Comparison of Echocardiography and Multi-Planar Gated Acquisition Scans for Predicting Cancer-Treatment-Related Cardiovascular Dysfunction.

BACKGROUND: Current guidelines recommend using sequential cardiac imaging to monitor for cancer treatment-related cardiac dysfunction (CTRCD) in patients undergoing potentially cardiotoxic chemotherapy. Multiple different imaging cardiac modalities are available and there are few prospective head-to-head comparative studies to help guide treatment. OBJECTIVES: To perform an exploratory prospective cohort study of "real-world" CTRCD comparing multigated acquisition nuclear ventriculography (MUGA) at the referring cancer specialist's discretion with a novel echocardiographic strategy at an Australian tertiary hospital. METHOD: Patients were recruited from haematology and oncology outpatient clinics if they were scheduled for treatment with anthracyclines and/or trastuzumab. Patients underwent simultaneous MUGA-based cardiac imaging (conventional strategy) at a frequency according to evidenced-based guidelines in addition to researcher-conducted echocardiographic imaging. The echocardiographic imaging was performed in all patients at time points recommended by international society guidelines. Outcomes included adherence to guideline recommendations, concordance between MUGA and echocardiographic left ventricular ejection fraction (LVEF) measurements, and detection of cardiac dysfunction (defined as >5% LVEF decrement from baseline by three-dimensional [3D]-LVEF). A secondary end point was accuracy of global longitudinal strain in predicting cardiac dysfunction. RESULTS: In total, 35 patients were recruited, including 15 with breast cancer, 19 with haematological malignancy, and one with gastric cancer. MUGA and echocardiographic LVEF measurements correlated poorly with limits of agreement of 30% between 3D-LVEF and MUGA-LVEF and 37% for 3D-LVEF and MUGA-LVEF. Only one case (2.9%) of CTRCD was diagnosed by MUGA, compared with 12 (34.2%) cases by echocardiography. Four (4) patients had >10% decrement in 3D-LVEF that was not detected by MUGA. Global longitudinal strain at 2 months displayed significant ability to predict CTRCD (area under the curve, 0.75, 95% confidence interval, 0.55-0.94). CONCLUSIONS: The MUGA correlates poorly with echocardiographic assessment with substantial discrepancy between MUGA and echocardiography in CTRCD diagnosis. Echocardiographic and MUGA imaging strategies should not be considered equivalent for imaging cancer patients, and a single imaging modality should ideally be used per patient to prevent misdiagnosis by inter-modality variation These findings should be considered hypothesis-generating and require confirmation with larger studies.

Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020-2022 pandemic, Washington State.

BACKGROUND: Long-term care facilities (LTCFs) are vulnerable to disease outbreaks. Here, we jointly analyze SARS-CoV-2 genomic and paired epidemiologic data from LTCFs and surrounding communities in Washington state (WA) to assess transmission patterns during 2020-2022, in a setting of changing policy. We describe sequencing efforts and genomic epidemiologic findings across LTCFs and perform in-depth analysis in a single county. METHODS: We assessed genomic data representativeness, built phylogenetic trees, and conducted discrete trait analysis to estimate introduction sizes over time, and explored selected outbreaks to further characterize transmission events. RESULTS: We found that transmission dynamics among cases associated with LTCFs in WA changed over the course of the COVID-19 pandemic, with variable introduction rates into LTCFs, but decreasing amplification within LTCFs. SARS-CoV-2 lineages circulating in LTCFs were similar to those circulating in communities at the same time. Transmission between staff and residents was bi-directional. CONCLUSIONS: Understanding transmission dynamics within and between LTCFs using genomic epidemiology on a broad scale can assist in targeting policies and prevention efforts. Tracking facility-level outbreaks can help differentiate intra-facility outbreaks from high community transmission with repeated introduction events. Based on our study findings, methods for routine tree building and overlay of epidemiologic data for hypothesis generation by public health practitioners are recommended. Discrete trait analysis added valuable insight and can be considered when representative sequencing is performed. Cluster detection tools, especially those that rely on distance thresholds, may be of more limited use given current data capture and timeliness. Importantly, we noted a decrease in data capture from LTCFs over time. Depending on goals for use of genomic data, sentinel surveillance should be increased or targeted surveillance implemented to ensure available data for analysis.

