Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration.

BACKGROUND: Severe influenza is a serious global health issue that leads to prolonged hospitalization and mortality on a significant scale. The pathogenesis of this infectious disease is poorly understood. Therefore, this study aimed to identify the key genes associated with severe influenza patients necessitating invasive mechanical ventilation. METHODS: The current study utilized two publicly accessible gene expression profiles (GSE111368 and GSE21802) from the Gene Expression Omnibus database. The research focused on identifying the genes exhibiting differential expression between severe and non-severe influenza patients. We employed three machine learning algorithms, namely the Least Absolute Shrinkage and Selection Operator regression model, Random Forest, and Support Vector Machine-Recursive Feature Elimination, to detect potential key genes. The key gene was further selected based on the diagnostic performance of the target genes substantiated in the dataset GSE101702. A single-sample gene set enrichment analysis algorithm was applied to evaluate the participation of immune cell infiltration and their associations with key genes. RESULTS: A total of 44 differentially expressed genes were recognized; among them, we focused on 10 common genes, namely PCOLCE2, HLA_DPA1, LOC653061, TDRD9, MPO, HLA_DQA1, MAOA, S100P, RAP1GAP, and CA1. To ensure the robustness of our findings, we employed overlapping LASSO regression, Random Forest, and SVM-RFE algorithms. By utilizing these algorithms, we were able to pinpoint the aforementioned 10 genes as potential biomarkers for distinguishing between both cases of influenza (severe and non-severe). However, the gene HLA_DPA1 has been recognized as a crucial factor in the pathological condition of severe influenza. Notably, the validation dataset revealed that this gene exhibited the highest area under the receiver operating characteristic curve, with a value of 0.891. The use of single-sample gene set enrichment analysis has provided valuable insights into the immune responses of patients afflicted with severe influenza that have further revealed a categorical correlation between the expression of HLA_DPA1 and lymphocytes. CONCLUSION: The findings indicated that the HLA_DPA1 gene may play a crucial role in the immune-pathological condition of severe influenza and could serve as a promising therapeutic target for patients infected with severe influenza.

Effectiveness of prolonged versus standard-course of oseltamivir in critically ill patients with severe influenza infection: A multicentre cohort study.

The aim of this study is to investigate the effectiveness of prolonged versus standard course oseltamivir treatment among critically ill patients with severe influenza. A retrospective study of a prospectively collected database including adults with influenza infection admitted to 184 intensive care units (ICUs) in Spain from 2009 to 2018. Prolonged oseltamivir was defined if patients received the treatment beyond 5 days, whereas the standard-course group received oseltamivir for 5 days. The primary outcome was all-cause ICU mortality. Propensity score matching (PSM) was constructed, and the outcome was investigated through Cox regression and RCSs. Two thousand three hundred and ninety-seven subjects were included, of whom 1943 (81.1%) received prolonged oseltamivir and 454 (18.9%) received standard treatment. An optimal full matching algorithm was performed by matching 2171 patients, 1750 treated in the prolonged oseltamivir group and 421 controls in the standard oseltamivir group. After PSM, 387 (22.1%) patients in the prolonged oseltamivir and 119 (28.3%) patients in the standard group died (p = 0.009). After adjusting confounding factors, prolonged oseltamivir significantly reduced ICU mortality (odds ratio [OR]: 0.53, 95% confidence interval [CI]: 0.40-0.69). Prolonged oseltamivir may have protective effects on survival at Day 10 compared with a standard treatment course. Sensitivity analysis confirmed these findings. Compared with standard treatment, prolonged oseltamivir was associated with reduced ICU mortality in critically ill patients with severe influenza. Clinicians should consider extending the oseltamivir treatment duration to 10 days, particularly in higher-risk groups of prolonged viral shedding. Further randomized controlled trials are warranted to confirm these findings.

Genetic analysis of cuproptosis subtypes and immunological features in severe influenza.

