Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=2&sort=-other_investigators
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-other_investigators", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-other_investigators", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=-other_investigators", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-other_investigators" }, "data": [ { "type": "Grant", "id": "15652", "attributes": { "award_id": "2430389", "title": "NSF I-Corps Hub (Track 1): Northwest Region", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "I-Corps Hubs" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": null, "award_amount": 15000000, "principal_investigator": { "id": 32157, "first_name": "Richard", "last_name": "Lyons", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32156, "first_name": "Sosale S", "last_name": "Sastry", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps Hubs project is the development of infrastructure needed for entrepreneurial training for academic science, technology, engineering, and math (STEM) researchers and high potential community teams. This training will accelerate the commercialization of cutting-edge technologies and enhance regional innovation. It will also support workforce readiness in a region that is rapidly changing as the result of post-pandemic, economic and geographic dynamics of “meta cities,” and net in and out migration to and from rural and underserved areas throughout the region. In addition, Hub activities will provide the training needed to power other NSF initiatives promoting commercialization and innovation. Developing these entrepreneurial skills for both academic researchers and throughout the region’s workforce amplifies the economic and societal impact of NSF and other-funded basic research while accelerating the growth of startups, providing economic benefit to the region and beyond. This will be accomplished in an inclusive way to multiply opportunities, increases national competitiveness, and secures an economic future for all. This I-Corps Hubs project is based on the aim to advance the translation of deep technologies into societal and economic impact. This collaboration covers a large geographic area, inclusive of both urban and rural locations throughout Alaska, California, Oregon, and Washington. The region shares distinct commonalities between the proposed Partners and synergies that may be leveraged to serve a uniquely diverse population and maximize economic impact throughout the region. The proposed Hub activities will be designed to support regional and national I-Corps training through team expansion, fuel regional and national economic growth, produce actionable entrepreneurial research, and broaden participation among underrepresented areas and populations. The Hub Partners share a mission to reduce time and risk associated with translating top research from lab-to-market, while expanding educational and economic opportunity throughout the region. Through education, evidence, and experience, the Hub will drive creation of sustainable, scalable technology-based startups with both regional and national impacts. The Hub will strive to raise awareness of the value of entrepreneurship among science and engineering faculty and students, using a variety of programs designed for inclusivity and meeting scientists and engineers at their knowledge and skill level, whether they are curious about the fit of their technology to solve an industry problem or are committed company founders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15650", "attributes": { "award_id": "2447173", "title": "Doctoral Dissertation Research: Task Performance and Causal Claims: Leveraging Network Analysis and Large Language Models to Extract Information from Organizational Hazard Texts", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "Decision, Risk & Mgmt Sci" ], "program_reference_codes": [], "program_officials": [ { "id": 577, "first_name": "Claudia", "last_name": "Gonzalez-Vallejo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-03-01", "end_date": null, "award_amount": 12599, "principal_investigator": { "id": 10571, "first_name": "Carter", "last_name": "Butts", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 177, "ror": "", "name": "University of California-Irvine", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 32153, "first_name": "Sabrina", "last_name": "Mai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 177, "ror": "", "name": "University of California-Irvine", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Crises – particularly those rising to the level of disasters –demand an organized response. In complex systems of organizational disaster response, core tasks such as communication and coordination of activities become large-scale challenges. This study evaluates such challenges in two research thrusts. The first thrust examines the broader discourse created by official online communications disseminated to the public during the COVID-19 pandemic. The second thrust examines the extent to which an organization's task performance impacts effective collaborations in an emergent multi-organizational disaster response network, with a specific case study of the 2005 Hurricane Katrina. The “lessons learned\" from our communications studies are used to synthesize concrete organizational communication strategies that can help practitioners and officials better engage stakeholders, thereby building the trust between the public and officials that is critical for disaster management. The results of our collaboration study inform organizational training efforts for hazard events, which may decrease the friction of collaboration and coordination efforts in response to large-scale disasters and thus contribute to the protection of lives and property. This study aims to provide insights at multiple levels of the organizational response process, from shared task performance among organizations to the broader structure of public-facing discourse around health hazards and the emergence of multi-organizational response networks. This study leverages the strengths of machine learning-based natural language processing methodologies and