Building up a genomic surveillance platform for SARS-CoV-2 in the middle of a pandemic: a true North-South collaboration.

Next-generation sequencing technology has revolutionised pathogen surveillance over the last two decades. However, the benefits are not equitably distributed, with developing countries lagging far behind in acquiring the required technology and analytical capacity. Recent declines in the cost associated with sequencing-equipment and running consumables have created an opportunity for broader adoption. During the COVID-19 pandemic, rapid diagnostics development and DNA sequencing revolutionised the ability to diagnose and sequence SARS-CoV-2 rapidly. Socioeconomic inequalities substantially impact the ability to sequence SARS-CoV-2 strains and undermine a developing country's pandemic preparedness. Low- and middle-income countries face additional challenges in establishing, maintaining and expanding genomic surveillance. We present our experience of establishing a genomic surveillance system at the Aga Khan University, Karachi, Pakistan. Despite being at a leading health sciences research institute in the country, we encountered significant challenges. These were related to collecting standardised contextual data for SARS-CoV-2 samples, procuring sequencing reagents and consumables, and challenges with library preparation, sequencing and submission of high-quality SARS-CoV-2 genomes. Several technical roadblocks ensued during the implementation of the genomic surveillance framework, which were resolved in collaboration with our partners. High-quality genome sequences were then deposited on open-access platforms per the best practices. Subsequently, these efforts culminated in deploying Pakistan's first SARS-CoV-2 phyllo surveillance map as a Nextstrain build. Our experience offers lessons for the successful development of Genomic Surveillance Infrastructure in resource-limited settings struck by a pandemic.

Metagenomic next-generation sequencing to characterize potential etiologies of non-malarial fever in a cohort living in a high malaria burden area of Uganda.

Causes of non-malarial fevers in sub-Saharan Africa remain understudied. We hypothesized that metagenomic next-generation sequencing (mNGS), which allows for broad genomic-level detection of infectious agents in a biological sample, can systematically identify potential causes of non-malarial fevers. The 212 participants in this study were of all ages and were enrolled in a longitudinal malaria cohort in eastern Uganda. Between December 2020 and August 2021, respiratory swabs and plasma samples were collected at 313 study visits where participants presented with fever and were negative for malaria by microscopy. Samples were analyzed using CZ ID, a web-based platform for microbial detection in mNGS data. Overall, viral pathogens were detected at 123 of 313 visits (39%). SARS-CoV-2 was detected at 11 visits, from which full viral genomes were recovered from nine. Other prevalent viruses included Influenza A (14 visits), RSV (12 visits), and three of the four strains of seasonal coronaviruses (6 visits). Notably, 11 influenza cases occurred between May and July 2021, coinciding with when the Delta variant of SARS-CoV-2 was circulating in this population. The primary limitation of this study is that we were unable to estimate the contribution of bacterial microbes to non-malarial fevers, due to the difficulty of distinguishing bacterial microbes that were pathogenic from those that were commensal or contaminants. These results revealed the co-circulation of multiple viral pathogens likely associated with fever in the cohort during this time period. This study illustrates the utility of mNGS in elucidating the multiple potential causes of non-malarial febrile illness. A better understanding of the pathogen landscape in different settings and age groups could aid in informing diagnostics, case management, and public health surveillance systems.

Joint visualization of seasonal influenza serology and phylogeny to inform vaccine composition.

Seasonal influenza vaccines must be updated regularly to account for mutations that allow influenza viruses to escape our existing immunity. A successful vaccine should represent the genetic diversity of recently circulating viruses and induce antibodies that effectively prevent infection by those recent viruses. Thus, linking the genetic composition of circulating viruses and the serological experimental results measuring antibody efficacy is crucial to the vaccine design decision. Historically, genetic and serological data have been presented separately in the form of static visualizations of phylogenetic trees and tabular serological results to identify vaccine candidates. To simplify this decision-making process, we have created an interactive tool for visualizing serological data that has been integrated into Nextstrain's real-time phylogenetic visualization framework, Auspice. We show how the combined interactive visualizations may be used by decision makers to explore the relationships between complex data sets for both prospective vaccine virus selection and retrospectively exploring the performance of vaccine viruses.