The mechanisms regulating cuproptosis in severe influenza are still unknown. We aimed to identify the molecular subtypes of cuproptosis and immunological characteristics associated with severe influenza in patients requiring invasive mechanical ventilation (IMV). The expression of cuproptosis modulatory factors and immunological characteristics of these patients were analyzed using the public datasets (GSE101702, GSE21802, and GSE111368) from the Gene Expression Omnibus (GEO). Seven cuproptotic-associated genes (ATP7B, ATP7A, FDX1, LIAS, DLD, MTF1, DBT) related to active immune responses were identified in patients suffering from severe and non-severe influenza and two cuproptosis-associated molecular subtypes were discovered in severe influenza patients. Singe-set gene set expression analysis (SsGSEA) indicated that compared with subtype 2, subtype 1 was characterized by reduced adaptive cellular immune responses and increased neutrophil activation. Gene set variation assessment revealed that cluster-specific differentially expressed genes (DEGs) in subtype 1 were involved in autophagy, apoptosis, oxidative phosphorylation, and T cell, immune, and inflammatory responses, amongst others. The random forest (RF) model revealed the most differentiating efficiency with relatively small residual and root mean square error and an increased area under the curve value (AUC = 0.857). Lastly, a five-gene-based RF model (CD247, GADD45A, KIF1B, LIN7A, HLA_DPA1) was established, which showed satisfactory efficiency in the test datasets GSE111368 (AUC = 0.819). Nomogram calibration and decision curve analysis demonstrated its accuracy for the prediction of severe influenza. This study suggests that cuproptosis might be associated with the immunopathology of severe influenza. Additionally, an efficient model for the prediction of cuproptosis subtypes was developed which will contribute to the prevention and treatment of severe influenza patients needing IMV.

High-Dose Influenza Vaccine Is Associated With Reduced Mortality Among Older Adults With Breakthrough Influenza Even When There Is Poor Vaccine-Strain Match.

BACKGROUND: High-dose (HD) influenza vaccine offers improved protection from influenza virus infection among older adults compared with standard-dose (SD) vaccine. Here, we explored whether HD vaccine attenuates disease severity among older adults with breakthrough influenza. METHODS: This was a retrospective cohort study of US claims data for influenza seasons 2016-2017, 2017-2018, and 2018-2019, defined as 1 October through 30 April, among adults aged ≥65 years. After adjusting the different cohorts for the probability of vaccination conditional on patients' characteristics, we compared 30-day mortality rate post-influenza among older adults who experienced breakthrough infection after receipt of HD or SD influenza vaccines and among those not vaccinated (NV). RESULTS: We evaluated 44 456 influenza cases: 23 109 (52%) were unvaccinated, 15 037 (33.8%) received HD vaccine, and 6310 (14.2%) received SD vaccine. Significant reductions in mortality rates among breakthrough cases were observed across all 3 seasons for HD vs NV, ranging from 17% to 29% reductions. A significant mortality reduction of 25% was associated with SD vaccination vs NV in the 2016-2017 season when there was a good match between circulating influenza viruses and selected vaccine strains. When comparing HD vs SD cohorts, mortality reductions were higher among those who received HD in the last 2 seasons when mismatch between vaccine strains and circulating H3N2 viruses was documented, albeit not significant. CONCLUSIONS: HD vaccination was associated with lower post-influenza mortality among older adults with breakthrough influenza, even during seasons when antigenically drifted H3N2 circulated. Improved understanding of the impact of different vaccines on attenuating disease severity is warranted when assessing vaccine policy recommendations.

Development of a nomogram for severe influenza in previously healthy children: a retrospective cohort study.

OBJECTIVE: We aimed to develop a nomogram to predict the risk of severe influenza in previously healthy children. METHODS: In this retrospective cohort study, we reviewed the clinical data of 1135 previously healthy children infected with influenza who were hospitalized in the Children's Hospital of Soochow University between 1 January 2017 and 30 June 2021. Children were randomly assigned in a 7:3 ratio to a training or validation cohort. In the training cohort, univariate and multivariate logistic regression analyses were used to identify risk factors, and a nomogram was established. The validation cohort was used to evaluate the predictive ability of the model. RESULT: Wheezing rales, neutrophils, procalcitonin > 0.25 ng/mL, Mycoplasma pneumoniae infection, fever, and albumin were selected as predictors. The areas under the curve were 0.725 (95% CI: 0.686-0.765) and 0.721 (95% CI: 0.659-0.784) for the training and validation cohorts, respectively. The calibration curve showed that the nomogram was well calibrated. CONCLUSION: The nomogram may predict the risk of severe influenza in previously healthy children.

The origin of regulatory from the effector cells in LAG-3-marked Th1 immunity against severe influenza virus infection.