network analysis techniques to extract from, and analyze massive corpuses of hazards communications. Thus, this study contributes to methodologies of large-scale information extraction, decreasing costs previously associated with obtaining high-quality data from records while increasing the potential value of underutilized historical case studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15635", "attributes": { "award_id": "1R01CA285395-01A1", "title": "Resolution of inflammation in chemical-induced cancer", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [ { "id": 22996, "first_name": "THOMAS K.", "last_name": "HOWCROFT", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": "2029-12-31", "award_amount": 749474, "principal_investigator": { "id": 31455, "first_name": "Dipak", "last_name": "Panigrahy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31456, "first_name": "Charles Nicholas", "last_name": "Serhan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32137, "first_name": "Hooman Henry", "last_name": "Rashidi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 771, "ror": "https://ror.org/04drvxt59", "name": "Beth Israel Deaconess Medical Center", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "While exposure to toxic environmental chemicals can mutate DNA, causing cancer, the underlying non- genotoxic mechanisms that support malignant transformation remain largely uncharacterized. Transformed cells require growth factors to support and stimulate them in carcinogenesis, resulting in hyperinflammation and a cytokine storm. A paradigm shift is emerging in understanding the resolution of acute inflammation as an active biochemical process with our discovery of novel specialized pro-resolving mediators (SPMs), such as resolvins, and endogenous resolution programs. At nanogram doses, SPMs stimulate macrophage-mediated clearance of debris and counter-regulate pro-inflammatory cytokine (e.g., TNFα) production without immune suppression. Despite approaches to block systemic inflammation, there are no “pro-resolving” therapies for cancer treatment. Furthermore, the impact of carcinogens on eicosanoids and pro-resolving lipid media- tors, both critical regulators of the initiation and resolution of inflammation, is completely unknown. In response to NOSI-20-018 “Promoting Fundamental Research in Inflammation Resolution,” this proposal focuses on ad- vancing our recent results indicating that stimulating the resolution of inflammation prevents carcingogen- induced tumor growth by counter-regulating cytokine storms. Therefore, the overarching theme of this pro- posal is to elucidate the underlying processes of failed resolution of inflammation initiated by environmental chemicals. We will rely on a set of established experimental systems, including genetic and pharmacological manipulation of SPMs and their receptors in animal models and macrophage studies. In Specific Aim 1, we will profile lipid autacoid mediators, including eicosanoids (PGs, LTs) and SPMs, as well as cytokines to test our innovative hypothesis that environmental chemicals trigger dysregulation of SPMs that leads to an uncon- trolled cytokine & eicosanoid storm. We will evaluate pro-resolving lipid mediators as interventional targets in chemical-induced cancer. We will elucidate cellular, intracellular, and receptor-mediated pro-resolving and anti-tumor mechanisms of resolvins. In Specific Aim 2, the mechanisms that mediate failed resolution of in- flammation in chemical-induced cancer will be investigated. These studies will complement Specific Aim 3 in a multi-pronged approach that will evaluate SPM analog mimetics and humanized nano-pro-resolving medi- cines (NPRMs) carrying SPM cargo as novel targeted treatment approaches to prevent chemical-induced can- cer. These studies will offer new animal models to evaluate toxic chemicals and novel therapies to counter cancer. We will then connect our preclinical findings to clinical disease phenotypes using a new computational framework to understand failed resolution of inflammation in chemical-induced cancers. As SPMs are safe and effective in inflammatory disorders, the proposed studies shall provide the basis for rapid translation of resolution-directed treatments in humans as a new direction to prevent and/or reduce cancers that arise from environmental carcinogens.", "keywords": [ "Acute", "Aflatoxin B1", "Agonist", "Animal Model", "Animals", "Anti-Inflammatory Agents", "Apoptotic", "Autacoids", "Biochemical Process", "CD59 Antigen", "Cancer Control", "Cancer Etiology", "Cancer Patient", "Carcinogens", "Cell Death Induction", "Chemical Stimulation", "Chemicals", "Clinical", "Complement", "Cytotoxic Chemotherapy", "DNA", "Data", "Disease", "Dose", "Eicosanoids", "Environmental Carcinogens", "Environmental and Occupational Exposure", "Event", "Excision", "Exposure to", "Failure", "Family", "Genetic", "Growth Factor", "Homeostasis", "Human", "Human body", "Immunosuppression", "Inflammation", "Inflammation Mediators", "Inflammatory", "Intervention", "Laboratories", "Lipids", "Machine Learning", "Macrophage", "Malignant - descriptor", "Malignant Neoplasms", "Mediating", "Mediator", "Medicine", "Micrometastasis", "Molecular", "Mutate", "Necrosis", "Occupational", "Pathologic", "Phagocytosis", "Poison", "Preclinical data", "Prevention", "Process", "Production", "Recurrent tumor", "Research Personnel", "Resolution", "Risk Factors", "Role", "Severity of illness", "Signal Transduction", "System", "TNF gene", "Testing", "Time", "Toxic effect", "Toxin", "Translations", "Treatment Efficacy", "analog", "cancer initiation", "cancer therapy", "carcinogenesis", "cell transformation", "chemotherapy", "combat", "computer framework", "cytokine", "cytokine release syndrome", "disease phenotype", "environmental chemical", "fundamental research", "innovation", "lipid mediator", "lipidome", "mimetics", "nano", "neoplastic cell", "novel", "novel strategies", "novel therapeutics", "pharmacologic", "prevent", "programs", "receptor", "response", "systemic inflammatory response", "targeted treatment", "tumor", "tumor growth", "tumor progression", "tumorigenesis" ], "approved": true } }, { "type": "Grant", "id": "15631", "attributes": { "award_id": "1R21HL177773-01", "title": "Emulated