Spatial transcriptomics of fetal membrane-Decidual interface reveals unique contributions by cell types in term and preterm births.

During pregnancy, two fetomaternal interfaces, the placenta-decidua basalis and the fetal membrane-decidua parietals, allow for fetal growth and maturation and fetal-maternal crosstalk, and protect the fetus from infectious and inflammatory signaling that could lead to adverse pregnancy outcomes. While the placenta has been studied extensively, the fetal membranes have been understudied, even though they play critical roles in pregnancy maintenance and the initiation of term or preterm parturition. Fetal membrane dysfunction has been associated with spontaneous preterm birth (PTB, < 37 weeks gestation) and preterm prelabor rupture of the membranes (PPROM), which is a disease of the fetal membranes. However, it is unknown how the individual layers of the fetal membrane decidual interface (the amnion epithelium [AEC], the amnion mesenchyme [AMC], the chorion [CTC], and the decidua [DEC]) contribute to these pregnancy outcomes. In this study, we used a single-cell transcriptomics approach to unravel the transcriptomics network at spatial levels to discern the contributions of each layer of the fetal membranes and the adjoining maternal decidua during the following conditions: scheduled caesarian section (term not in labor [TNIL]; n = 4), vaginal term in labor (TIL; n = 3), preterm labor with and without rupture of membranes (PPROM; n = 3; and PTB; n = 3). The data included 18,815 genes from 13 patients (including TIL, PTB, PPROM, and TNIL) expressed across the four layers. After quality control, there were 11,921 genes and 44 samples. The data were processed by two pipelines: one by hierarchical clustering the combined cases and the other to evaluate heterogeneity within the cases. Our visual analytical approach revealed spatially recognized differentially expressed genes that aligned with four gene clusters. Cluster 1 genes were present predominantly in DECs and Cluster 3 centered around CTC genes in all labor phenotypes. Cluster 2 genes were predominantly found in AECs in PPROM and PTB, while Cluster 4 contained AMC and CTC genes identified in term labor cases. We identified the top 10 differentially expressed genes and their connected pathways (kinase activation, NF-κB, inflammation, cytoskeletal remodeling, and hormone regulation) per cluster in each tissue layer. An in-depth understanding of the involvement of each system and cell layer may help provide targeted and tailored interventions to reduce the risk of PTB.

Exploring team dynamics during the development of a multi-institutional cross-disciplinary translational team: Implications for potential best practices.

Introduction: A recent literature review revealed no studies that explored teams that used an explicit theoretical framework for multiteam systems in academic settings, such as the increasingly important multi-institutional cross-disciplinary translational team (MCTT) form. We conducted an exploratory 30-interview grounded theory study over two rounds to analyze participants' experiences from three universities who assembled an MCTT in order to pursue a complex grant proposal related to research on post-acute sequelae of COVID-19, also called "long COVID." This article considers activities beginning with preliminary discussions among principal investigators through grant writing and submission, and completion of reviews by the National Center for Advancing Translational Sciences, which resulted in the proposal not being scored. Methods: There were two stages to this interview study with MCTT members: pre-submission, and post-decision. Round one focused on the process of developing structures to collaborate on proposal writing and assembly, whereas round two focused on evaluation of the complete process. A total of 15 participants agreed to be interviewed in each round. Findings: The first round of interviews was conducted prior to submission and explored issues during proposal writing, including (1) importance of the topic; (2) meaning and perception of "team" within the MCTT context; and (3) leadership at different levels of the team. The second round explored best practices-related issues including (1) leadership and design; (2) specific proposal assembly tasks; (3) communication; and (4) critical events. Conclusion: We conclude with suggestions for developing best practices for assembling MCTTs involving multi-institutional teams.

Subtyping Social Determinants of Health in All of Us: Network Analysis and Visualization Approach.