In severe respiratory virus infections, including influenza, an exaggerated host immune response has been linked to the severe disease and death. Control of the overwhelming immune response is thus essential. Efforts with broad-spectrum immunosuppressive agents such as steroids are disappointing. A better understanding of host immune response using animal experimental system is required to avoid undesired outcome of experimental manipulation. Following severe influenza virus infection in influenza hemagglutinin antigen-specific transgenic mouse experimental model, step-wise evolving cells from a pool of naïve hemagglutinin-specific CD4+ T cells were studied for phenotypic, genomic, and functional characterization in vivo. Naïve CD4+ T cells respond with Th1 commitment in the absolute majority. They first develop into LAG-3Med IFN-γ-secreting Th1 effectors and then evolve into LAG-3High IFN-γ-not-secreting regulators with increasing LAG-3 expression upon continuous activation and cell division. The LAG-3Med IFN-γ-secreting effectors contribute to inflammation, boost inflammatory response of cognate antigen-specific CD8+ T cells, and aggravate the disease despite facilitated virus clearance. In contrast, LAG-3High regulators do not contribute to inflammation, suppress CD8+ T cell inflammatory response, alleviate lung pathology, and ameliorate the disease with preserved virus clearance. Moderated CD8+ T cells retain proliferative capacity, and persist beyond virus clearance. Such moderation is distinct from Foxp-3+ regulator-mediated suppression, which suppresses proliferative and inflammatory responses of the CD8+ T cells and impairs virus clearance with inflammation alleviation. Origin of regulatory from the effector cells of LAG-3-marked Th1 immunity alleviates lung inflammation without impairment of virus eradication.

Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation.

BACKGROUND: Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA). RESULTS: In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response. CONCLUSION: We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.

Cycling and activated CD8+ T lymphocytes and their association with disease severity in influenza patients.

BACKGROUND: T cell lymphopenia was a significant characteristic of severe influenza infection and it was associated with the functional changes of T cells. It is necessary to clarify the T cells characteristics of kinetic changes and their correlation with disease severity. METHODS: In a cohort of hospitalized influenza patients with varying degrees of severity, we characterized lymphocyte populations using flow cytometry. RESULTS: The numbers of cycling (Ki67+) T cells at the acute phase of severe influenza were higher, especially in the memory (CD45RO+) T cell subsets. T cells from hospitalized influenza patients also had significantly higher levels of the exhausted marker PD-1. Cycling status of T cells was associated with T cell activation during the acute phase of influenza infection. The recruitment of cycling and activated (CD38+HLA-DR+) CD8+ T cells subset is delayed in severe influenza patients. CONCLUSIONS: The increased numbers of cycling memory (Ki67+CD45RO+) T cells subsets and delayed kinetics of activated (CD38+HLA-DR+) CD8+ T cells, could serve as possible biological markers for disease severity.

Rapid influenza molecular testing in secondary care and influenza surveillance in England: Any impact?

INTRODUCTION: The use of rapid molecular testing for influenza diagnosis is becoming increasingly popular. Used at the point of care or in a clinical laboratory, these tests detect influenza A and B viruses, though many do not distinguish between influenza A subtypes. The UK Severe Influenza Surveillance System (USISS) collects surveillance data on laboratory-confirmed influenza admissions to secondary care in England. This study set out to understand how rapid influenza molecular testing was being used and how it might influence the availability of subtyping data collected on influenza cases admitted to secondary care in England. METHODS: At the end of the 2017/2018 and 2018/2019 influenza seasons, a questionnaire was sent to all National Health Service Hospital Trusts in England to evaluate the use of rapid influenza testing. Surveillance data collected through USISS was analysed from 2011/2012 to 2020/2021. RESULTS: Of responding trusts, 42% (13/31) in 2017/2018 and 55% (9/17) in 2018/2019 used rapid influenza molecular tests, either alone or in combination with other testing. The majority of rapid tests used did not subtype the influenza A result, and limited follow-up testing occurred. Surveillance data showed significant proportions of influenza A hospital and intensive care unit/high dependency unit admissions without subtyping information, increasing by approximately 35% between 2012/2013 and 2020/2021. CONCLUSIONS: The use of rapid influenza molecular tests is a likely contributing factor to the large proportion of influenza A hospitalisations in England that were unsubtyped. Given their clear clinical advantages, further work must be done to reinforce these data for public health through integrated genomic surveillance.

Rates of Severe Influenza-Associated Outcomes Among Older Adults Living With Diabetes-Influenza Hospitalization Surveillance Network (FluSurv-NET), 2012-2017.