Target Trials of Steroids in Patients with Acute Respiratory Distress Syndrome", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 22454, "first_name": "GUOFEI", "last_name": "Zhou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-15", "end_date": "2026-12-31", "award_amount": 153099, "principal_investigator": { "id": 27913, "first_name": "Elias", "last_name": "Baedorf Kassis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32134, "first_name": "Li-Wei H", "last_name": "Lehman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32135, "first_name": "Zachary", "last_name": "Shahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Acute respiratory distress syndrome (ARDS) is a severe form of lung injury with significant public health implications due to severe morbidity and mortality. The need to utilize existing data to inform prospective research and clinical decision making was emphasized during the COVID pandemic, when ARDS became a leading cause of death, and clinicians were forced to operate outside of existing evidence. `Dynamic treatment regimes' (DTRs) are rules for making treatment decisions sequentially at multiple time-points based on a patient's evolving history. Most relevant treatment strategies for ARDS are DTRs. DTRs may be evaluated in randomized trials, however it is infeasible to conduct randomized trials testing all DTRs of interest. This grant proposes `target trial emulation' from observational data using `g-methods' for confounding adjustment to address multiple gaps in our knowledge about ARDS care. Target trial emulation with g-methods is essentially the gold standard for causal inference about DTRs from observational data, but the approach is underutilized in the critical care setting. We will also explore generating personalized DTRs based on machine learning derived phenotypes. The investigation will utilize two large datasets—including the Medical Information Mart for Intensive Care (MIMIC) IV database and the eICU collaborative research database—representing a wide geographic and demographic spectrum. We will specifically address questions surrounding initiation, duration and dosing of steroids among patients with ARDS in the following clinical aims. Using target trial emulations and g-methods, we will: 1) estimate effects of early and sustained steroid use compared with delayed, abbreviated, or no-steroids regimes across a range of doses in patients with ARDS, 2) estimate the effects of dynamic strategies for steroid initiation based on evolving markers of disease severity in ARDS patients, and 3) identify the effects of steroid strategies across cohorts defined by joint ARDS and sepsis status. We will utilize multiple datasets to assess stability of findings across centers. The project represents a collaborative effort between experts in critical care medicine (with a specialty in mechanical ventilation), critical care data science, and causal inference. Our results will address important gaps in clinical knowledge about treatment of ARDS and inform the design of future randomized trials. Our study designs, code, and constructed cohorts will also provide valuable templates for other researchers to use in future observational studies, which we hope will broadly improve the quality of evidence from observational data in critical care.", "keywords": [ "Abbreviations", "Acute Respiratory Distress Syndrome", "Address", "Admission activity", "American", "Attenuated", "COVID-19 pandemic", "Caring", "Cause of Death", "Clinical", "Code", "Cohort Studies", "Confusion", "Critical Care", "Critical Illness", "Data", "Data Science", "Data Set", "Databases", "Development", "Disease Marker", "Dose", "Dual-Energy X-Ray Absorptiometry", "Early treatment", "Enrollment", "Equilibrium", "European", "Future", "Geography", "Grant", "Hospital Mortality", "Inflammation", "Intensive Care", "Intensive Care Units", "Investigation", "Joints", "Knowledge", "Machine Learning", "Mechanical ventilation", "Mechanics", "Medical", "Medicine", "Methodology", "Methods", "Modeling", "Modification", "Morbidity - disease rate", "Observational Study", "Outcome", "Output", "Patients", "Phenotype", "Population", "Population Study", "Public Health", "Randomized Controlled Trials", "Recording of previous events", "Research", "Research Design", "Research Personnel", "Resources", "Respiratory Failure", "Sepsis", "Septic Shock", "Severity of illness", "Societies", "Steroids", "Testing", "Time", "Treatment outcome", "Uncertainty", "alternative treatment", "burden of illness", "clinical decision-making", "cohort", "cost", "design", "improved", "interest", "large datasets", "lung injury", "medical specialties", "mortality", "multiple datasets", "patient population", "prospective", "randomized trial", "sepsis induced ARDS", "treatment duration", "treatment effect", "treatment strategy", "trial design" ], "approved": true } }, { "type": "Grant", "id": "15631", "attributes": { "award_id": "1R21HL177773-01", "title": "Emulated Target Trials of Steroids in Patients with Acute Respiratory Distress Syndrome", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 22454, "first_name": "GUOFEI", "last_name": "Zhou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-15", "end_date": "2026-12-31", "award_amount": 153099, "principal_investigator": { "id": 27913, "first_name": "Elias", "last_name": "Baedorf Kassis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32134, "first_name": "Li-Wei H", "last_name": "Lehman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32135, "first_name": "Zachary", "last_name": "Shahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Acute respiratory distress syndrome (ARDS) is a severe form of lung injury with significant public health implications due to severe morbidity and mortality. The need to utilize existing data to inform prospective research and clinical decision making was emphasized during the COVID pandemic, when ARDS became a leading cause of death, and clinicians were forced to operate outside of existing evidence. `Dynamic treatment regimes' (DTRs) are rules for making treatment decisions sequentially at multiple time-points based on a patient's evolving history. Most