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Timbie JW, Kim AY, Baker L, Li R, W Concannon T. Lessons on the use of real-world data in medical device research: findings from the National Evaluation System for Health Technology Test-Cases. J Comp Eff Res 2024; 13:e240078. [PMID: 39150225 PMCID: PMC11367563 DOI: 10.57264/cer-2024-0078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Accepted: 07/23/2024] [Indexed: 08/17/2024] Open
Abstract
Aim: Although the US FDA encourages manufacturers of medical devices to submit real-world evidence (RWE) to support regulatory decisions, the ability of real-world data (RWD) to generate evidence suitable for decision making remains unclear. The 2017 Medical Device User Fee Amendments (MDUFA IV), authorized the National Evaluation System for health Technology Coordinating Center (NESTcc) to conduct pilot projects, or 'Test-Cases', to assess whether current RWD captures the information needed to answer research questions proposed by industry stakeholders. We synthesized key lessons about the challenges conducting research with RWD and the strategies used by research teams to enhance their ability to generate evidence from RWD based on 18 Test-Cases conducted between 2020 and 2022. Materials & methods: We reviewed study protocols and reports from each Test-Case team and conducted 49 semi-structured interviews with representatives of participating organizations. Interview transcripts were coded and thematically analyzed. Results: Challenges that stakeholders encountered in working with RWD included the lack of unique device identifiers, capturing key data elements and their appropriate meaning in structured data, limited reliability of diagnosis and procedure codes in structured data, extracting information from unstructured electronic health record (EHR) data, limited capture of long-term study end points, missing data and data sharing. Successful strategies included using manufacturer and supply chain data, leveraging clinical registries and registry reporting processes to collect and aggregate data, querying standardized EHR data, implementing natural language processing algorithms and using multidisciplinary research teams. Conclusion: The Test-Cases identified numerous challenges working with RWD but also opportunities to address these challenges and improve researchers' ability to use RWD to generate evidence on medical devices.
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Zang C, Hou Y, Schenck EJ, Xu Z, Zhang Y, Xu J, Bian J, Morozyuk D, Khullar D, Nordvig AS, Shenkman EA, Rothman RL, Block JP, Lyman K, Zhang Y, Varma J, Weiner MG, Carton TW, Wang F, Kaushal R. Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts. COMMUNICATIONS MEDICINE 2024; 4:130. [PMID: 38992068 PMCID: PMC11239808 DOI: 10.1038/s43856-024-00549-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/10/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors. METHODS In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests. RESULTS We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7-0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). CONCLUSIONS This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yu Hou
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, New York, NY, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Dmitry Morozyuk
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Dhruv Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Anna S Nordvig
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Russell L Rothman
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason P Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Kristin Lyman
- Louisiana Public Health Institute, New Orleans, LA, USA
| | - Yiye Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jay Varma
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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3
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Meng W, Xu J, Huang Y, Wang C, Song Q, Ma A, Song L, Bian J, Ma Q, Yin R. Autoencoder to Identify Sex-Specific Sub-phenotypes in Alzheimer's Disease Progression Using Longitudinal Electronic Health Records. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.07.24310055. [PMID: 39040206 PMCID: PMC11261930 DOI: 10.1101/2024.07.07.24310055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Alzheimer's Disease (AD) is a complex neurodegenerative disorder significantly influenced by sex differences, with approximately two-thirds of AD patients being women. Characterizing the sex-specific AD progression and identifying its progression trajectory is a crucial step to developing effective risk stratification and prevention strategies. In this study, we developed an autoencoder to uncover sex-specific sub-phenotypes in AD progression leveraging longitudinal electronic health record (EHR) data from OneFlorida+ Clinical Research Consortium. Specifically, we first constructed temporal patient representation using longitudinal EHRs from a sex-stratified AD cohort. We used a long short-term memory (LSTM)-based autoencoder to extract and generate latent representation embeddings from sequential clinical records of patients. We then applied hierarchical agglomerative clustering to the learned representations, grouping patients based on their progression sub-phenotypes. The experimental results show we successfully identified five primary sex-based AD sub-phenotypes with corresponding progression pathways with high confidence. These sex-specific sub-phenotypes not only illustrated distinct AD progression patterns but also revealed differences in clinical characteristics and comorbidities between females and males in AD development. These findings could provide valuable insights for advancing personalized AD intervention and treatment strategies.