Trends in fetal and neonatal outcomes during the COVID-19 pandemic in Alabama.

BACKGROUND: The current study evaluated the hypothesis that the COVID-19 pandemic is associated with higher stillbirth but lower neonatal mortality rates. METHODS: We compared three epochs: baseline (2016-2019, January-December, weeks 1-52, and 2020, January-February, weeks 1-8), initial pandemic (2020, March-December, weeks 9-52, and 2021, January-June, weeks 1-26), and delta pandemic (2021, July-September, weeks 27-39) periods, using Alabama Department of Public Health database including deliveries with stillbirths ≥20 weeks or live births ≥22 weeks gestation. The primary outcomes were stillbirth and neonatal mortality rates. RESULTS: A total of 325,036 deliveries were included (236,481 from baseline, 74,076 from initial pandemic, and 14,479 from delta pandemic period). The neonatal mortality rate was lower in the pandemic periods (4.4 to 3.5 and 3.6/1000 live births, in the baseline, initial, and delta pandemic periods, respectively, p < 0.01), but the stillbirth rate did not differ (9 to 8.5 and 8.6/1000 births, p = 0.41). On interrupted time-series analyses, there were no significant changes in either stillbirth (p = 0.11 for baseline vs. initial pandemic period, and p = 0.67 for baseline vs. delta pandemic period) or neonatal mortality rates (p = 0.28 and 0.89, respectively). CONCLUSIONS: The COVID-19 pandemic periods were not associated with a significant change in stillbirth and neonatal mortality rates compared to the baseline period. IMPACT: The COVID-19 pandemic could have resulted in changes in fetal and neonatal outcomes. However, only a few population-based studies have compared the risk of fetal and neonatal mortality in the pandemic period to the baseline period. This population-based study identifies the changes in fetal and neonatal outcomes during the initial and delta COVID-19 pandemic period as compared to the baseline period. The current study shows that stillbirth and neonatal mortality rates were not significantly different in the initial and delta COVID-19 pandemic periods as compared to the baseline period.

Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions.

BACKGROUND: In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored. OBJECTIVE: The aim of this study is to convene experts in public health, infectious diseases, virology, and bioinformatics-many of whom were actively engaged in the COVID-19 response-to discuss and report on the application of phylodynamic tools to inform pandemic responses. METHODS: In total, 4 focus groups (FGs) occurred between June 2020 and June 2021, covering both the pre- and postvariant strain emergence and vaccination eras of the ongoing COVID-19 crisis. Participants included national and international academic and government researchers, clinicians, public health practitioners, and other stakeholders recruited through purposive and convenience sampling by the study team. Open-ended questions were developed to prompt discussion. FGs I and II concentrated on phylodynamics for the public health practitioner, while FGs III and IV discussed the methodological nuances of phylodynamic inference. Two FGs per topic area to increase data saturation. An iterative, thematic qualitative framework was used for data analysis. RESULTS: We invited 41 experts to the FGs, and 23 (56%) agreed to participate. Across all the FG sessions, 15 (65%) of the participants were female, 17 (74%) were White, and 5 (22%) were Black. Participants were described as molecular epidemiologists (MEs; n=9, 39%), clinician-researchers (n=3, 13%), infectious disease experts (IDs; n=4, 17%), and public health professionals at the local (PHs; n=4, 17%), state (n=2, 9%), and federal (n=1, 4%) levels. They represented multiple countries in Europe, the United States, and the Caribbean. Nine major themes arose from the discussions: (1) translational/implementation science, (2) precision public health, (3) fundamental unknowns, (4) proper scientific communication, (5) methods of epidemiological investigation, (6) sampling bias, (7) interoperability standards, (8) academic/public health partnerships, and (9) resources. Collectively, participants felt that successful uptake of phylodynamic tools to inform the public health response relies on the strength of academic and public health partnerships. They called for interoperability standards in sequence data sharing, urged careful reporting to prevent misinterpretations, imagined that public health responses could be tailored to specific variants, and cited resource issues that would need to be addressed by policy makers in future outbreaks. CONCLUSIONS: This study is the first to detail the viewpoints of public health practitioners and molecular epidemiology experts on the use of viral genomic data to inform the response to the COVID-19 pandemic. The data gathered during this study provide important information from experts to help streamline the functionality and use of phylodynamic tools for pandemic responses.

