Risk factors and outcomes for pandemic H1N1 influenza compared with seasonal influenza in hospitalized children in China

Determinants of Fatal Outcome in Patients Admitted to Intensive Care Units With Influenza, European Union 2009-2017

Background

Morbidity, severity, and mortality associated with annual influenza epidemics are of public health concern. We analyzed surveillance data on hospitalized laboratory-confirmed influenza cases admitted to intensive care units to identify common determinants for fatal outcome and inform and target public health prevention strategies, including risk communication.

Methods

We performed a descriptive analysis and used Poisson regression models with robust variance to estimate the association of age, sex, virus (sub)type, and underlying medical condition with fatal outcome using European Union data from 2009 to 2017.

Results

Of 13 368 cases included in the basic dataset, 2806 (21%) were fatal. Age ≥40 years and infection with influenza A virus were associated with fatal outcome. Of 5886 cases with known underlying medical conditions and virus A subtype included in a more detailed analysis, 1349 (23%) were fatal. Influenza virus A(H1N1)pdm09 or A(H3N2) infection, age ≥60 years, cancer, human immunodeficiency virus infection and/or other immune deficiency, and heart, kidney, and liver disease were associated with fatal outcome; the risk of death was lower for patients with chronic lung disease and for pregnant women.

Conclusions

This study re-emphasises the importance of preventing influenza in the elderly and tailoring strategies to risk groups with underlying medical conditions.

Combination of procalcitonin and C-reactive protein levels in the early diagnosis of bacterial co-infections in children with H1N1 influenza

Objective

This study evaluated the diagnostic value of measuring the levels of procalcitonin (PCT) and C‐reactive protein (CRP) to differentiate children co‐infected with H1N1 influenza and bacteria from children infected with H1N1 influenza alone.

Methods

Consecutive patients (children aged < 5 years) with laboratory‐confirmed H1N1 influenza who were hospitalized or received outpatient care from a tertiary‐care hospital in Canton, China, between January 1, 2012, and September 1, 2017, were included in the present study. Laboratory results, including serum PCT and CRP levels, white blood cell (WBC) counts, and bacterial cultures, were analyzed. The predictive value of the combination of biomarkers versus any of the biomarkers alone for diagnosing bacterial co‐infections was evaluated using logistic regression analyses.

Results

Significantly higher PCT (1.46 vs 0.21 ng/mL, P < 0.001) and CRP (19.20 vs 5.10 mg/dL, P < 0.001) levels were detected in the bacterial co‐infection group than in the H1N1 infection‐alone group. Using PCT or CRP levels alone, the areas under the curves (AUCs) for predicting bacterial co‐infections were 0.801 (95% CI, 0.772‐0.855) and 0.762 (95% CI, 0.722‐0.803), respectively. Using a combination of PCT and CRP, the logistic regression‐based model, Logit(P) = −1.912 + 0.546 PCT + 0.087 CRP, showed significantly greater accuracy (AUC: 0.893, 95% CI: 0.842‐0.934) than did the other three biomarkers.

Conclusions

The combination of PCT and CRP levels could provide a useful method of distinguishing bacterial co‐infections from an H1N1 influenza infection alone in children during the early disease phase. After further validation, the flexible model derived here could assist clinicians in decision‐making processes.

The frequency of influenza and bacterial coinfection: a systematic review and meta-analysis

Aim

Coinfecting bacterial pathogens are a major cause of morbidity and mortality in influenza. However, there remains a paucity of literature on the magnitude of coinfection in influenza patients.

Method

A systematic search of MeSH, Cochrane Library, Web of Science, SCOPUS, EMBASE, and PubMed was performed. Studies of humans in which all individuals had laboratory confirmed influenza, and all individuals were tested for an array of common bacterial species, met inclusion criteria.

Results

Twenty‐seven studies including 3215 participants met all inclusion criteria. Common etiologies were defined from a subset of eight articles. There was high heterogeneity in the results (I² = 95%), with reported coinfection rates ranging from 2% to 65%. Although only a subset of papers were responsible for observed heterogeneity, subanalyses and meta‐regression analysis found no study characteristic that was significantly associated with coinfection. The most common coinfecting species were Streptococcus pneumoniae and Staphylococcus aureus, which accounted for 35% (95% CI, 14%–56%) and 28% (95% CI, 16%–40%) of infections, respectively; a wide range of other pathogens caused the remaining infections. An assessment of bias suggested that lack of small‐study publications may have biased the results.

Conclusions

The frequency of coinfection in the published studies included in this review suggests that although providers should consider possible bacterial coinfection in all patients hospitalized with influenza, they should not assume all patients are coinfected and be sure to properly treat underlying viral processes. Further, high heterogeneity suggests additional large‐scale studies are needed to better understand the etiology of influenza bacterial coinfection.