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Affiliation(s)
- Weimin Meng
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Jie Xu
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Yu Huang
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Cankun Wang
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Qianqian Song
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Anjun Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Lixin Song
- School of Nursing, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
| | - Jiang Bian
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Qin Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Rui Yin
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
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4
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Su C, Hou Y, Xu J, Xu Z, Zhou M, Ke A, Li H, Xu J, Brendel M, Maasch JRMA, Bai Z, Zhang H, Zhu Y, Cincotta MC, Shi X, Henchcliffe C, Leverenz JB, Cummings J, Okun MS, Bian J, Cheng F, Wang F. Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit Med 2024; 7:184. [PMID: 38982243 PMCID: PMC11233682 DOI: 10.1038/s41746-024-01175-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
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Grants
- R21 AG083003 NIA NIH HHS
- R01 AG082118 NIA NIH HHS
- R56 AG074001 NIA NIH HHS
- R01AG076448 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1AG072449 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- MJFF-023081 Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)
- R01AG080991 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P30 AG072959 NIA NIH HHS
- 3R01AG066707-01S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R21AG083003 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG066707 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R35 AG071476 NIA NIH HHS
- RF1 AG082211 NIA NIH HHS
- R56AG074001 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG082118 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R25 AG083721 NIA NIH HHS
- RF1AG082211 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 NS093334 NINDS NIH HHS
- AG083721-01 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1NS133812 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20GM109025 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 NS133812 NINDS NIH HHS
- R35AG71476 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 AG073323 NIA NIH HHS
- R01 AG066707 NIA NIH HHS
- R01AG053798 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG076234 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01 AG076448 NIA NIH HHS
- R01 AG080991 NIA NIH HHS
- R01 AG076234 NIA NIH HHS
- U01NS093334 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20 GM109025 NIGMS NIH HHS
- P30AG072959 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 AG072449 NIA NIH HHS
- R01 AG053798 NIA NIH HHS
- 3R01AG066707-02S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01AG073323 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- ALZDISCOVERY-1051936 Alzheimer's Association
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Manqi Zhou
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Alison Ke
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Haoyang Li
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Matthew Brendel
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jacqueline R M A Maasch
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computer Science, Cornell Tech, Cornell University, New York, NY, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, Arlington, TX, USA
| | - Molly C Cincotta
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Claire Henchcliffe
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Michael S Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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5
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Varma JK, Zang C, Carton TW, Block JP, Khullar DJ, Zhang Y, Weiner MG, Rothman RL, Schenck EJ, Xu Z, Lyman K, Bian J, Xu J, Shenkman EA, Maughan C, Castro-Baucom L, O’Brien L, Wang F, Kaushal R. Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER program. PLoS One 2024; 19:e0282451. [PMID: 38843159 PMCID: PMC11156291 DOI: 10.1371/journal.pone.0282451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 01/16/2024] [Indexed: 06/09/2024] Open
Abstract
IMPORTANCE The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. OBJECTIVE To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. DESIGN Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. SETTING Healthcare facilities in New York and Florida. PARTICIPANTS Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. EXPOSURE Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. MAIN OUTCOME(S) AND MEASURE(S) Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons without a COVID-19 test or diagnosis during the 31-180 days after the last negative test. RESULTS We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those without a COVID-19 test or diagnosis (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). CONCLUSIONS AND RELEVANCE We documented a substantial relative risk of pulmonary embolism and a large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.
