Molecular characterization of a nosocomial outbreak of influenza B virus in an acute care hospital setting

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.

Influenza A subtype H3N2 is associated with an increased risk of hospital dissemination - an observational study over six influenza seasons

BACKGROUND

Previous studies on hospital-acquired influenza (HAI) have not systematically evaluated the possible impact of different influenza subtypes. HAI has historically been associated with high mortality, but clinical consequences may be less severe in a modern hospital setting.

AIMS

To identify and quantify HAI for each season, investigate possible associations with varying influenza subtypes, and to determine HAI-associated mortality.

METHODS

All influenza-PCR-positive adult patients (>18 years old) hospitalized in Skåne County during 2013-2019, were prospectively included in the study. Positive influenza samples were subtyped. Medical records of patients with suspected HAI were examined to confirm a nosocomial origin and to determine 30-day mortality.

RESULTS

Of 4110 hospitalized patients with a positive influenza PCR, 430 (10.5%) were HAI. Influenza A(H3N2) infections were more often HAI (15.1%) than influenza A(H1N1)pdm09, and influenza B (6.3% and 6.8% respectively, P<0.001). The majority of HAI caused by H3N2 were clustered (73.3 %) and were the cause of all 20 hospital outbreaks consisting of ≥4 affected patients. In contrast, the majority of HAI caused by influenza A(H1N1)pdm09 and influenza B were solitary cases (60% and 63.2%, respectively, P<0.001). Mortality associated with HAI was 9.3% and similar between subtypes.

CONCLUSIONS

HAI caused by influenza A(H3N2) was associated with an increased risk of hospital dissemination. Our study is relevant for future seasonal influenza infection control preparedness and shows that subtyping of influenza may help to define relevant infection control measures. Mortality in HAI remains substantial in a modern hospital setting.

Occupational risks for infection with influenza A and B: a national case-control study covering 1 July 2006-31 December 2019

OBJECTIVES

We investigated whether crowded workplaces, sharing surfaces and exposure to infections were factors associated with a positive test for influenza virus.

METHODS

We studied 11 300 cases with a positive test for influenza A and 3671 cases of influenza B from Swedish registry of communicable diseases. Six controls for each case were selected from the population registry, with each control being assigned the index date of their corresponding case. We linked job histories to job-exposure matrices (JEMs), to assess different transmission dimensions of influenza and risks for different occupations compared with occupations that the JEM classifies as low exposed. We used adjusted conditional logistic analyses to estimate the ORs for influenza with 95% CI.

RESULTS

The highest odds were for influenza were: regular contact with infected patients (OR 1.64, 95% CI 1.54 to 1.73); never maintained social distance (OR 1.51, 95% CI 1.43 to 1.59); frequently sharing materials/surfaces with the general public (OR 1.41, 95% CI 1.34 to 1.48); close physical proximity (OR 1.54, 95% CI 1.45 to 1.62) and high exposure to diseases or infections (OR 1.54, 95% CI 1.44 to 1.64). There were small differences between influenza A and influenza B. The five occupations with the highest odds as compared with low exposed occupations were: primary care physicians, protective service workers, elementary workers, medical and laboratory technicians, and taxi drivers.

CONCLUSIONS

Contact with infected patients, low social distance and sharing surfaces are dimensions that increase risk for influenza A and B. Further safety measures are needed to diminish viral transmission in these contexts.

Investigating Epidemiologic and Molecular Links Between Patients With Community- and Hospital-Acquired Influenza A: 2017-2018 and 2019-2020, Michigan

Background

Hospital-acquired influenza virus infection (HAII) can cause severe morbidity and mortality. Identifying potential transmission routes can inform prevention strategies.

Methods

We identified all hospitalized patients testing positive for influenza A virus at a large, tertiary care hospital during the 2017-2018 and 2019-2020 influenza seasons. Hospital admission dates, locations of inpatient service, and clinical influenza testing information were retrieved from the electronic medical record. Time-location groups of epidemiologically linked influenza patients were defined and contained ≥1 presumed HAII case (first positive ≥48 hours after admission). Genetic relatedness within time-location groups was assessed by whole genome sequencing.

Results

During the 2017-2018 season, 230 patients tested positive for influenza A(H3N2) or unsubtyped influenza A including 26 HAIIs. There were 159 influenza A(H1N1)pdm09 or unsubtyped influenza A-positive patients identified during the 2019-2020 season including 33 HAIIs. Consensus sequences were obtained for 177 (77%) and 57 (36%) of influenza A cases in 2017-2018 and 2019-2020, respectively. Among all influenza A cases, there were 10 time-location groups identified in 2017-2018 and 13 in 2019-2020; 19 of 23 groups included ≤4 patients. In 2017-2018, 6 of 10 groups had ≥2 patients with sequence data, including ≥1 HAII case. Two of 13 groups met this criteria in 2019-2020. Two time-location groups from 2017-2018 each contained 3 genetically linked cases.

Conclusions

Our results suggest that HAIIs arise from outbreak transmission from nosocomial sources as well as single infections from unique community introductions.

Distinguishing viruses responsible for influenza-like illness

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.

NGSocomial Infections: High-Resolution Views of Hospital-Acquired Infections Through Genomic Epidemiology

Hospital outbreak investigations are high-stakes epidemiology. Contacts between staff and patients are numerous; environmental and community exposures are plentiful; and patients are highly vulnerable. Having the best data is paramount to understanding an outbreak in order to stop ongoing transmission and prevent future outbreaks. In the past 5 years, the high-resolution view of transmission offered by analyzing pathogen whole-genome sequencing (WGS) is increasingly part of hospital outbreak investigations. Concerns over speed and actionability, assay validation, liability, cost, and payment models lead to further opportunities for work in this area. Now accelerated by funding for COVID-19, the use of genomics in hospital outbreak investigations has firmly moved from the academic literature to more quotidian operations, with associated concerns involving regulatory affairs, data integration, and clinical interpretation. This review details past uses of WGS data in hospital-acquired infection outbreaks as well as future opportunities to increase its utility and growth in hospital infection prevention.

