Molecular characteristics and successful management of a respiratory syncytial virus outbreak among pediatric patients with hemato-oncological disease

Target capture sequencing reveals a monoclonal outbreak of respiratory syncytial virus B infections among adult hematologic patients

Background

Respiratory syncytial virus (RSV) causes community-acquired respiratory tract infections during winter. However, outbreaks in hospitals also occur repeatedly. In particular, patients with hematologic malignancies are at an increased risk for a severe and potentially fatal course of RSV infection. Here we present the investigation of an RSV outbreak in a hematology ward for adults following the ORION statement.

Methods

An epidemiologic and molecular outbreak analysis was performed. We developed and employed a minimal oligonucleotide probe set in target capture probe sequencing that allows cost-effective RSV-A or -B capturing to reconstruct RSV genomes from clinical samples.

Results

Four adult patients were involved in the outbreak caused by RSV-B in March 2019. The enforcement of the pre-existing infection control measures by effective training of hospital staff contributed to a successful containment. PCR-based RSV screening on the ward enabled early detection of new cases and rapid isolation measures. The molecular analysis demonstrated that the outbreak sequences were highly related and distinct to other RSV-B strains circulating at the same time.

Conclusions

A multimodal infection control concept is essential for the timely detection and control of RSV outbreaks in patients with hematological disease. Among other measures, preventive screening for respiratory viruses is recommended. Furthermore, the integration of conventional and molecular epidemiology, such as whole-genome sequencing and variant calling, significantly contributes to the understanding of transmission pathways. Based on this, appropriate conclusions can be drawn for targeted prevention measures that have prepared us for the COVID-19 pandemic beyond the RSV approach described here.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13756-022-01120-z.

Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients

Respiratory syncytial virus (RSV) causes millions of infections among children in the US each year and can cause severe disease or death. Infections that are not promptly detected can cause outbreaks that put other hospitalized patients at risk. No tools besides diagnostic testing are available to rapidly and reliably predict RSV infections among hospitalized patients. We conducted a retrospective study from pediatric electronic health record (EHR) data and built a machine learning model to predict whether a patient will test positive to RSV by nucleic acid amplification test during their stay. Our model demonstrated excellent discrimination with an area under the receiver-operating curve of 0.919, a sensitivity of 0.802, and specificity of 0.876. Our model can help clinicians identify patients who may have RSV infections rapidly and cost-effectively. Successfully integrating this model into routine pediatric inpatient care may assist efforts in patient care and infection control.

Influenza and respiratory syncytial virus screening for the detection of asymptomatically infected patients in hematology and oncology

Introduction: Respiratory syncytial virus (RSV) and influenza virus infections are a significant healthcare risk for immunocompromised patients. In addition to community onset, nosocomial acquisition and transmission may also occur. Detection of asymptomatic shedders (e.g., patients in the incubation period or immunosuppressed long term shedders) facilitates control of nosocomial transmission.

Methods: To strengthen the existing infection control concept, a PCR-based screening for RSV and influenza virus was implemented for all patients lacking respiratory symptoms (asymptomatic patients) who were hospitalized on an adult and a pediatric hemato-oncological ward. Laboratory results of this screening were analyzed retrospectively.

Results: 665 respiratory specimens were obtained for screening from 251 patients (26% were 18 years and younger) from December 2016 to April 2017. In 23 patients without respiratory symptoms, either influenza virus or RSV infection was found, resulting in a detection rate of about 9%. In 6 patients, the infection was presumably detected during the incubation period, because an increase of viral load was observed in subsequent specimens. Positive screening results facilitated timely implementation of adequate infection control precautions. Nosocomial clusters of RSV or influenza were not detected during the screening period on the two wards.

Conclusion: The seasonal screening program expanded our existing infection control concept in terms of patients lacking respiratory symptoms who shed influenza virus or RSV. It enabled us to identify 23 RSV or influenza infections in patients lacking respiratory symptoms in a 4-month period and thus to rapidly take isolation precautions.