Background: Diabetes mellitus (DM) is common among older adults hospitalized with influenza, yet data are limited on the impact of DM on risk of severe influenza-associated outcomes. Methods: We included adults aged ≥65 years hospitalized with influenza during 2012-2013 through 2016-2017 from the Influenza Hospitalization Surveillance Network (FluSurv-NET), a population-based surveillance system for laboratory-confirmed influenza-associated hospitalizations conducted in defined counties within 13 states. We calculated population denominators using the Centers for Medicare and Medicaid Services county-specific DM prevalence estimates and National Center for Health Statistics population data. We present pooled rates and rate ratios (RRs) of intensive care unit (ICU) admission, pneumonia diagnosis, mechanical ventilation, and in-hospital death for persons with and without DM. We estimated RRs and 95% confidence intervals (CIs) using meta-analysis with site as a random effect in order to control for site differences in the estimates. Results: Of 31 934 hospitalized adults included in the analysis, 34% had DM. Compared to those without DM, adults with DM had higher rates of influenza-associated hospitalization (RR, 1.57 [95% CI, 1.43-1.72]), ICU admission (RR, 1.84 [95% CI, 1.67-2.04]), pneumonia (RR, 1.57 [95% CI, 1.42-1.73]), mechanical ventilation (RR, 1.95 [95% CI, 1.74-2.20]), and in-hospital death (RR, 1.48 [95% CI, 1.23-1.80]). Conclusions: Older adults with DM have higher rates of severe influenza-associated outcomes compared to those without DM. These findings reinforce the importance of preventing influenza virus infections through annual vaccination, and early treatment of influenza illness with antivirals in older adults with DM.

Evaluation of the peripheral blood T and B cell subsets and IRF-7 variants in adult patients with severe influenza virus infection.

BACKGROUND AND AIMS: Influenza virus is one of the leading infections causing death among human being. Despite known risks, primary immune deficiency due to Interferon Regulatory Factor-7 (IRF7) gene defect was reported as a possible cause of the risk factors for complicated influenza. We aimed to investigate the changes in peripheral T and B cell subsets in adult patients with severe seasonal influenza virus infection and the investigation of variants of IRF7 gene. METHODS: In this study, 32 patients, hospitalized due to influenza infection-related acute respiratory failure were included. RESULTS: The median age of the patients was 76 years (26-96), and 13/32 (40.6%) were in the intensive care unit. Central memory Th, effector memory Th, TEMRA Th, cytotoxic T lymphocytes (CTL), central memory CTL of the patients were found to be increased, naive CTL were decreased. There was a significant increase in the percentage of effector memory Th, and a decrease in the percentage of naive CTL in patients ≥65 years-old compared to patients <65 years old (P = .039, and P = .017, respectively). IRF7 gene analysis revealed two different nucleotide changes in three patients; c.535 A > G; p.Lys179Glu (K179E) and c584A > T; p.His195Leu (H195L), located in the fourth exon of the IRF7 gene. DISCUSSION: The increases in central and effector memory Th, central memory CTL and decrease of naive CTLs may be secondary to the virus infection. K179E (rs1061502) and H195L (rs139709725) variants were not reported to be related with susceptibility to an infection yet. It is conceivable to investigate for novel variants in other genes related to antiviral immunity.

Invasive pulmonary aspergillosis among intubated patients with SARS-CoV-2 or influenza pneumonia: a European multicenter comparative cohort study.

BACKGROUND: Recent multicenter studies identified COVID-19 as a risk factor for invasive pulmonary aspergillosis (IPA). However, no large multicenter study has compared the incidence of IPA between COVID-19 and influenza patients. OBJECTIVES: To determine the incidence of putative IPA in critically ill SARS-CoV-2 patients, compared with influenza patients. METHODS: This study was a planned ancillary analysis of the coVAPid multicenter retrospective European cohort. Consecutive adult patients requiring invasive mechanical ventilation for > 48 h for SARS-CoV-2 pneumonia or influenza pneumonia were included. The 28-day cumulative incidence of putative IPA, based on Blot definition, was the primary outcome. IPA incidence was estimated using the Kalbfleisch and Prentice method, considering extubation (dead or alive) within 28 days as competing event. RESULTS: A total of 1047 patients were included (566 in the SARS-CoV-2 group and 481 in the influenza group). The incidence of putative IPA was lower in SARS-CoV-2 pneumonia group (14, 2.5%) than in influenza pneumonia group (29, 6%), adjusted cause-specific hazard ratio (cHR) 3.29 (95% CI 1.53-7.02, p = 0.0006). When putative IPA and Aspergillus respiratory tract colonization were combined, the incidence was also significantly lower in the SARS-CoV-2 group, as compared to influenza group (4.1% vs. 10.2%), adjusted cHR 3.21 (95% CI 1.88-5.46, p < 0.0001). In the whole study population, putative IPA was associated with significant increase in 28-day mortality rate, and length of ICU stay, compared with colonized patients, or those with no IPA or Aspergillus colonization. CONCLUSIONS: Overall, the incidence of putative IPA was low. Its incidence was significantly lower in patients with SARS-CoV-2 pneumonia than in those with influenza pneumonia. Clinical trial registration The study was registered at ClinicalTrials.gov, number NCT04359693 .

