Effectiveness of inactivated influenza vaccine against laboratory-confirmed influenza among Chinese elderly: a test-negative design.

BACKGROUND: Evidence on the effectiveness of influenza vaccination in the elderly is limited, and results are controversial. There are also few reports from China. METHODS: We conducted a test-negative case-control study design to estimate influenza vaccine effectiveness (VE) against laboratory-confirmed influenza-associated visits among elderly (aged ≥ 60 years) across four influenza seasons in Ningbo, China, from 2018 to 19 to 2021-22. Influenza-positive cases and negative controls were randomly matched in a 1:1 ratio according to age, sex, hospital, and date of influenza testing. We used logistic regression models to compare vaccination odds ratios (ORs) in cases to controls. We calculated the VE as [100% × (1-adjusted OR)] and calculated the 95% confidence interval (CI) around the estimate. RESULTS: A total of 30,630 elderly patients tested for influenza with virus nucleic acid or antigen during the study period. After exclusions, we included 1 825 influenza-positive cases and 1 825 influenza-negative controls. Overall, the adjusted VE for influenza-related visits was 63.5% (95% CI, 56.3-69.5%), but varied by season. Influenza VE was 59.8% (95% CI, 51.5-66.7%) for influenza A and 89.6% (95% CI, 77.1-95.3%) for influenza B. The VE for ages 60-69 and 70-79 was 65.2% (95% CI, 55.4-72.9%) and 69.8% (95% CI, 58.7-77.9%), respectively, but only 45.4% (95% CI, 6.2-68.2%) for ages 80 and over. CONCLUSIONS: Standard-dose inactivated influenza vaccine has shown good protection in the elderly in China. However, protection may not be satisfactory in people aged 80 years and older.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings. AIM: This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI. METHODS: This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance. RESULTS: Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results. CONCLUSION: This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

Diagnostic value of routine blood tests in differentiating between SARS-CoV-2, influenza A, and RSV infections in hospitalized children: a retrospective study.

BACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), influenza A, and respiratory syncytial virus (RSV) infections have similar modes of transmission and clinical symptoms. There is a need to identify simple diagnostic indicators to distinguish these three infections, particularly for community hospitals and low- and middle-income countries that lack nucleic acid detection kits. This study used clinical data to assess the diagnostic value of routine blood tests in differentiating between SARS-CoV-2, influenza A, and RSV infections in children. METHODS: A total of 1420 children treated at the Hangzhou Children's Hospital between December 2022 and June 2023 were enrolled in this study, of whom 351 had SARS-CoV-2, 671 had influenza, and 398 had RSV. In addition, 243 healthy children were also collected. The blood test results of SARS-CoV-2 patients were compared to those of patients with influenza A and RSV and the healthy controls. The area under the receiver operating characteristic curve (AUC-ROC) was employed to evaluate each blood parameter's diagnostic value. RESULTS: Children with SARS-CoV-2 exhibited notably elevated levels of white blood cell (WBC) count, platelet (PLT) count, neutrophil count, and neutrophil-to-lymphocyte ratio (NLR) compared to influenza A patients (P < 0.05). In contrast, SARS-CoV-2 patients exhibited a decrease in the mean platelet volume to platelet count ratio (MPV/PLT) and the lymphocyte-to-monocyte ratio (LMR) when compared to other individuals (P < 0.05). These parameters had an AUC between 0.5 and 0.7. Compared to patients with RSV, SARS-CoV-2 patients had significantly higher MPV/PLT and significantly lower WBC, lymphocyte, PLT, LMR, and lymphocyte multiplied by platelet (LYM*PLT) values (P < 0.05). However, only LYM*PLT had an acceptable diagnostic value above 0.7 for all age groups. Compared to healthy children, children with COVID-19 exhibited elevated NLR and MPV/PLT levels, alongside decreased lymphocyte, PLT, LMR, and LYM*PLT values. (P < 0.05). The AUC of the LMR, LYM*PLT, and PLT were above 0.7 in all age groups, indicating promising diagnostic values. CONCLUSIONS: The routine blood parameters among patients with COVID-19, influenza A, and RSV differ significantly early in the disease and could be used by clinicians to discriminate between the 3 types of infection.

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.

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.

Estimating the incidence of COVID-19, influenza and respiratory syncytial virus infection in three regions of Queensland, Australia, winter 2022: findings from a novel longitudinal testing-based sentinel surveillance programme.

OBJECTIVE: The 2022 Australian winter was the first time that COVID-19, influenza and respiratory syncytial virus (RSV) were circulating in the population together, after two winters of physical distancing, quarantine and borders closed to international travellers. We developed a novel surveillance system to estimate the incidence of COVID-19, influenza and RSV in three regions of Queensland, Australia. DESIGN: We implemented a longitudinal testing-based sentinel surveillance programme. Participants were provided with self-collection nasal swabs to be dropped off at a safe location at their workplace each week. Swabs were tested for SARS-CoV-2 by PCR. Symptomatic participants attended COVID-19 respiratory clinics to be tested by multiplex PCR for SARS-CoV-2, influenza A and B and RSV. Rapid antigen test (RAT) results reported by participants were included in the analysis. SETTING AND PARTICIPANTS: Between 4 April 2022 and 3 October 2022, 578 adults were recruited via their workplace. Due to rolling recruitment, withdrawals and completion due to positive COVID-19 results, the maximum number enrolled in any week was 423 people. RESULTS: A total of 4290 tests were included. Participation rates varied across the period ranging from 25.9% to 72.1% of enrolled participants. The total positivity of COVID-19 was 3.3%, with few influenza or RSV cases detected. Widespread use of RAT may have resulted in few symptomatic participants attending respiratory clinics. The weekly positivity rate of SARS-CoV-2 detected during the programme correlated with the incidence of notified cases in the corresponding communities. CONCLUSION: This testing-based surveillance programme could estimate disease trends and be a useful tool in settings where testing is less common or accessible. Difficulties with recruitment meant the study was underpowered. The frontline sentinel nature of workplaces meant participants were not representative of the general population but were high-risk groups providing early warning of disease.