Background: Social determinants of health (SDoH), such as financial resources and housing stability, account for between 30-55% of people's health outcomes. While many studies have identified strong associations among specific SDoH and health outcomes, most people experience multiple SDoH that impact their daily lives. Analysis of this complexity requires the integration of personal, clinical, social, and environmental information from a large cohort of individuals that have been traditionally underrepresented in research, which is only recently being made available through the All of Us research program. However, little is known about the range and response of SDoH in All of Us, and how they co-occur to form subtypes, which are critical for designing targeted interventions. Objective: To address two research questions: (1) What is the range and response to survey questions related to SDoH in the All of Us dataset? (2) How do SDoH co-occur to form subtypes, and what are their risk for adverse health outcomes? Methods: For Question-1, an expert panel analyzed the range of SDoH questions across the surveys with respect to the 5 domains in Healthy People 2030 (HP-30), and analyzed their responses across the full All of Us data (n=372,397, V6). For Question-2, we used the following steps: (1) due to the missingness across the surveys, selected all participants with valid and complete SDoH data, and used inverse probability weighting to adjust their imbalance in demographics compared to the full data; (2) an expert panel grouped the SDoH questions into SDoH factors for enabling a more consistent granularity; (3) used bipartite modularity maximization to identify SDoH biclusters, their significance, and their replicability; (4) measured the association of each bicluster to three outcomes (depression, delayed medical care, emergency room visits in the last year) using multiple data types (surveys, electronic health records, and zip codes mapped to Medicaid expansion states); and (5) the expert panel inferred the subtype labels, potential mechanisms that precipitate adverse health outcomes, and interventions to prevent them. Results: For Question-1, we identified 110 SDoH questions across 4 surveys, which covered all 5 domains in HP-30. However, the results also revealed a large degree of missingness in survey responses (1.76%-84.56%), with later surveys having significantly fewer responses compared to earlier ones, and significant differences in race, ethnicity, and age of participants of those that completed the surveys with SDoH questions, compared to those in the full All of Us dataset. Furthermore, as the SDoH questions varied in granularity, they were categorized by an expert panel into 18 SDoH factors. For Question-2, the subtype analysis (n=12,913, d=18) identified 4 biclusters with significant biclusteredness (Q=0.13, random-Q=0.11, z=7.5, P<0.001), and significant replication (Real-RI=0.88, Random-RI=0.62, P<.001). Furthermore, there were statistically significant associations between specific subtypes and the outcomes, and with Medicaid expansion, each with meaningful interpretations and potential targeted interventions. For example, the subtype Socioeconomic Barriers included the SDoH factors not employed, food insecurity, housing insecurity, low income, low literacy, and low educational attainment, and had a significantly higher odds ratio (OR=4.2, CI=3.5-5.1, P-corr<.001) for depression, when compared to the subtype Sociocultural Barriers. Individuals that match this subtype profile could be screened early for depression and referred to social services for addressing combinations of SDoH such as housing insecurity and low income. Finally, the identified subtypes spanned one or more HP-30 domains revealing the difference between the current knowledge-based SDoH domains, and the data-driven subtypes. Conclusions: The results revealed that the SDoH subtypes not only had statistically significant clustering and replicability, but also had significant associations with critical adverse health outcomes, which had translational implications for designing targeted SDoH interventions, decision-support systems to alert clinicians of potential risks, and for public policies. Furthermore, these SDoH subtypes spanned multiple SDoH domains defined by HP-30 revealing the complexity of SDoH in the real-world, and aligning with influential SDoH conceptual models such as by Dahlgren-Whitehead. However, the high-degree of missingness warrants repeating the analysis as the data becomes more complete. Consequently we designed our machine learning code to be generalizable and scalable, and made it available on the All of Us workbench, which can be used to periodically rerun the analysis as the dataset grows for analyzing subtypes related to SDoH, and beyond.

The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults.