Validation of International Classification of Diseases criteria to identify severe influenza hospitalizations.

In this cohort study of hospitalized patients with linked medical record data, we developed International Classification of Diseases (ICD) criteria that accurately identified laboratory-confirmed, severe influenza hospitalizations (positive predictive value [PPV] 80%, 95% confidence interval [CI] 71-87%), which we validated through medical record documentation. These criteria identify patients with clinically important influenza illness outcomes to inform evaluation of preventive and therapeutic interventions and public health policy recommendations.

Evaluation of efficacy and safety of Qiangzhu-qinggan formula as an adjunctive therapy in adult patients with severe influenza: study protocol for a randomized parallel placebo-controlled double-blind multicenter trial.

BACKGROUND: Influenza can fall into three categories according to severity: mild influenza, severe influenza, and critical influenza. Severe influenza can result in critical illness and sometimes death particularly in patients with comorbidities, advanced age, or pregnancy. Neuraminidase inhibitors (NAIs) are the only antiviral drugs in widespread use for influenza. However, the effectiveness of NAIs against severe influenza is uncertain. New effective drugs or regimens are therefore urgently needed. Qiangzhu-qinggan (QZQG) formula has been found to be effective against influenza virus infection during long-term application in China, which lacks support of evidence-based clinical trial till now. This study is designed to assess the efficacy and safety of QZQG formula as an adjuvant therapy in adult patients with severe influenza. METHODS: This protocol is drawn up in accordance with the SPIRIT guidelines and CONSORT Extension for Chinese herbal medicine formulas. This is a randomized, placebo-controlled, double-blind, multicenter trial. Two hundred twenty-eight adults with severe influenza are randomly assigned in a 1:1 ratio to QZQG or placebo for 7 days. All participants need to receive 1 day of screening before randomization, 7 days of intervention, and 21 days of observation after randomization. The primary outcome is the proportion of clinical improvement, defined as the proportion of patients who met the criteria of 3 points or less in the seven-category ordinal scale or 2 points or less in National Early Warning Score 2 within 7 days after randomization. DISCUSSION: This is the first randomized, controlled, parallel, double-blind clinical trial to evaluate the efficacy and safety of traditional Chinese herbal formula granules as an adjuvant therapy in adult patients with severe influenza. This study aims to redefine the value of traditional Chinese herbal medicines in the treatment of virus-related respiratory infectious diseases and serves as an example of evidence-based clinical trials of other Chinese herbal medicines.

The Weight of Obesity in Immunity from Influenza to COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged in December 2019 and rapidly outspread worldwide endangering human health. The coronavirus disease 2019 (COVID-19) manifests itself through a wide spectrum of symptoms that can evolve to severe presentations as pneumonia and several non-respiratory complications. Increased susceptibility to COVID-19 hospitalization and mortality have been linked to associated comorbidities as diabetes, hypertension, cardiovascular diseases and, recently, to obesity. Similarly, individuals living with obesity are at greater risk to develop clinical complications and to have poor prognosis in severe influenza pneumonia. Immune and metabolic dysfunctions associated with the increased susceptibility to influenza infection are linked to obesity-associated low-grade inflammation, compromised immune and endocrine systems, and to high cardiovascular risk. These preexisting conditions may favor virological persistence, amplify immunopathological responses and worsen hemodynamic instability in severe COVID-19 as well. In this review we highlight the main factors and the current state of the art on obesity as risk factor for influenza and COVID-19 hospitalization, severe respiratory manifestations, extrapulmonary complications and even death. Finally, immunoregulatory mechanisms of severe influenza pneumonia in individuals with obesity are addressed as likely factors involved in COVID-19 pathophysiology.