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Affiliation(s)
- Jay K. Varma
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - Thomas W. Carton
- Louisiana Public Health Institute, New Orleans, Louisiana, United States of America
| | - Jason P. Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dhruv J. Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
- Department of Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - Mark G. Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - Russell L. Rothman
- Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Edward J. Schenck
- Department of Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - Kristin Lyman
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - Jiang Bian
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, Florida, United States of America
| | - Jie Xu
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, Florida, United States of America
| | - Elizabeth A. Shenkman
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, Florida, United States of America
| | - Christine Maughan
- Utah COVID-19 Long Haulers, Salt Lake City, Utah, United States of America
| | | | - Lisa O’Brien
- Utah COVID-19 Long Haulers, Salt Lake City, Utah, United States of America
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
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6
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Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, Haines JL, Leverenz JB, Gan L, Hu J, Rosen-Zvi M, Pieper AA, Cummings J. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell Rep Med 2024; 5:101379. [PMID: 38382465 PMCID: PMC10897520 DOI: 10.1016/j.xcrm.2023.101379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 08/15/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
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Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec Institute for Learning Algorithms and CIFAR AI Research Chair, HEC Montreal, Montréal, QC H3T 2A7, Canada
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zhimin Fu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; College of Pharmacy, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46037, USA
| | - Jonathan L Haines
- Cleveland Institute for Computational Biology, and Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Jianying Hu
- IBM Research, Yorktown Heights, New York, NY 10598, USA
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare and Life Sciences Discovery, IBM Research Labs, Haifa 3498825, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190500, Israel
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
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7
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Liu Y, Siddiqi KA, Cho H, Park H, Prosperi M, Cook RL. Demographics, Trends, and Clinical Characteristics of HIV Pre-Exposure Prophylaxis Recipients and People Newly Diagnosed with HIV from Large Electronic Health Records in Florida. AIDS Patient Care STDS 2024; 38:14-22. [PMID: 38227279 PMCID: PMC10794838 DOI: 10.1089/apc.2023.0220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024] Open
Abstract
Florida is one of the HIV epicenters with high incidence and marked sociodemographic disparities. We analyzed a decade of statewide electronic health record/claims data-OneFlorida+-to identify and characterize pre-exposure prophylaxis (PrEP) recipients and newly diagnosed HIV cases in Florida. Refined computable phenotype algorithms were applied and a total of 2186 PrEP recipients and 7305 new HIV diagnoses were identified between January 2013 and April 2021. We examined patients' sociodemographic characteristics, stratified by self-reported sex, along with both frequency-driven and expert-selected descriptions of clinical conditions documented within 12 months before the first PrEP use or HIV diagnosis. PrEP utilization rate increased in both sexes; higher rates were observed among males with sex differences widening in recent years. HIV incidence peaked in 2016 and then decreased with minimal sex differences observed. Clinical characteristics were similar between the PrEP and new HIV diagnosis cohorts, characterized by a low prevalence of sexually transmitted infections (STIs) and a high prevalence of mental health and substance use conditions. Study limitations include the overrepresentation of Medicaid recipients, with over 96% of female PrEP users on Medicaid, and the inclusion of those engaged in regular health care. Although PrEP uptake increased in Florida, and HIV incidence decreased, sex disparity among PrEP recipients remained. Screening efforts beyond individuals with documented prior STI and high-risk behavior, especially for females, including integration of mental health care with HIV counseling and testing, are crucial to further equalize PrEP access and improve HIV prevention programs.
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Affiliation(s)
- Yiyang Liu
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Khairul A. Siddiqi
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Hwayoung Cho
- Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, Florida, USA
| | - Haesuk Park
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Robert L. Cook
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
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8
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Li P, Lyu T, Alkhuzam K, Spector E, Donahoo WT, Bost S, Wu Y, Hogan WR, Prosperi M, Schatz DA, Atkinson MA, Haller MJ, Shenkman EA, Guo Y, Bian J, Shao H. The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records. J Am Med Inform Assoc 2023; 31:165-173. [PMID: 37812771 PMCID: PMC10746308 DOI: 10.1093/jamia/ocad194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/31/2023] [Accepted: 09/21/2023] [Indexed: 10/11/2023] Open
Abstract
OBJECTIVE Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence. MATERIALS AND METHODS We conducted a retrospective, cross-sectional analysis involving children and adolescents <20 years old identified from the OneFlorida+ Clinical Research Network (2018-2020). T1D cases were identified using a previously validated computable phenotyping algorithm. The T1D prevalence for each ZIP Code Tabulation Area (ZCTA, 5 digits), defined as the number of T1D cases divided by the total number of residents in the corresponding ZCTA, was calculated. Population coverage for each ZCTA was measured using observed health system penetration rates (HSPR), which was calculated as the ratio of residents in the corresponding ZTCA and captured by OneFlorida+ to the overall population in the same ZCTA reported by the Census. We used a recursive partitioning algorithm to identify the minimum required observed HSPR to estimate T1D prevalence and compare our estimate with the reported T1D prevalence from the SEARCH study. RESULTS Observed HSPRs of 55%, 55%, and 60% were identified as the minimum thresholds for the non-Hispanic White, non-Hispanic Black, and Hispanic populations. The estimated T1D prevalence for non-Hispanic White and non-Hispanic Black were 2.87 and 2.29 per 1000 youth, which are comparable to the reference study's estimation. The estimated prevalence of T1D for Hispanics (2.76 per 1000 youth) was higher than the reference study's estimation (1.48-1.64 per 1000 youth). The standardized T1D prevalence in the overall Florida population was 2.81 per 1000 youth in 2019. CONCLUSION Our study provides a method to estimate T1D prevalence in children and adolescents using EHRs and reports the estimated HSPRs and prevalence of T1D for different race and ethnicity groups to facilitate EHR-based diabetes surveillance.
