 coinfected cases.

INTRODUCTION: Coinfection of COVID-19 with influenza pathogens, may complicate the diagnosis, treatment, and prognosis, which is a new concern. This study aims to evaluate COVID-19 and influenza coinfected cases during the flu season, while the SARS-CoV-2 pandemic continues. METHODOLOGY: The study was conducted between November 2021 and January 2022. A total of 1987 (1752 outpatients, 235 inpatients) patients were included, and 44 simultaneous COVID-19 and influenza laboratory-confirmed diagnoses. RESULTS: During the study period, 1553 patients were diagnosed with COVID-19, 390 influenza, and 44 were diagnosed with coinfection. The incidence of coinfected cases was 2.2% (n = 44) in all patients, When coinfected cases were examined, there was a statistically significant difference between the disease duration in the inpatients (19.86 ± 10.78 days) and the disease duration in the outpatients (7.63 ± 2.25 days) (p < 0.05). 31.8% (n = 14) of coinfected cases were hospitalized, and the mortality rate was 50.0% (n = 7) in hospitalized patients. CONCLUSIONS: Coinfection with SARS-CoV-2 and Influenza was not uncommon. Data on coinfected cases are limited in the literature. The coinfection with SARS-CoV-2 and influenza A should be considered in patients with complaints such as fever, myalgia, weakness, shortness of breath, and cough during the flu season. Using the diagnostic test showing two diseases in a single sample may contribute to protecting patient and community health in follow-up and treatment.