Discovery and verification of extracellular microRNA biomarkers for diagnostic and prognostic assessment of preeclampsia at triage.

We report on the identification of extracellular miRNA (ex-miRNA) biomarkers for early diagnosis and prognosis of preeclampsia (PE). Small RNA sequencing of maternal serum prospectively collected from participants undergoing evaluation for suspected PE revealed distinct patterns of ex-miRNA expression among different categories of hypertensive disorders in pregnancy. Applying an iterative machine learning method identified three bivariate miRNA biomarkers (miR-522-3p/miR-4732-5p, miR-516a-5p/miR-144-3p, and miR-27b-3p/let-7b-5p) that, when applied serially, distinguished between PE cases of different severity and differentiated cases from controls with a sensitivity of 93%, specificity of 79%, positive predictive value (PPV) of 55%, and negative predictive value (NPV) of 89%. In a small independent validation cohort, these ex-miRNA biomarkers had a sensitivity of 91% and specificity of 57%. Combining these ex-miRNA biomarkers with the established sFlt1:PlGF protein biomarker ratio performed better than either set of biomarkers alone (sensitivity of 89.4%, specificity of 91.3%, PPV of 95.5%, and NPV of 80.8%).

Perceived COVID-19 risk and testing experiences in the San Ysidro U.S./Mexico border region.

Racial and ethnic disparities in COVID-19 incidence are pronounced in underserved U.S./Mexico border communities. Working and living environments in these communities can lead to increased risk of COVID-19 infection and transmission, and this increased risk is exacerbated by lack of access to testing. As part of designing a community and culturally tailored COVID-19 testing program, we surveyed community members in the San Ysidro border region. The purpose of our study was to characterize knowledge, attitudes, and beliefs of prenatal patients, prenatal caregivers, and pediatric caregivers at a Federally Qualified Health Center (FHQC) in the San Ysidro region regarding perceived risk of COVID-19 infection and access to testing. A cross-sectional survey was used to collect information on experiences accessing COVID-19 testing and perceived risk of COVID-19 infection within San Ysidro between December 29, 2020 and April 2, 2021. A total of 179 surveys were analyzed. Most participants identified as female (85%) and as Mexican/Mexican American (75%). Over half (56%) were between the age of 25 and 34 years old. Perceived Risk: 37% reported moderate to high risk of COVID-19 infection, whereas 50% reported their risk low to none. Testing Experience: Approximately 68% reported previously being tested for COVID-19. Among those tested, 97% reported having very easy or easy access to testing. Reasons for not testing included limited appointment availability, cost, not feeling sick, and concern about risk of infection while at a testing facility. This study is an important first step to understand the COVID-19 risk perceptions and testing access among patients and community members living near the U.S./Mexico border in San Ysidro, California.

COVID-19 testing strategies that fail to incorporate culturally competent methods to reach traditionally underserved communities can lead to persistent transmission and increased infection rates. During the early stages of the COVID-19 pandemic, we surveyed 179 people living in a community with high burden of COVID-19 infection about their perception of infection risk and their experiences accessing testing. Capturing and understanding these community perceptions on COVID-19 risk are vital when developing a testing program that is accessible and appropriate for the target population. In our study, we found half of survey respondents thought their risk of COVID-19 infection as low to none and over half of respondents stated they had already been tested for COVID-19. These findings provide insight to the beliefs of individuals who live and seek health care in communities with high rates of COVID-19 infection and will help guide the design and implementation of culturally tailored testing strategies.

Clinical and economic evaluation of a proteomic biomarker preterm birth risk predictor: cost-effectiveness modeling of prenatal interventions applied to predicted higher-risk pregnancies within a large and diverse cohort.

OBJECTIVES: Preterm birth occurs in more than 10% of U.S. births and is the leading cause of U.S. neonatal deaths, with estimated annual costs exceeding $25 billion USD. Using real-world data, we modeled the potential clinical and economic utility of a prematurity-reduction program comprising screening in a racially and ethnically diverse population with a validated proteomic biomarker risk predictor, followed by case management with or without pharmacological treatment. METHODS: The ACCORDANT microsimulation model used individual patient data from a prespecified, randomly selected sub-cohort (N = 847) of a multicenter, observational study of U.S. subjects receiving standard obstetric care with masked risk predictor assessment (TREETOP; NCT02787213). All subjects were included in three arms across 500 simulated trials: standard of care (SoC, control); risk predictor/case management comprising increased outreach, education and specialist care (RP-CM, active); and multimodal management (risk predictor/case management with pharmacological treatment) (RP-MM, active). In the active arms, only subjects stratified as higher risk by the predictor were modeled as receiving the intervention, whereas lower-risk subjects received standard care. Higher-risk subjects' gestational ages at birth were shifted based on published efficacies, and dependent outcomes, calibrated using national datasets, were changed accordingly. Subjects otherwise retained their original TREETOP outcomes. Arms were compared using survival analysis for neonatal and maternal hospital length of stay, bootstrap intervals for neonatal cost, and Fisher's exact test for neonatal morbidity/mortality (significance, p < .05). RESULTS: The model predicted improvements for all outcomes. RP-CM decreased neonatal and maternal hospital stay by 19% (p = .029) and 8.5% (p = .001), respectively; neonatal costs' point estimate by 16% (p = .098); and moderate-to-severe neonatal morbidity/mortality by 29% (p = .025). RP-MM strengthened observed reductions and significance. Point estimates of benefit did not differ by race/ethnicity. CONCLUSIONS: Modeled evaluation of a biomarker-based test-and-treat strategy in a diverse population predicts clinically and economically meaningful improvements in neonatal and maternal outcomes.