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Affiliation(s)
- Piaopiao Li
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Khalid Alkhuzam
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Eliot Spector
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - William T Donahoo
- Division of Endocrinology, Diabetes & Metabolism, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Sarah Bost
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - William R Hogan
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Desmond A Schatz
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Mark A Atkinson
- Diabetes Institute, University of Florida, Gainesville, FL, United States
| | - Michael J Haller
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Elizabeth A Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Hui Shao
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
- Center for Drug Evaluation and Safety, University of Florida, Gainesville, FL, United States
- Department of Family and Preventive Medicine, School of Medicine, Emory University, Atlanta, GA, United States
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9
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Zang C, Zhang H, Xu J, Zhang H, Fouladvand S, Havaldar S, Cheng F, Chen K, Chen Y, Glicksberg BS, Chen J, Bian J, Wang F. High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data. Nat Commun 2023; 14:8180. [PMID: 38081829 PMCID: PMC10713627 DOI: 10.1038/s41467-023-43929-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hansi Zhang
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Sajjad Fouladvand
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kun Chen
- Department of Statistics, University of Connecticut, Storrs, CT, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics (DBEI), the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Chen
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA.
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10
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Sandifer PA. Linking coastal environmental and health observations for human wellbeing. Front Public Health 2023; 11:1202118. [PMID: 37780424 PMCID: PMC10540068 DOI: 10.3389/fpubh.2023.1202118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/24/2023] [Indexed: 10/03/2023] Open
Abstract
Coastal areas have long been attractive places to live, work, and recreate and remain so even in the face of growing threats from global environmental change. At any moment, a significant portion of the human population is exposed to both positive and negative health effects associated with coastal locations. Some locations may be "hotspots" of concern for human health due to ongoing climatic and other changes, accentuating the need for better understanding of coastal environment-human health linkages. This paper describes how environmental and health data could be combined to create a coastal environmental and human health observing system. While largely based on information from the US and Europe, the concept should be relevant to almost any coastal area. If implemented, a coastal health observing system would connect a variety of human health data and environmental observations for individuals and communities, and where possible cohorts. Health data would be derived from questionnaires and other personal sources, clinical examinations, electronic health records, wearable devices, and syndromic surveillance, plus information on vulnerability and health-relevant community characteristics, and social media observations. Environmental data sources would include weather and climate, beach and coastal conditions, sentinel species, occurrences of harmful organisms and substances, seafood safety advisories, and distribution, proximity, and characteristics of health-promoting green and blue spaces. Where available, information on supporting resources could be added. Establishment of a linked network of coastal health observatories could provide powerful tools for understanding the positive and negative health effects of coastal living, lead to better health protections and enhanced wellbeing, and provide significant benefits to coastal residents, including the historically disadvantaged, as well as the military, hospitals and emergency departments, academic medical, public health, and environmental health programs, and others. Early networks could provide best practices and lessons learned to assist later entries.
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Affiliation(s)
- Paul A. Sandifer
- Center for Coastal Environmental and Human Health, College of Charleston, Charleston, SC, United States
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11
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Xu J, Yin R, Huang Y, Gao H, Wu Y, Guo J, Smith GE, DeKosky ST, Wang F, Guo Y, Bian J. Identification of Outcome-Oriented Progression Subtypes from Mild Cognitive Impairment to Alzheimer's Disease Using Electronic Health Records. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.27.23293270. [PMID: 37577594 PMCID: PMC10418300 DOI: 10.1101/2023.07.27.23293270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Alzheimer's disease (AD) is a complex heterogeneous neurodegenerative disease that requires an in-depth understanding of its progression pathways and contributing factors to develop effective risk stratification and prevention strategies. In this study, we proposed an outcome-oriented model to identify progression pathways from mild cognitive impairment (MCI) to AD using electronic health records (EHRs) from the OneFlorida+ Clinical Research Consortium. To achieve this, we employed the long short-term memory (LSTM) network to extract relevant information from the sequential records of each patient. The hierarchical agglomerative clustering was then applied to the learned representation to group patients based on their progression subtypes. Our approach identified multiple progression pathways, each of which represented distinct patterns of disease progression from MCI to AD. These pathways can serve as a valuable resource for researchers to understand the factors influencing AD progression and to develop personalized interventions to delay or prevent the onset of the disease.