Identifying Food Insecurity in Cardiology Clinic and Connecting Families to Resources.

BACKGROUND: Food insecurity (FI) increases children's risk for illness and developmental and behavioral problems, which are ongoing concerns for congenital heart disease (CHD) patients. In 2020, 14.8% of households with children suffered from FI. The Hunger Vital Signs (HVS) asks 2 questions to assess FI. The global aim of the project is to implement HVS and connect FI families to resources. METHODS: Stakeholders identified 6 critical drivers in implementing FI screening at an outpatient cardiology clinic and conducted plan-do-study-act (PDSA) cycles to implement HVS. Over the 13-month study period, time series analyses were performed to assess our process measure (FI screening) and outcome measure (connection of FI families to resources). Demographics and severity of CHD were analyzed for FI families. RESULTS: Screening rates increased from 0% to >85%, screening 5064 families. Process evaluations revealed roadblocks including screening discomfort. FI families were more likely to identify as Black or multiple or other ethnicity. Severe CHD patients were at higher risk for FI (n = 106, odds ratio [OR] 1.67 [1.21-2.29], P = .002). Face-to-face meetings with social work and community partnerships reduced loss to follow-up and our ability to offer all FI families individualized FI resources. CONCLUSION: HVS screening can be implemented in a cardiology clinic to improve identification of FI families. A written tool can combat screening discomfort and improve identification of FI families. Children with severe CHD may be at increased risk for FI. A multidisciplinary team and community partnerships can improve individualized resource distribution.

Using genomic epidemiology of SARS-CoV-2 to support contact tracing and public health surveillance in rural Humboldt County, California.

BACKGROUND: During the COVID-19 pandemic within the United States, much of the responsibility for diagnostic testing and epidemiologic response has relied on the action of county-level departments of public health. Here we describe the integration of genomic surveillance into epidemiologic response within Humboldt County, a rural county in northwest California. METHODS: Through a collaborative effort, 853 whole SARS-CoV-2 genomes were generated, representing ~58% of the 1,449 SARS-CoV-2-positive cases detected in Humboldt County as of March 12, 2021. Phylogenetic analysis of these data was used to develop a comprehensive understanding of SARS-CoV-2 introductions to the county and to support contact tracing and epidemiologic investigations of all large outbreaks in the county. RESULTS: In the case of an outbreak on a commercial farm, viral genomic data were used to validate reported epidemiologic links and link additional cases within the community who did not report a farm exposure to the outbreak. During a separate outbreak within a skilled nursing facility, genomic surveillance data were used to rule out the putative index case, detect the emergence of an independent Spike:N501Y substitution, and verify that the outbreak had been brought under control. CONCLUSIONS: These use cases demonstrate how developing genomic surveillance capacity within local public health departments can support timely and responsive deployment of genomic epidemiology for surveillance and outbreak response based on local needs and priorities.

Infection clusters can elevate risk of diagnostic target failure for detection of SARS-CoV-2.

The C29197T mutation is one of 4 point mutations known to cause N-gene target failure (NGTF) in the Xpert Xpress SARS-CoV-2 and Xpert Omni SARS-CoV-2 assays from Cepheid (Sunnyvale, CA). We describe a high local prevalence in January of 8.5% (CI 4.9-14.2%) for the C29197T mutation, which was over 3-fold higher than the prevalence estimated statewide in California during the same time frame, 2.5% (CI 2.1-2.8%). Using phylogenetic analysis, we discovered that this increase in prevalence was due, at least in part, to a disproportionately large infection cluster of unknown origin. This study emphasizes the importance of sequencing at the local jurisdictional level and demonstrates the impact that regional variation can have when assessing risk due to point mutations that impact clinical test performance. It also reinforces the need for diligent reporting of abnormal test results by clinical laboratories, especially during Emergency Use Authorization (EUA) periods, as additional information is gathered about the target organism and the performance of EUA-authorized tests over time.

Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package.