Accuracy of Influenza ICD-10 Diagnosis Codes in Identifying Influenza Illness in Children.

Importance: Studies of influenza in children commonly rely on coded diagnoses, yet the ability of International Classification of Diseases, Ninth Revision codes to identify influenza in the emergency department (ED) and hospital is highly variable. The accuracy of newer International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes to identify influenza in children is unknown. Objective: To determine the accuracy of ICD-10 influenza discharge diagnosis codes in the pediatric ED and inpatient settings. Design, Setting, and Participants: Children younger than 18 years presenting to the ED or inpatient settings with fever and/or respiratory symptoms at 7 US pediatric medical centers affiliated with the Centers for Disease Control and Prevention-sponsored New Vaccine Surveillance Network from December 1, 2016, to March 31, 2020, were included in this cohort study. Nasal and/or throat swabs were collected for research molecular testing for influenza, regardless of clinical testing. Data, including ICD-10 discharge diagnoses and clinical testing for influenza, were obtained through medical record review. Data analysis was performed in August 2023. Main Outcomes and Measures: The accuracy of ICD-10-coded discharge diagnoses was characterized using molecular clinical or research laboratory test results as reference. Measures included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Estimates were stratified by setting (ED vs inpatient) and age (0-1, 2-4, and 5-17 years). Results: A total of 16 867 children in the ED (median [IQR] age, 2.0 [0.0-4.0] years; 9304 boys [55.2%]) and 17 060 inpatients (median [IQR] age, 1.0 [0.0-4.0] years; 9798 boys [57.4%]) were included. In the ED, ICD-10 influenza diagnoses were highly specific (98.0%; 95% CI, 97.8%-98.3%), with high PPV (88.6%; 95% CI, 88.0%-89.2%) and high NPV (85.9%; 95% CI, 85.3%-86.6%), but sensitivity was lower (48.6%; 95% CI, 47.6%-49.5%). Among inpatients, specificity was 98.2% (95% CI, 98.0%-98.5%), PPV was 82.8% (95% CI, 82.1%-83.5%), sensitivity was 70.7% (95% CI, 69.8%-71.5%), and NPV was 96.5% (95% CI, 96.2%-96.9%). Accuracy of ICD-10 diagnoses varied by patient age, influenza season definition, time between disease onset and testing, and clinical setting. Conclusions and Relevance: In this large cohort study, influenza ICD-10 discharge diagnoses were highly specific but moderately sensitive in identifying laboratory-confirmed influenza; the accuracy of influenza diagnoses varied by clinical and epidemiological factors. In the ED and inpatient settings, an ICD-10 diagnosis likely represents a true-positive influenza case.

Antibody-independent surface plasmon resonance assays for influenza vaccine quality control.

Surface plasmon resonance (SPR)-based biosensors have emerged as a powerful platform for bioprocess monitoring due to their ability to detect biointeractions in real time, without the need for labeling. Paramount for the development of a robust detection platform is the immobilization of a ligand with high specificity and affinity for the in-solution species of interest. Following the 2009 H1N1 pandemic, much effort has been made toward the development of quality control platforms for influenza A vaccine productions, many of which have employed SPR for detection. Due to the rapid antigenic drift of influenza's principal surface protein, hemagglutinin, antibodies used for immunoassays need to be produced seasonally. The production of these antibodies represents a 6-8-week delay in immunoassay and, thus, vaccine availability. This review focuses on SPR-based assays that do not rely on anti-HA antibodies for the detection, characterization, and quantification of influenza A in bioproductions and biological samples. KEY POINTS: • The single radial immunodiffusion assay (SRID) has been the gold standard for the quantification of influenza vaccines since 1979. Due to antigenic drift of influenza's hemagglutinin protein, new antibody reagents for the SRID assay must be produced each year, requiring 6-8 weeks. The resulting delay in immunoassay availability is a major bottleneck in the influenza vaccine pipeline. This review highlights ligand options for the detection and quantification of influenza viruses using surface plasmon resonance biosensors.

Severe A(H1N1)pdm09 influenza acute encephalopathy outbreak in children in Tuscany, Italy, December 2023 to January 2024.

A severe outbreak of influenza A(H1N1pdm09) infection in seven children (median age: 52 months) occurred between December 2023 and January 2024 in Tuscany, Italy. Clinical presentation ranged from milder encephalopathy to acute necrotizing encephalopathy (ANE) with coma and multiorgan failure; one child died. This report raises awareness for clinicians to identify and treat early acute encephalopathy caused by H1N1 influenza and serves as a reminder of severe presentations of influenza in young children and the importance of vaccination.

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.

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.

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.

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.

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.