relevant treatment strategies for ARDS are DTRs. DTRs may be evaluated in randomized trials, however it is infeasible to conduct randomized trials testing all DTRs of interest. This grant proposes `target trial emulation' from observational data using `g-methods' for confounding adjustment to address multiple gaps in our knowledge about ARDS care. Target trial emulation with g-methods is essentially the gold standard for causal inference about DTRs from observational data, but the approach is underutilized in the critical care setting. We will also explore generating personalized DTRs based on machine learning derived phenotypes. The investigation will utilize two large datasets—including the Medical Information Mart for Intensive Care (MIMIC) IV database and the eICU collaborative research database—representing a wide geographic and demographic spectrum. We will specifically address questions surrounding initiation, duration and dosing of steroids among patients with ARDS in the following clinical aims. Using target trial emulations and g-methods, we will: 1) estimate effects of early and sustained steroid use compared with delayed, abbreviated, or no-steroids regimes across a range of doses in patients with ARDS, 2) estimate the effects of dynamic strategies for steroid initiation based on evolving markers of disease severity in ARDS patients, and 3) identify the effects of steroid strategies across cohorts defined by joint ARDS and sepsis status. We will utilize multiple datasets to assess stability of findings across centers. The project represents a collaborative effort between experts in critical care medicine (with a specialty in mechanical ventilation), critical care data science, and causal inference. Our results will address important gaps in clinical knowledge about treatment of ARDS and inform the design of future randomized trials. Our study designs, code, and constructed cohorts will also provide valuable templates for other researchers to use in future observational studies, which we hope will broadly improve the quality of evidence from observational data in critical care.", "keywords": [ "Abbreviations", "Acute Respiratory Distress Syndrome", "Address", "Admission activity", "American", "Attenuated", "COVID-19 pandemic", "Caring", "Cause of Death", "Clinical", "Code", "Cohort Studies", "Confusion", "Critical Care", "Critical Illness", "Data", "Data Science", "Data Set", "Databases", "Development", "Disease Marker", "Dose", "Dual-Energy X-Ray Absorptiometry", "Early treatment", "Enrollment", "Equilibrium", "European", "Future", "Geography", "Grant", "Hospital Mortality", "Inflammation", "Intensive Care", "Intensive Care Units", "Investigation", "Joints", "Knowledge", "Machine Learning", "Mechanical ventilation", "Mechanics", "Medical", "Medicine", "Methodology", "Methods", "Modeling", "Modification", "Morbidity - disease rate", "Observational Study", "Outcome", "Output", "Patients", "Phenotype", "Population", "Population Study", "Public Health", "Randomized Controlled Trials", "Recording of previous events", "Research", "Research Design", "Research Personnel", "Resources", "Respiratory Failure", "Sepsis", "Septic Shock", "Severity of illness", "Societies", "Steroids", "Testing", "Time", "Treatment outcome", "Uncertainty", "alternative treatment", "burden of illness", "clinical decision-making", "cohort", "cost", "design", "improved", "interest", "large datasets", "lung injury", "medical specialties", "mortality", "multiple datasets", "patient population", "prospective", "randomized trial", "sepsis induced ARDS", "treatment duration", "treatment effect", "treatment strategy", "trial design" ], "approved": true } }, { "type": "Grant", "id": "15630", "attributes": { "award_id": "1R01AI182308-01A1", "title": "Integrated Host/Microbe (IHM) Metagenomics of the Lower Airway to Diagnose PediatricRespiratory Infections, Identify Etiologic Pathogens, and Predict Outcomes", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 8282, "first_name": "Inka I", "last_name": "Sastalla", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": "2029-12-31", "award_amount": 781129, "principal_investigator": { "id": 32132, "first_name": "Charles", "last_name": "Langelier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32133, "first_name": "PETER M", "last_name": "MOURANI", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 768, "ror": "https://ror.org/043mz5j54", "name": "University of California, San Francisco", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Lower respiratory tract infection (LRTI) leads to more deaths each year than any other type of infection and disproportionately affects children. The past three years have seen record pediatric hospitalizations due to RSV, influenza, and SARS-CoV-2, highlighting the burden of LRTI in this vulnerable demographic. LRTI remains diagnostically challenging in children due to high rates of viral/bacterial co-infections, noninfectious syndromes that mimic LRTI, frequent incidental pathogen carriage, and the limitations of existing clinical diagnostics. As a result, accurate and timely LRTI diagnosis is difficult to achieve in pediatric critical care, leading to the inappropriate use of empirical antibiotics, the emergence of resistant pathogens, and adverse patient outcomes. Respiratory infections involve a dynamic relationship among three key features: pathogens, the airway microbiome, and the host immune response. However, existing clinical tests rely primarily on pathogen detection, limiting their diagnostic and prognostic utility. Our group has pioneered integrated host/microbe (IHM) metagenomic next generation sequencing (mNGS) methods that enable accurate, culture-independent LRTI diagnosis by simultaneously assessing all three key LRTI features from a single tracheal aspirate sample. Here, we propose a prospective, multicenter cohort study of 400 critically ill children with acute respiratory failure requiring mechanical ventilation that is designed to validate and extend the IHM diagnostic we developed. Aim 1 will independently validate the performance of our existing IHM LRTI diagnostic classifier in distinguishing LRTI from non-infectious acute respiratory conditions and