BACKGROUND: The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility, and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. RESULTS: As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonizable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonization and submission of sequence data and contextual information to public biorepositories. CONCLUSIONS: Well-structured, rich contextual data add value, promote reuse, and enable aggregation and integration of disparate datasets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the NCBI's BioSample database.

Tracing the Origin, Spread, and Molecular Evolution of Zika Virus in Puerto Rico, 2016-2017.

We reconstructed the 2016-2017 Zika virus epidemic in Puerto Rico by using complete genomes to uncover the epidemic's origin, spread, and evolutionary dynamics. Our study revealed that the epidemic was propelled by multiple introductions that spread across the island, intricate evolutionary patterns, and ≈10 months of cryptic transmission.

Repeated introductions and intensive community transmission fueled a mumps virus outbreak in Washington State.

In 2016/2017, Washington State experienced a mumps outbreak despite high childhood vaccination rates, with cases more frequently detected among school-aged children and members of the Marshallese community. We sequenced 166 mumps virus genomes collected in Washington and other US states, and traced mumps introductions and transmission within Washington. We uncover that mumps was introduced into Washington approximately 13 times, primarily from Arkansas, sparking multiple co-circulating transmission chains. Although age and vaccination status may have impacted transmission, our data set could not quantify their precise effects. Instead, the outbreak in Washington was overwhelmingly sustained by transmission within the Marshallese community. Our findings underscore the utility of genomic data to clarify epidemiologic factors driving transmission and pinpoint contact networks as critical for mumps transmission. These results imply that contact structures and historic disparities may leave populations at increased risk for respiratory virus disease even when a vaccine is effective and widely used.

Integration of genomic sequencing into the response to the Ebola virus outbreak in Nord Kivu, Democratic Republic of the Congo.

On 1 August 2018, the Democratic Republic of the Congo (DRC) declared its tenth Ebola virus disease (EVD) outbreak. To aid the epidemiologic response, the Institut National de Recherche Biomédicale (INRB) implemented an end-to-end genomic surveillance system, including sequencing, bioinformatic analysis and dissemination of genomic epidemiologic results to frontline public health workers. We report 744 new genomes sampled between 27 July 2018 and 27 April 2020 generated by this surveillance effort. Together with previously available sequence data (n = 48 genomes), these data represent almost 24% of all laboratory-confirmed Ebola virus (EBOV) infections in DRC in the period analyzed. We inferred spatiotemporal transmission dynamics from the genomic data as new sequences were generated, and disseminated the results to support epidemiologic response efforts. Here we provide an overview of how this genomic surveillance system functioned, present a full phylodynamic analysis of 792 Ebola genomes from the Nord Kivu outbreak and discuss how the genomic surveillance data informed response efforts and public health decision making.

Ebola Virus Transmission Initiated by Relapse of Systemic Ebola Virus Disease.

During the 2018-2020 Ebola virus disease (EVD) outbreak in North Kivu province in the Democratic Republic of Congo, EVD was diagnosed in a patient who had received the recombinant vesicular stomatitis virus-based vaccine expressing a ZEBOV glycoprotein (rVSV-ZEBOV) (Merck). His treatment included an Ebola virus (EBOV)-specific monoclonal antibody (mAb114), and he recovered within 14 days. However, 6 months later, he presented again with severe EVD-like illness and EBOV viremia, and he died. We initiated epidemiologic and genomic investigations that showed that the patient had had a relapse of acute EVD that led to a transmission chain resulting in 91 cases across six health zones over 4 months. (Funded by the Bill and Melinda Gates Foundation and others.).

Ten recommendations for supporting open pathogen genomic analysis in public health.

Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such as COVID-19, investigate transmission chains and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of whole-genome sequencing also poses new challenges for public-health agencies that must adapt to support a new set of expertise, which means that the capacity to perform genomic data assembly and analysis has not expanded as widely as the adoption of sequencing itself. In this Perspective, we make recommendations for developing an accessible, unified informatic ecosystem to support pathogen genomic analysis in public-health agencies across income settings. We hope that the creation of this ecosystem will allow agencies to effectively and efficiently share data, workflows and analyses and thereby increase the reproducibility, accessibility and auditability of pathogen genomic analysis while also supporting agency autonomy.