Preterm birth, defined as delivery before 37 weeks' gestation, is the leading cause of illness and death in newborns. In the United States, more than 10% of infants are born prematurely, and this rate is substantially higher in lower-income, inner-city and Black populations. Prematurity associates with greatly increased risk of short- and long-term medical complications and can generate significant costs throughout the lives of affected children. Annual U.S. health care costs to manage short- and long-term prematurity complications are estimated to exceed $25 billion.Clinical interventions, including case management (increased patient outreach, education and specialist care), pharmacological treatment and their combination can provide benefit to pregnancies at higher risk for preterm birth. Early and sensitive risk detection, however, remains a challenge.We have developed and validated a proteomic biomarker risk predictor for early identification of pregnancies at increased risk of preterm birth. The ACCORDANT study modeled treatments with real-world patient data from a racially and ethnically diverse U.S. population to compare the benefits of risk predictor testing plus clinical intervention for higher-risk pregnancies versus no testing and standard care. Measured outcomes included neonatal and maternal length of hospital stay, associated costs and neonatal morbidity and mortality. The model projected improved outcomes and reduced costs across all subjects, including ethnic and racial minority populations, when predicted higher-risk pregnancies were treated using case management with or without pharmacological treatment. The biomarker risk predictor shows high potential to be a clinically important component of risk stratification for pregnant women, leading to tangible gains in reducing the impact of preterm birth.

Engaging Underserved Communities in COVID-19 Health Equity Implementation Research: An Analysis of Community Engagement Resource Needs and Costs.

Background: Meaningful community engagement is instrumental to effective implementation and sustainment of equitable public health interventions. Significant resources are necessary to ensure that community engagement takes place in culturally sensitive, trusted ways that optimize positive public health outcomes. However, the types and costs of resources best suited to enable meaningful community engagement in implementation research are not well-documented. This study's objectives are (1) to describe a pragmatic method for systematically tracking and documenting resources utilized for community engagement activities, (2) report resources across phases of implementation research, and (3) provide recommendations for planning and budgeting for community engagement in health equity implementation research. Methods: Community engagement partners completed a tracking log of their person-hours for community engagement activities across three phases of community engagement (startup, early, maintenance) in two implementation research projects to promote equity in COVID-19 testing and vaccination for underserved communities. Both projects completed a six-session Theory of Change (i.e., a facilitated group discussion about current and desired conditions that culminated with a set of priorities for strategic change making) over 4 months with respective Community Advisory Boards (CAB) that included community organizers, promotores, federally qualified health center providers and administrators, and public health researchers. The reported person-hours that facilitated community member engagement were documented and summarized within and across project phases. Results: For both projects, the startup phase required the highest number of person-hours (M = 60), followed by the maintenance (M = 53) and early phase (M = 47). Within the startup phase, a total of 5 community engagement activities occurred with identifying and inviting CAB members incurring the greatest number of person-hours (M = 19). Within the early phase, a total of 11 community engagement activities occurred with coordinating and leading live interpretation (Spanish) during CAB sessions incurring the greatest number of person-hours (M = 10). The maintenance phase included 11 community engagement activities with time dedicated to written translation of CAB materials into Spanish incurring the greatest number of person-hours (M = 10). Conclusions: Study findings indicate that the most significant investment of resources is required in the startup period. Needed resources decreased, albeit with a greater diversity of activities, in later phases of community engagement with Spanish language translation requiring most in the later stage of the study. This study contributes to the community engagement and implementation science literature by providing a pragmatic tracking and measurement approach and recommendations for planning for and assessing costs to facilitate meaningful community engagement in public health implementation research.

Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling.

RNA-seq is increasingly used for quantitative profiling of small RNAs (for example, microRNAs, piRNAs and snoRNAs) in diverse sample types, including isolated cells, tissues and cell-free biofluids. The accuracy and reproducibility of the currently used small RNA-seq library preparation methods have not been systematically tested. Here we report results obtained by a consortium of nine labs that independently sequenced reference, 'ground truth' samples of synthetic small RNAs and human plasma-derived RNA. We assessed three commercially available library preparation methods that use adapters of defined sequence and six methods using adapters with degenerate bases. Both protocol- and sequence-specific biases were identified, including biases that reduced the ability of small RNA-seq to accurately measure adenosine-to-inosine editing in microRNAs. We found that these biases were mitigated by library preparation methods that incorporate adapters with degenerate bases. MicroRNA relative quantification between samples using small RNA-seq was accurate and reproducible across laboratories and methods.