in identifying likely etiologic pathogens. Aim 2 will develop a novel host gene expression classifier specifically for bacterial LRTI rule-out, which would reduce unnecessary antibiotic use in a principled manner. For both Aims 1 and 2, we will additionally develop parsimonious host-based PCR versions of the classifiers for rapid, point-of-care LRTI diagnosis and bacterial LRTI rule-out where mNGS capacity is unavailable. Finally, Aim 3 will develop novel IHM classifiers to predict LRTI outcomes, including prolonged mechanical ventilation and persistent acute respiratory distress syndrome (ARDS), which can facilitate prioritization of resources and intensive interventions to the highest-risk patients. This study promises to address the unmet need for accurate molecular LRTI diagnostics that detect emerging pathogens, enable judicious antimicrobial treatment, and predict outcomes in critically ill children. Our multidisciplinary team of translational scientists with experience in innovative metagenomic approaches is well- positioned to accomplish the study goals. The results of this study will directly inform the design of a future clinical trial evaluating the impact of IHM diagnostics on clinical management and patient outcomes.", "keywords": [ "2019-nCoV", "Acute", "Acute Respiratory Distress Syndrome", "Acute respiratory failure", "Address", "Affect", "Antibiotic Therapy", "Antibiotics", "Area Under Curve", "Bacterial Infections", "Biological Markers", "Biological Testing", "COVID-19 pandemic", "Cessation of life", "Child", "Childhood", "Clinical", "Clinical Management", "Clinical Trials", "Cohort Studies", "Communicable Diseases", "Critical Care", "Critically ill children", "Data", "Decision Making", "Detection", "Diagnosis", "Diagnostic", "Diagnostic tests", "Early identification", "Elements", "Enrollment", "Enterovirus", "Epidemiology", "Etiology", "Future", "Gene Expression", "Genetic Transcription", "Goals", "Hospitalization", "Immune response", "Infection", "Influenza", "Innovative Therapy", "Intervention", "Intubation", "Lower Respiratory Tract Infection", "Mechanical ventilation", "Medical center", "Medicine", "Metagenomics", "Methods", "Microbe", "Molecular", "Morbidity - disease rate", "Outcome", "Pathogen detection", "Patient-Focused Outcomes", "Patients", "Performance", "Positioning Attribute", "Preparation", "Prognosis", "Prospective cohort study", "Randomized Controlled Trials", "Reporting", "Resistance", "Resource Allocation", "Resources", "Respiratory Tract Infections", "Sampling", "Scientist", "Specimen", "Syndrome", "Testing", "Trachea", "Translational Research", "Validation", "Viral", "accurate diagnostics", "adjudication", "antimicrobial", "aspirate", "clinical application", "clinical care", "clinical diagnostics", "clinical investigation", "co-infection", "cohort", "design", "diagnostic assay", "diagnostic value", "emerging pathogen", "experience", "high risk", "improved", "infection management", "innovation", "lung microbiome", "machine learning method", "metagenomic sequencing", "microbial", "microbiome", "mortality", "multidisciplinary", "next generation sequencing", "novel", "novel diagnostics", "outcome prediction", "pathogen", "pathogenic microbe", "point of care", "prevent", "prognostic", "prognostic assays", "prognostic value", "prospective", "recruit", "respiratory", "respiratory microbiome", "respiratory pathogen", "secondary analysis", "success", "translational scientist" ], "approved": true } }, { "type": "Grant", "id": "15628", "attributes": { "award_id": "1R34MH134930-01A1", "title": "Building Healthy Eating and Self-Esteem Together for University Students (BEST-U): A Pilot Randomized Controlled Trial of an mHealth Intervention for Binge-Spectrum Disorders", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Mental Health (NIMH)" ], "program_reference_codes": [], "program_officials": [ { "id": 31611, "first_name": "Marcy Ellen", "last_name": "Burstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": "2027-12-31", "award_amount": 249521, "principal_investigator": { "id": 32128, "first_name": "Kara Alise", "last_name": "Christensen Pacella", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32129, "first_name": "Kelsie Terese", "last_name": "Forbush", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1496, "ror": "", "name": "UNIVERSITY OF KANSAS LAWRENCE", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "Eating disorders (EDs) are a critical concern on college campuses. Moreover, since the COVID-19 pandemic, ED prevalence has increased by 62% in university women and 140% in university men. Resources are inadequate to meet demand, leading to delays in students’ access to treatment. Untreated (or poorly treated) EDs result in greater healthcare utilization and costs to students, as well as lower academic achievement and increased psychiatric disability and mortality, suggesting a critical need for quality ED treatment on university campuses and to rethink treatment delivery. One way to address this gap in care delivery is to improve treatment accessibility and scalability, such as dissemination via mobile apps. Guided self-help Cognitive- Behavior Therapy (CBT-gsh) is a cost-effective option that can be delivered by non-traditional service providers, such as nurses and physicians. Our scientific premise is that the mHealth CBT-gsh app, Building Healthy Eating and Self-Esteem Together for University Students (BEST-U), will lead to reductions in binge eating (primary outcome) through reductions in dietary restraint and weight/shape concerns (target mechanisms). Our pilot data showed strong support for our premise, specifically the need for brief, targeted mHealth interventions in students and the ability of the program to significantly reduce binge eating and impairment and increase wellbeing, with high user acceptability and low drop-out rates. However, prior to implementing BEST-U at other universities, we need to test the intervention in a real-world setting with the end goal of disseminating at scale. Our objectives are to: 1) conduct an effectiveness test of BEST-U compared to a similar dose of present-centered therapy (PCT) in students with non-low weight binge-spectrum EDs and 2) test target