Objectives: Managing multimorbidity as aging stroke patients is complex; standard self-management programs necessitate adaptations. We used visual analytics to examine complex relationships among aging stroke survivors' comorbidities. These findings informed pre-adaptation of a component of the Chronic Disease Self-Management Program. Methods: Secondary analysis of 2013-2014 Medicare claims with stroke as an index condition, hospital readmission within 90 days (n = 42,938), and 72 comorbidities. Visual analytics identified patient subgroups and co-occurring comorbidities. Guided by the framework for reporting adaptations and modifications to evidence-based interventions, an interdisciplinary team developed vignettes that highlighted multimorbidity to customize the self-management program. Results: There were five significant subgroups (z = 6.19, p < .001) of comorbidities such as obesity and cancer. We constructed 6 vignettes based on the 5 subgroups. Discussion: Aging stroke patients often face substantial disease-management hurdles. We used visual analytics to inform pre-adaptation of a self-management program to fit the needs of older adult stroke survivors.

A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.

BACKGROUND: A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications. OBJECTIVE: This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient's risk of an adverse outcome and compare its accuracy with and without patient subgroup information. METHODS: The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility. RESULTS: In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), significantly replicated (Rand Index=0.92, 0.94, 0.89; P<.001, <.001, <.01), and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.6%, 99.34%, 99.86%). In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement (0.059, 0.11) but not as measured by the C-statistic or integrated discrimination improvement. CONCLUSIONS: Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond.

Evaluation of Information Theoretic Network Meta-analysis to Rank First-Line Anticancer Regimens for Hormone Receptor-Positive, ERBB2-Negative Metastatic Breast Cancer.

Importance: Hormone receptor-positive, ERBB2 (formerly HER2/neu)-negative metastatic breast cancer (HR-positive, ERBB2-negative MBC) is treated with targeted therapy, endocrine therapy, chemotherapy, or combinations of these modalities; however, evaluating the increasing number of treatment options is challenging because few regimens have been directly compared in randomized clinical trials (RCTs), and evidence has evolved over decades. Information theoretic network meta-analysis (IT-NMA) is a graph theory-based approach for regimen ranking that takes effect sizes and temporality of evidence into account. Objective: To examine the performance of an IT-NMA approach to rank HR-positive, ERBB2-negative MBC treatment regimens. Data Sources: HemOnc.org, a freely available medical online resource of interventions, regimens, and general information relevant to the fields of hematology and oncology, was used to identify relevant RCTs. Study Selection: All primary and subsequent reports of RCTs of first-line systemic treatments for HR-positive, ERBB2-negative MBC that were referenced on HemOnc.org and published between 1974 and 2019 were included. Additional RCTs that were evaluated by a prior traditional network meta-analysis on HR-positive, ERBB2-negative MBC were also included. Data Extraction and Synthesis: RCTs were independently extracted from HemOnc.org and a traditional NMA by separate observers. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline for NMA with several exceptions: the risk of bias within individual studies and inconsistency in the treatment network were not assessed. Main Outcomes and Measures: Regimen rankings generated by IT-NMA based on clinical trial variables, including primary end point, enrollment number per trial arm, P value, effect size, years of enrollment, and year of publication. Results: A total of 203 RCTs with 63 629 patients encompassing 252 distinct regimens were compared by IT-NMA, which resulted in 151 rankings as of 2019. Combinations of targeted and endocrine therapy were highly ranked, especially the combination of endocrine therapy with cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors. For example, letrozole plus palbociclib was ranked first and letrozole plus ribociclib, third. Older monotherapies that continue to be used in RCTs in comparator groups, such as anastrozole (251 of 252) and letrozole (252), fell to the bottom of the rankings. Many regimens gravitated toward indeterminacy by 2019. Conclusions and Relevance: In this network meta-analysis study, combination therapies appeared to be associated with better outcomes than monotherapies in the treatment of HR-positive, ERBB2-negative MBC. These findings suggest that IT-NMA is a promising method for longitudinal ranking of anticancer regimens from RCTs with different end points, sparse interconnectivity, and decades-long timeframes.

SMART COVID Navigator, a Clinical Decision Support Tool for COVID-19 Treatment: Design and Development Study.

BACKGROUND: COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19's effect on patients with certain pre-existing conditions. OBJECTIVE: In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient's electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19. METHODS: The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient's medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient's medical history will affect their COVID-19 outcomes. RESULTS: The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources-enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined. CONCLUSIONS: The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient's medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions.