Clinical Characteristics and Predictors of Mortality in Critically Ill Adult Patients with Influenza Infection.

Patients with influenza infection may develop acute respiratory distress syndrome (ARDS), which is associated with high mortality. Some patients with ARDS receiving extracorporeal membrane oxygenation (ECMO) support die of infectious complications. We aimed to investigate the risk factors affecting the clinical outcomes in critically ill patients with influenza. We retrospectively reviewed the medical records of influenza patients between January 2006 and May 2016 at the Kaohsiung Veterans General Hospital in Taiwan. Patients aged below 20 years or without laboratory-confirmed influenza were excluded. Critically ill patients who presented with ARDS (P = 0.004, odds ratio (OR): 8.054, 95% confidence interval (CI): 1.975-32.855), a higher Acute Physiology and Chronic Health Evaluation (APACHE) II score (P = 0.008, OR: 1.102, 95% CI: 1.025-1.184), or higher positive end-expiratory pressure (P = 0.008, OR: 1.259, 95% CI: 1.061-1.493) may have a higher risk of receiving ECMO. Influenza A (P = 0.037, OR: 0.105, 95% CI: 0.013-0.876) and multiple organ failure (P = 0.007, OR: 0.056, 95% CI: 0.007-0.457) were significantly associated with higher mortality rates. In conclusion, our study showed critically ill influenza patients with ARDS, higher APACHE II scores, and higher positive end-expiratory pressure have a higher risk of receiving ECMO support. Influenza A and multiple organ failure are predictors of mortality.

Spanish Influenza Score (SIS): utilidad del Machine Learning en el desarrollo de una escala temprana de predicción de mortalidad en la gripe grave / Spanish Influenza Score (SIS): Usefulness of machine learning in the development of an early mortality prediction score in severe influenza

Objetivo: Desarrollar una escala predicitva de mortalidad (SIS) en pacientes con gripe grave considerando las variables de ingreso a UCI y comparar su eficacia respecto de un modelo d Random Forest (RF). Diseño Sub-análisis de base de datos GETGAG/SEMICYUC. Ámbito Medicina intensiva. Intervenciones Ninguna. Pacientes Pacientes ingresados en 184 UCI españolas (2009-2018) con infección por gripe. Variables: Demográficas, nivel de gravedad, tiempo síntomas hasta el ingreso al hospital (Gap-H) o desde hospital a UCI (Gap-UCI), o al diagnóstico (Gap-Dg), vacunación, cuadrantes infiltrados, insuficiencia renal, ventilación no-invasiva o invasiva (VM), shock y comorbilidades. Los puntos de corte y la importancia de las variables se obtuvieron de forma automática. Se realizó validación cruzada y regresión logística a partir de la cual se desarrolló la puntuación SIS. Se aplicó la puntuación y se calculó la exactitud y la discriminación (AUC-ROC) para SIS y RF. El análisis se realizó mediante CRAN-R Project. Resultados Se incluyeron 3.959 pacientes, edad 55 (43-67) años, 60% hombres, APACHE II de 16 (12-21) y SOFA 5 (4-8) puntos y una mortalidad del 21,3%. VM, shock, APACHE II, SOFA, insuficiencia renal aguda y Gap-UCI fueron incluidas en SIS. A partir de los OR se construyó el SIS que demostró una exactitud del 83% y un AUC-ROC del 82% similar al RF (AUC-ROC 82%). Conclusiones La escala SIS de fácil aplicación, ha demostrado una adecuada capacidad de estratificación del riesgo de mortalidad en la UCI. Sin embargo, estos resultados deberán ser validados prospectivamente. (AU)