mechanisms that lead to changes in binge eating. To accomplish our objectives, we will test the following specific aims: 1) conduct an RCT of BEST-U (N=47) compared to a similar dose of PCT (N=47) in students with non-low weight binge-spectrum EDs; 2) test target mechanisms that lead to changes in binge eating and other ED symptoms; and 3) characterize barriers and facilitators to implementation across two campuses. Our exploratory aim will test food reinforcement and food-choice impulsivity as potential target mechanisms or response moderators of rapid response in binge eating. Given the rapidly rising rates of EDs and the lack of existing treatment resources, the proposed study is innovative and significant because it will provide a scalable treatment to fill gaps in care to promote student wellness and educational attainment. Our pilot data showed initial efficacy for BEST-U, yet the proposed study is necessary to validate the treatment in a student health setting prior to large-scale dissemination. Furthermore, given that few studies have identified underlying mechanisms that explain how CBT-gsh works and for whom, this study may lead to improved ability to tailor or modify existing CBT-gsh (e.g., personalized medicine approaches) or lead to novel intervention development for students who are unlikely to respond rapidly (or at all) to first-line CBT interventions for EDs.", "keywords": [ "Academic achievement", "Address", "Administrator", "Binge Eating", "COVID-19 pandemic", "Caring", "Clinical", "Clinical Services", "Cognitive Therapy", "Community Practice", "Data", "Disease", "Dose", "Dropout", "Eating", "Eating Disorders", "Education", "Effectiveness", "Effectiveness of Interventions", "Elements", "Emotions", "Ensure", "Focus Groups", "Food", "Frequencies", "Goals", "Health", "Health Care", "Health Personnel", "Health Services Accessibility", "Healthy Eating", "Impairment", "Impulsivity", "Intervention", "Lead", "Mental Health", "Mission", "Mobile Health Application", "Monitor", "National Institute of Mental Health", "Nurses", "Outcome", "Pattern", "Personal Satisfaction", "Physicians", "Population", "Postdoctoral Fellow", "Prevalence", "Prevention", "Professional counselor", "Psychological reinforcement", "Public Health", "Randomized", "Randomized Controlled Trials", "Reporting", "Research", "Resources", "Risk", "Shapes", "Student Health Services", "Students", "Symptoms", "Testing", "Theoretical Domains framework", "Training", "Treatment outcome", "United States National Institutes of Health", "Universities", "Weight", "Woman", "Work", "care delivery", "college", "cost", "cost effective", "dietary", "doctoral student", "effectiveness testing", "evidence base", "excessive exercise", "follow-up", "health administration", "health care service utilization", "health care settings", "implementation facilitators", "implementation framework", "implementation intervention", "implementation strategy", "improved", "innovation", "mHealth", "men", "mhealth interventions", "mobile application", "mortality", "multi-site trial", "novel", "personalized medicine", "practice setting", "primary outcome", "programs", "psychiatric disability", "psychologic", "purge", "response", "restraint", "secondary outcome", "self esteem", "self help", "service providers", "therapy development", "treatment arm", "university student" ], "approved": true } }, { "type": "Grant", "id": "15617", "attributes": { "award_id": "1R01AI189657-01", "title": "Synergistic Nanobodies for Pandemic Preparedness", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 6115, "first_name": "DIPANWITA", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-02-24", "end_date": "2030-01-31", "award_amount": 867563, "principal_investigator": { "id": 32114, "first_name": "JOHN D.", "last_name": "AITCHISON", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32115, "first_name": "MICHAEL P", "last_name": "ROUT", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 763, "ror": "https://ror.org/0420db125", "name": "Rockefeller University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Betacoronaviruses (beta-CoVs), including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, have reshaped our understanding of pandemic preparedness. These viruses demonstrate a remarkable ability to mutate and evade defenses, continuing to infect populations worldwide despite extensive vaccination efforts and antiviral therapies. The chameleon-like nature of SARS-CoV-2, particularly its modifications to the Spike protein, consistently outpaces existing countermeasures, necessitating new strategies. This proposal introduces a pioneering class of nanobodies (Nbs), engineered from the immune system of llamas, designed to provide comprehensive protection against all beta-CoVs. These biologics not only advance treatment but also signify a pivotal step in pandemic preparedness, equipping us to outpace the relentless evolution of beta-CoVs. Our innovation lies in developing multivalent, synergistic combinations of broad-spectrum, high-efficacy Nbs. By harnessing these combinations, we amplify their efficacy and scope, concurrently increasing their resistance to viral mutations. Administered intranasally or directly to the lungs, these Nbs serve as both prophylactics and therapeutic agents. Our first Aim is to strategically expand upon our proven repertoires to identify, isolate, and characterize a much larger and more diverse repertoire of Nbs that collectively are strongly neutralizing across the beta-CoVs. We will use cutting-edge methods to produce diverse Nbs from llamas exposed to spike proteins of various beta- CoVs, selecting those with high affinity, specificity, and stability. We aim to discover synergistic, escape-resistant Nb pairs through combination testing and structural analysis. In our second Aim, we will optimize critical parameters important for developing broadly neutralizing Nb combinations and derivatives for human use. We will evaluate the in vivo synergistic potential of Nbs targeting major threats like MERS-CoV and SARS-CoV-2, and engineer Nbs to optimize their properties and efficacy in preparation for clinical trials. Deploying these pre- programmed Nbs at an outbreak's onset will protect