Prognostic Performance of Peripheral Blood Biomarkers in Identifying Seropositive Individuals at Risk of Developing Clinically Symptomatic Chagas Cardiomyopathy.

Biomarkers for prognosis-based detection of Trypanosoma cruzi-infected patients presenting no clinical symptoms to cardiac Chagas disease (CD) are not available. In this study, we examined the performance of seven biomarkers in prognosis and risk of symptomatic CD development. T. cruzi-infected patients clinically asymptomatic (C/A; n = 30) or clinically symptomatic (C/S; n = 30) for cardiac disease and humans who were noninfected and healthy (N/H; n = 24) were enrolled (1 - ß = 80%, α = 0.05). Serum, plasma, and peripheral blood mononuclear cells (PBMCs) were analyzed for heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), vimentin, poly(ADP-ribose) polymerase (PARP1), 8-hydroxy-2-deoxyguanosine (8-OHdG), copeptin, endostatin, and myostatin biomarkers by enzyme-linked immunosorbent assay (ELISA) and Western blotting. Secreted hnRNPA1, vimentin, PARP1, 8-OHdG, copeptin, and endostatin were increased by 1.4- to 7.0-fold in CD subjects versus N/H subjects (P < 0.001) and showed excellent predictive value in identifying the occurrence of infection (area under the receiver operating characteristic [ROC] curve [AUC], 0.935 to 0.999). Of these, vimentin, 8-OHdG, and copeptin exhibited the best performance in prognosis of C/S (versus C/A) CD, determined by binary logistic regression analysis with the Cox and Snell test (R2C&S = 0.492 to 0.688). A decline in myostatin and increase in hnRNPA1 also exhibited good predictive value in identifying C/S and C/A CD status, respectively. Furthermore, circulatory 8-OHdG (Wald χ2 = 15.065), vimentin (Wald χ2 = 14.587), and endostatin (Wald χ2 = 17.902) levels exhibited a strong association with changes in left ventricular ejection fraction and diastolic diameter (P = 0.001) and predicted the risk of cardiomyopathy development in CD patients. We have identified four biomarkers (vimentin, 8-OHdG, copeptin, and endostatin) that offer excellent value in prognosis and risk of symptomatic CD development. Decline in these four biomarkers and increase in hnRNPA1 would be useful in monitoring the efficacy of therapies and vaccines in halting CD. IMPORTANCE There is a lack of validated biomarkers for diagnosis of T. cruzi-infected individuals at risk of developing heart disease. Of the seven potential biomarkers that were screened, vimentin, 8-OHdG, copeptin, and endostatin exhibited excellent performance in distinguishing the clinical severity of Chagas disease. A decline in these four biomarkers can also be used for monitoring the therapeutic responses of infected patients to established or newly developed drugs and vaccines and precisely inform the patients about their progress. These biomarkers can easily be screened using the readily available plasma/serum samples in the clinical setting by an ELISA that is inexpensive, fast, and requires low-tech resources at the facility, equipment, and personnel levels.

Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions.

Several studies have shown that COVID-19 patients with prior comorbidities have a higher risk for adverse outcomes, resulting in a disproportionate impact on older adults and minorities that fit that profile. However, although there is considerable heterogeneity in the comorbidity profiles of these populations, not much is known about how prior comorbidities co-occur to form COVID-19 patient subgroups, and their implications for targeted care. Here we used bipartite networks to quantitatively and visually analyze heterogeneity in the comorbidity profiles of COVID-19 inpatients, based on electronic health records from 12 hospitals and 60 clinics in the greater Minneapolis region. This approach enabled the analysis and interpretation of heterogeneity at three levels of granularity (cohort, subgroup, and patient), each of which enabled clinicians to rapidly translate the results into the design of clinical interventions. We discuss future extensions of the multigranular heterogeneity framework, and conclude by exploring how the framework could be used to analyze other biomedical phenomena including symptom clusters and molecular phenotypes, with the goal of accelerating translation to targeted clinical care.

How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach.

BACKGROUND: When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. OBJECTIVE: This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. METHODS: We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. RESULTS: The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. CONCLUSIONS: The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.