Objective: To develop a mortality prediction score (Spanish Influenza Score [SIS]) for patients with severe influenza considering only variables at ICU admission, and compare its performance respect of Random Forest (RF). Design Sub-analysis from the GETGAG/SEMICYUC database. Scope Intensive Care Medicine. Patients Patients admitted to 184 Spanish ICUs (2009-2018) with influenza infection Intervention: None. Variables Demographic data, severity of illness, times from symptoms on set until hospital admission (Gap-H), hospital to ICU (Gap-ICU) or hospital to diagnosis (Gap-Dg), antiviral vaccination, number of quadrants infiltrated, acute renal failure, invasive or noninvasive ventilation, shock and comorbidities. The study variable cut-off points and importance were obtained automatically. Logistic regression analysis with cross-validation was performed to develop the SIS score using the output coefficients. Accuracy and discrimination (AUC-ROC) were applied to evaluate SIS and RF. All analyses were performed using R (CRAN-R Project). Results A total of 3959 patients were included. The mean age was 55 years (range 43-67), 60% were men, APACHE II 16 (12-21) and SOFA 5 (4-8), with ICU mortality 21.3%. Mechanical ventilation, shock, APACHE II, SOFA, acute renal failure and Gap-ICU were included in the SIS. The latter was generated according to the ORs obtained by logistic regression, and showed an accuracy of 83% with an AUC-ROC of 82%, similar to RF (AUC-ROC 82%). Conclusions The SIS score is easy to apply and shows adequate capacity to stratify the risk of ICU mortality. However, further studies are needed to validate the tool prospectively. (AU)

Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation.

One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co-expression network analysis in one eligible influenza GEO data set (GSE111368) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysis (GSEA) for single hub gene revealed that these genes had a close association with antimicrobial response and neutrophils activity. To further evaluate these genes' ability for diagnosis/prognosis of disease developments, we adopted double validation with (a) another new independent data set (GSE101702); and (b) plasma samples collected from hospitalized influenza patients. We found that 10 hub genes presented highly correlation with disease severity. In particular, BPI and MMP8 encoding proteins in plasma achieved higher expression in severe and dead cases, which indicated an adverse disease development and suggested a frustrating prognosis. These findings provide new insight into severe influenza pathogenesis and identify two significant candidate genes that were superior to the conventional clinical indicators. These candidate genes or encoding proteins could be biomarker for clinical diagnosis and therapeutic targets for severe influenza infection.

Spanish Influenza Score (SIS): Usefulness of machine learning in the development of an early mortality prediction score in severe influenza. / Spanish Influenza Score (SIS): utilidad del Machine Learning en el desarrollo de una escala temprana de predicción de mortalidad en la gripe grave.

OBJECTIVE: To develop a mortality prediction score (Spanish Influenza Score [SIS]) for patients with severe influenza considering only variables at ICU admission, and compare its performance respect of Random Forest (RF). DESIGN: Sub-analysis from the GETGAG/SEMICYUC database. SCOPE: Intensive Care Medicine. PATIENTS: Patients admitted to 184 Spanish ICUs (2009-2018) with influenza infection Intervention: None. VARIABLES: Demographic data, severity of illness, times from symptoms onset until hospital admission (Gap-H), hospital to ICU (Gap-ICU) or hospital to diagnosis (Gap-Dg), antiviral vaccination, number of quadrants infiltrated, acute renal failure, invasive or noninvasive ventilation, shock and comorbidities. The study variable cut-off points and importance were obtained automatically. Logistic regression analysis with cross-validation was performed to develop the SIS score using the output coefficients. Accuracy and discrimination (AUC-ROC) were applied to evaluate SIS and RF. All analyses were performed using R (CRAN-R Project). RESULTS: A total of 3959 patients were included. The mean age was 55 years (range 43-67), 60% were men, APACHE II 16 (12-21) and SOFA 5 (4-8), with ICU mortality 21.3%. Mechanical ventilation, shock, APACHE II, SOFA, acute renal failure and Gap-ICU were included in the SIS. The latter was generated according to the ORs obtained by logistic regression, and showed an accuracy of 83% with an AUC-ROC of 82%, similar to RF (AUC-ROC 82%). CONCLUSIONS: The SIS score is easy to apply and shows adequate capacity to stratify the risk of ICU mortality. However, further studies are needed to validate the tool prospectively.

Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza.

H1N1 is the most common subtype of influenza virus circulating worldwide and can cause severe disease in some populations. Early prediction and intervention for patients who develop severe influenza will greatly reduce their mortality. In this study, we conducted a comprehensive analysis of 180 PBMC samples from three published datasets from the GEO DataSets. Differentially expressed gene (DEG) analysis and weighted correlation network analysis (WGCNA) were performed to provide candidate DEGs for model building. Functional enrichment and CIBERSORT analyses were also performed to evaluate the differences in composition and function of PBMCs between patients with severe and mild disease. Finally, a risk score model was built using lasso regression analysis, with six genes (CX3CR1, KLRD1, MMP8, PRTN3, RETN and SCD) involved. The model performed moderately in the early identification of patients that develop severe H1N1 disease.