first responders and medical personnel, reduce hospital surges, limit transmission, and buy time for new vaccine development and rollout. They will also provide crucial support to immunocompromised individuals, safeguarding the most vulnerable from the start. We hypothesize that our synergistic Nb combinations will introduce new beta-CoV neutralization methods, effectively prevent and treat infections, and maintain efficacy against emerging beta-CoV threats.", "keywords": [ "2019-nCoV", "Acceleration", "Affinity", "Anti-viral Therapy", "Antibodies", "Binding", "Biological Products", "Cells", "Clinical Trials", "Collaborations", "Development", "Disease Outbreaks", "Ensure", "Evolution", "Exhibits", "Exposure to", "Future", "Health Personnel", "Hospitals", "Human", "Immunocompromised Host", "Individual", "Infection", "Inhalators", "Intranasal Administration", "Llama", "Lung", "Membrane Glycoproteins", "Methods", "Middle East Respiratory Syndrome Coronavirus", "Modification", "Monoclonal Antibodies", "Mutate", "Mutation", "Nature", "Outcome", "Population", "Preparation", "Prevention", "Property", "Proteins", "Readiness", "Resistance", "SARS coronavirus", "SARS-CoV-2 variant", "Shapes", "Specificity", "Sum", "System", "Testing", "Therapeutic Agents", "Time", "Vaccination", "Variant", "Viral", "Virus", "betacoronavirus", "biophysical properties", "design", "first responder", "global health", "immunoengineering", "immunogenicity", "in vivo", "innovation", "insight", "nanobodies", "nanoengineering", "novel vaccines", "pandemic disease", "pandemic preparedness", "prevent", "prophylactic", "respiratory virus", "stem", "synergism", "transmission process", "vaccine development", "vaccine distribution" ], "approved": true } }, { "type": "Grant", "id": "15616", "attributes": { "award_id": "1R01AI185617-01A1", "title": "RNA epigenetic modifications in SARS-CoV-2", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 27781, "first_name": "Mary Katherine Bradford", "last_name": "Plimack", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-02-01", "end_date": "2030-01-31", "award_amount": 582618, "principal_investigator": { "id": 26224, "first_name": "Jianrong", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32113, "first_name": "Mark E.", "last_name": "Peeples", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 778, "ror": "", "name": "OHIO STATE UNIVERSITY", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "Post-transcriptional RNA modifications are widespread and regulates numerous biological processes including RNA metabolism, protein translation, gene expression, and disease. Among the more than 180 types of RNA modifications, N6-methyladenosine (m6A) and pseudouridine (Ψ) are the two most prevalent. The m6A modification is catalyzed by the host RNA methyltransferase complex of METTL3 and METTL14. The Ψ modification is converted from the nucleoside uridine (U) by the host pseudouridine synthases (PUSs). Despite being discovered in the 1950s, the biological functions of the m6A and Ψ modifications in the context of virus infection remain poorly understood. This project is built upon our recent development of high throughput sequencing techniques that have enabled mapping of m6A and ψ sites at a single base resolution. Using these techniques, we discovered that SARS-CoV-2 RNA isolated from well-differentiated primary human bronchial epithelial (HBE) cultures that include their in vivo target cells is heavily modified with m6A and ψ. In addition, we have found that depletion of several m6A and ψ writer proteins decreases SARS-CoV-2 replication in HBE culture. These findings led to our hypothesis that SARS-CoV-2 acquires m6A and Ψ modifications in its RNA to maximize virus replication. Thus, the goal of this project is to determine the mechanisms by which RNA m6A and ψ modifications modulate SARS-CoV-2 replication, gene expression, innate and adaptive immunity, and pathogenesis. In Aim 1, we will use a CRISP-Cas 9 technique to knock out host RNA m6A methyltransferases and PUSs in HBE cultures to determine the role(s) of m6A and Ψ modifications in the SARS-CoV-2 life cycle. We will also use knockout mice to examine the role(s) of m6A and Ψ modification in SARS-CoV-2 replication in vivo. We will also identify the specific PUS enzyme(s) that catalyze pseudouridylation on SARS-CoV-2 RNA. In Aim 2, we will mutate the m6A and/or ψ sites in the SARS-CoV-2 genomic RNA and use the reverse genetics system to generate recombinant SARS-CoV-2 lacking m6A and/or ψ modification sites and use them to determine the roles of m6A and ψ modifications on viral RNA metabolism, encapsidation, RNA replication, viral protein translation, and innate immunity. In Aim 3, we will determine whether m6A and ψ modifications modulate mucosal and adaptive immune responses of SARS-CoV-2 live attenuated vaccines (LAVs) and determine whether LAVs lacking m6A and/or ψ are more immunogenic in golden Syrian hamsters. Upon completion of this project, we expect to have unravelled the mechanisms by which m6A and Ψ modifications modulate the SARS- CoV-2 replication cycle, leading to the development of novel and improved LAVs and therapies for COVID-19 that target these RNA modifications.", "keywords": [ "2019-nCoV", "Adenosine", "Anti-viral Agents", "Anti-viral Therapy", "Antibodies", "Attenuated", "Attenuated Vaccines", "B-Lymphocytes", "Biological Process", "COVID-19", "COVID-19 treatment", "COVID-19 vaccine", "Carbon", "Cells", "Cessation of life", "Collaborations", "Complementary DNA", "Complex", "Development", "Disease", "Effectiveness", "Enzymes", "Epigenetic Process", "Exposure to", "Gene Expression", "Genetic Recombination", "Genetic Transcription", "Goals", "Hamsters", "High-Throughput Nucleotide Sequencing", "High-Throughput RNA Sequencing", "Human", "Immune response", "Infection", "Innate Immune Response", "Intramuscular", "Isomerism", "Knock-out", "Knockout Mice", "Life Cycle Stages", "Maps", "Mediating", "Mesocricetus auratus", "Methylation", "Methyltransferase", "Modeling", "Modification", "Mucosal Immune Responses", "Mucous Membrane", "Mutate", "Natural Immunity", "Nucleosides", "Nucleotides", "Pathogenesis", "Pharmaceutical Preparations", "Positioning Attribute", "Post-Transcriptional