Influenza A infections: predictors of disease severity.

Influenza affects approximately 10% of the world's population annually. It is associated with high morbidity and mortality rates due to its propensity to progress to severe acute respiratory infection, leading to 10-40% of hospitalized patients needing intensive care. Characterizing the multifactorial predictors of poor prognosis is essential for developing strategies against this disease. This study aimed to identify predictors of disease severity in influenza A-infected (IFA-infected) patients and to propose a prognostic score. A retrospective cross-sectional study was conducted with 142 IFA-infected out- and inpatients treated at a tertiary hospital between 2010 and 2018. The viral subtypes, hemagglutinin mutations, viral load, IL-28B SNPs, and clinical risk factors were evaluated according to the patient's ICU admission. Multivariate analysis identified the following risk factors for disease severity: neuromuscular diseases (OR = 7.02; 95% CI = 1.18-41.75; p = 0.032), cardiovascular diseases (OR = 5.47; 95% CI = 1.96-15.27; p = 0.001), subtype (H1N1) pdm09 infection (OR = 2.29; 95% CI = 1.02-5.15; p = 0.046), and viral load (OR = 1.43; 95% CI = 1.09-1.88; p = 0.009). The prognosis score for ICU admission is based on these predictors of severity presented and ROC curve AUC = 0.812 (p < 0.0001). Our results identified viral and host predictors of disease severity in IFA-infected patients, yielding a prognostic score that had a high performance in predicting the IFA patients' ICU admission and better results than a viral load value alone. However, its implementation in health services needs to be validated in a broader population.

Standardization of a high-performance RT-qPCR for viral load absolute quantification of influenza A.

Influenza is an acute viral infectious respiratory disease worldwide, presenting in different clinical forms, from influenza-like illness (ILI) to severe acute respiratory infection (SARI). Although real-time quantitative polymerase chain reaction (qPCR) is already an important tool for both diagnosis and treatment monitoring of several viral infections, the correlation between the clinical aspects and the viral load of influenza is still unclear. This lack of clarity is primarily due to the low accuracy and reproducibility of the methodologies developed to quantify the influenza virus. Thus, this study aimed to develop and standardize a universal absolute quantification for influenza A by reverse transcription-quantitative PCR (RT-qPCR), using a plasmid DNA. The assay showed efficiency (Eff%) 98.6, determination coefficient (R2) 0.998, linear range 10^1 to 10^10, limit of detection (LOD) 6.77, limit of quantification (LOQ) 20.52 copies/reaction. No inter and intra assay variability was shown, and neither was the matrix effect observed. Serial measurements of clinical samples collected at a 72h interval showed no change in viral load. By contrast, immunocompetent patients have a significantly lower viral load than immunosuppressed ones. Absolute quantification in clinical samples showed some predictors associated with increased viral load: (H1N1)pdm09 (0.045); women (p = 0.049) and asthmatics (p = 0.035). The high efficiency, precision, and previous performance in clinical samples suggest the assay can be used as an accurate universal viral load quantification of influenza A. Its applicability in predicting severity and response to antivirals needs to be evaluated.