Extensive Hospital In-Ward Clustering Revealed By Molecular Characterization of Influenza A Virus Infection

BACKGROUND

Nosocomial transmission of influenza A virus (InfA) infection is not fully recognized. The aim of this study was to describe the characteristics of hospitalized patients with InfA infections during an entire season and to investigate in-ward transmission at a large, acute-care hospital.

METHODS

During the 2016-17 season, all hospitalized patients ≥18 years old with laboratory-verified (real-time polymerase chain reaction) InfA were identified. Cases were characterized according to age; sex; comorbidity; antiviral therapy; viral load, expressed as cycle threshold values; length of hospital stay; 30-day mortality; and whether the InfA infection met criteria for a health care-associated influenza A infection (HCAI). Respiratory samples positive for InfA that were collected at the same wards within 7 days were chosen for whole-genome sequencing (WGS) and a phylogenetic analysis was performed to detect clustering. For reference, concurrent InfA strains from patients with community-acquired infection were included.

RESULTS

We identified a total of 435 InfA cases, of which 114 (26%) met the HCAI criteria. The overall 30-day mortality rate was higher among patients with HCAI (9.6% vs 4.6% among non-HCAI patients), although the difference was not statistically significant in a multivariable analysis, where age was the only independent risk factor for death (P < .05). We identified 8 closely related clusters (involving ≥3 cases) and another 10 pairs of strains, supporting in-ward transmission.

CONCLUSIONS

We found that the in-ward transmission of InfA occurs frequently and that HCAI may have severe outcomes. WGS may be used for outbreak investigations, as well as for evaluations of the effects of preventive measures.

Burden of influenza B virus infection and considerations for clinical management

Influenza B viruses cause significant morbidity and mortality, particularly in children, but the awareness of their impact is often less than influenza A viruses partly due to their lack of pandemic potential. Here, we summarise the biology, epidemiology and disease burden of influenza B, and review existing data on available antivirals for its management. There has long been uncertainty surrounding the clinical efficacy of neuraminidase inhibitors (NAIs) for influenza B treatment. In this article, we bring together the existing data on NAIs and discuss these alongside recent large randomised controlled trial data for the new polymerase inhibitor baloxavir in high-risk influenza B patients. Finally, we offer considerations for the clinical management of influenza B, with a focus on children and high-risk patients where disease burden is highest.

Metagenomic Nanopore Sequencing of Influenza Virus Direct from Clinical Respiratory Samples

Influenza is a major global public health threat as a result of its highly pathogenic variants, large zoonotic reservoir, and pandemic potential. Metagenomic viral sequencing offers the potential for a diagnostic test for influenza virus which also provides insights on transmission, evolution, and drug resistance and simultaneously detects other viruses. We therefore set out to apply the Oxford Nanopore Technologies sequencing method to metagenomic sequencing of respiratory samples. We generated influenza virus reads down to a limit of detection of 102 to 103 genome copies/ml in pooled samples, observing a strong relationship between the viral titer and the proportion of influenza virus reads (P = 4.7 × 10-5). Applying our methods to clinical throat swabs, we generated influenza virus reads for 27/27 samples with mid-to-high viral titers (cycle threshold [CT ] values, <30) and 6/13 samples with low viral titers (CT values, 30 to 40). No false-positive reads were generated from 10 influenza virus-negative samples. Thus, Nanopore sequencing operated with 83% sensitivity (95% confidence interval [CI], 67 to 93%) and 100% specificity (95% CI, 69 to 100%) compared to the current diagnostic standard. Coverage of full-length virus was dependent on sample composition, being negatively influenced by increased host and bacterial reads. However, at high influenza virus titers, we were able to reconstruct >99% complete sequences for all eight gene segments. We also detected a human coronavirus coinfection in one clinical sample. While further optimization is required to improve sensitivity, this approach shows promise for the Nanopore platform to be used in the diagnosis and genetic analysis of influenza virus and other respiratory viruses.

Airborne Transmission of Influenza Virus in a Hospital of Qinhuangdao During 2017-2018 Flu Season

The 2017-2018 flu season is considered to be one of the most severe, with numerous influenza outbreaks worldwide. In an infectious disease hospital of Qinhuangdao, air samples were collected daily from outpatient hall, clinical laboratory, fever clinic, children's ward (Children's Ward I/Children's Ward II), and adult ward during 23-29 January 2018 (peak flu activity) and 9-15 April 2018 (low flu activity). The air samples were collected with SLC-SiOH magnetic beads using impingement samplers. Real-time PCR assay was used to detect the RNA of airborne influenza (IFVA and IFVB) in the 91 collected aerosol samples. The results indicated that the air samples collected from the children's wards, adult ward and fever clinic were detected with airborne influenza viruses. However, the samples collected from outpatient hall and clinical laboratory were absence of influenza viruses. In addition, the subtypes of pH1N1/IFVA, H3N2/IFVA, yamagata/IFVB, and victoria/IFVB were detected among the samples with positive IFVA and IFVB. Notably, a new developed subtype of pH1N1 (an epidemic in 2018) was detected in the aerosol samples. In summary, this study profiled the distribution of airborne influenza in an infectious hospital in Qinhuangdao during 2017-2018 flu season. Patients infected with influenza could release airborne particles containing the virus into their environment. Healthcare workers and visitors in those places might have frequent exposure to airborne influenza virus. Therefore, we recommend some protective measures such as air disinfection and mask wearing to prevent and control the transmission of airborne influenza in hospital.