RNA Processing", "Process", "Proteins", "Pseudouridine", "Public Health", "RNA", "RNA Stability", "RNA metabolism", "RNA purification", "RNA replication", "Reader", "Recombinants", "Resolution", "Role", "Rotation", "SARS-CoV-2 immunity", "SARS-CoV-2 infection", "SARS-CoV-2 variant", "Signal Transduction", "Site", "Small Interfering RNA", "Societies", "System", "T-Lymphocyte", "Techniques", "Therapeutic Agents", "Translations", "Uridine", "Vaccines", "Viral Pathogenesis", "Viral Proteins", "Virus Diseases", "Virus Replication", "adaptive immune response", "adaptive immunity", "attenuation", "base", "bronchial epithelium", "genomic RNA", "immunogenic", "improved", "in vivo", "knock-down", "mRNA Translation", "novel", "novel therapeutics", "novel vaccines", "obligate intracellular parasite", "prevent", "public health relevance", "reverse genetics", "small hairpin RNA", "targeted treatment", "transmission process", "vaccine delivery", "variants of concern", "viral RNA" ], "approved": true } }, { "type": "Grant", "id": "15606", "attributes": { "award_id": "1R01HL171387-01A1", "title": "Analyzing effectiveness of ongoing natural experiments in telehealth", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 26847, "first_name": "ALISON GWENDOLYN MARY", "last_name": "Brown", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-03-01", "end_date": "2029-12-31", "award_amount": 705383, "principal_investigator": { "id": 32102, "first_name": "Mark J", "last_name": "Pletcher", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32103, "first_name": "Steven Michael", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Uncontrolled blood pressure (BP) is the most prevalent modifiable risk for cardiovascular disease (CVD) and disorders directly influencing CVD (e.g., diabetes, chronic kidney disease, etc.). Along with many other aspects of U.S. healthcare, management of uncontrolled BP was severely disrupted during the COVID- 19 pandemic. In response, many health systems rapidly accelerated implementation of new technologies, including telehealth visits for BP control and support for self-monitoring with home-based measurement of BP. Anecdotally, new BP control technologies and strategies have been implemented differentially with wide variation in timing and degree of utilization, but systematic analyses showing the extent and variability of implementation across sites are lacking. Meanwhile, substantial and variable backsliding in BP control rates across health systems was documented at the onset of the pandemic, and it is unclear how much of the variability is driven by differential implementation of new BP-related technologies and strategies. To learn from this unprecedented natural experiment and help guide the US healthcare enterprise towards more effective and equitable practices for management of BP control, we propose a mixed methods comparative effectiveness analysis. We will leverage our nationally scoped PCORnet Blood Pressure Control Laboratory (BPCL) – designed fundamentally for efficient surveillance of BP control and related process metrics using electronic health record (EHR) data – to develop and validate process metric queries that track implementation of new BP-related technologies & strategies, field these queries along with our previously developed metrics, extract trend results and individual patient-level data from participating sites, and conduct descriptive and causal inference analyses to decipher successful patterns of care for uncontrolled BP. And, we will conduct a positive deviance analysis with mixed methods approach to assess residual variability in BP control across clinics and learn from clinics with unexplained excellence. Our specific aims are to: 1) evaluate time trends and disparities in utilization of BP-related telehealth and home BP monitoring; 2) estimate causal effects of telehealth implementation on BP control and related metrics in hypertension management; and, 3) identify clinics with unexplained resilience in BP control and use mixed methods to analyze potential mechanisms and opportunities for dissemination of effective, scalable practices. As we have done in prior work, we will test for effect heterogeneity across important subgroups (sex, race, ethnicity) and place special emphasis on BP control in non-Hispanic Black patients, for whom disparities are historically largest. Findings from these aims will be discussed with stakeholders via webinar including a panel of frontline clinicians and leaders from positive deviant clinical sites, and disseminated via conference presentations and publications.", "keywords": [ "Abbreviations", "Acceleration", "Age", "American", "Blood Pressure", "COVID-19 pandemic", "Cardiovascular Diseases", "Caring", "Chronic Kidney Failure", "Clinic", "Code", "Consolidated Framework for Implementation Research", "Data", "Data Collection", "Diabetes Mellitus", "Disparity", "Effectiveness", "Electronic Health Record", "Equity", "Ethnic Origin", "Feedback", "Health Care", "Health system", "Heterogeneity", "Home", "Home Blood Pressure Monitoring", "Hypertension", "Interview", "Laboratories", "Learning", "Measurable", "Measurement", "Measures", "Methods", "Monitor", "Natural experiment", "Not Hispanic or Latino", "Outcome", "Outcomes Research", "Patient-Focused Outcomes", "Patients", "Patterns of Care", "Physiologic Monitoring", "Practice Management", "Process", "Publications", "Race", "Recommendation", "Residual state", "Rural", "Site", "Site Visit", "Standardization", "Subgroup", "Technology", "Testing", "Time trend", "Variant", "Visit", "Work", "black patient", "blood pressure control", "cardiovascular disorder risk", "clinical research site", "comparative effectiveness analysis", "data modeling", "design", "deviant", "health care settings", "hypertension control", "improved", "individual patient", "modifiable risk", "new technology", "novel strategies", "pandemic disease", "pandemic disruption", "partial recovery", "patient subsets", "pre-pandemic", "preventable death", "resilience", "response", "sex", "symposium", "technology validation", "telehealth", "trend", "webinar" ], "approved": true } } ], "meta": { "pagination": { "page": 2, "pages": 1392, "count": 13920 } } }