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12
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Smith G, Miller A, Marra DE, Wu Y, Bian J, Maraganore DM, Anton S. Evaluation of a Computable Phenotype for Successful Cognitive Aging. Mayo Clin Proc Innov Qual Outcomes 2023; 7:212-221. [PMID: 37304063 PMCID: PMC10250575 DOI: 10.1016/j.mayocpiqo.2023.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023] Open
Abstract
Objective To establish, apply, and evaluate a computable phenotype for the recruitment of individuals with successful cognitive aging. Participants and Methods Interviews with 10 aging experts identified electronic health record (EHR)-available variables representing successful aging among individuals aged 85 years and older. On the basis of the identified variables, we developed a rule-based computable phenotype algorithm composed of 17 eligibility criteria. Starting September 1, 2019, we applied the computable phenotype algorithm to all living persons aged 85 years and older at the University of Florida Health, which identified 24,024 individuals. This sample was comprised of 13,841 (58%) women, 13,906 (58%) Whites, and 16,557 (69%) non-Hispanics. A priori permission to be contacted for research had been obtained for 11,898 individuals, of whom 470 responded to study announcements and 333 consented to evaluation. Then, we contacted those who consented to evaluate whether their cognitive and functional status clinically met out successful cognitive aging criteria of a modified Telephone Interview for Cognitive Status score of more than 27 and Geriatric Depression Scale of less than 6. The study was completed on December 31, 2022. Results Of the 45% of living persons aged 85 years and older included in the University of Florida Health EHR database identified by the computable phenotype as successfully aged, approximately 4% of these responded to study announcements and 333 consented, of which 218 (65%) met successful cognitive aging criteria through direct evaluation. Conclusion The study evaluated a computable phenotype algorithm for the recruitment of individuals for a successful aging study using large-scale EHRs. Our study provides proof of concept of using big data and informatics as aids for the recruitment of individuals for prospective cohort studies.
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Affiliation(s)
- Glenn Smith
- Department of Clinical and Health Psychology, University of Florida, Gainesville
| | - Amber Miller
- Department of Neurology, College of Medicine, University of Florida, Gainesville
| | - David E. Marra
- Department of Psychology, VA Boston Healthcare System, Boston, MA
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville
| | | | - Stephen Anton
- Department of Clinical and Health Psychology, University of Florida, Gainesville
- Department of Physiology and Aging, University of Florida, Gainesville
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13
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Fradley MG, Nguyen NHK, Madnick D, Chen Y, DeMichele A, Makhlin I, Dent S, Lefebvre B, Carver J, Upshaw JN, DeRemer D, Ky B, Guha A, Gong Y. Adverse Cardiovascular Events Associated With Cyclin-Dependent Kinase 4/6 Inhibitors in Patients With Metastatic Breast Cancer. J Am Heart Assoc 2023; 12:e029361. [PMID: 37301767 PMCID: PMC10356048 DOI: 10.1161/jaha.123.029361] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/10/2023] [Indexed: 06/12/2023]
Abstract
Background Cyclin-dependent kinase (CDK) 4 and 6 inhibitors have significantly improved survival in patients with hormone receptor-positive metastatic breast cancer. There are few data regarding the epidemiology of cardiovascular adverse events (CVAEs) with these therapies. Methods and Results Using the OneFlorida Data Trust, adult patients without prior cardiovascular disease who received at least 1 CDK4/6 inhibitor were included in the analysis. CVAEs identified from International Classification of Diseases, Ninth and Tenth Revisions (ICD-9/10) codes included hypertension, atrial fibrillation(AF)/atrial flutter (AFL), heart failure/cardiomyopathy, ischemic heart disease, and pericardial disease. Competing risk analysis (Fine-Gray model) was used to determine the association between CDK4/6 inhibitor therapy and incident CVAEs. The effect of CVAEs on all-cause death was studied using Cox proportional hazard models. Propensity-weight analyses were performed to compare these patients to a cohort of patients treated with anthracyclines. A total of 1376 patients treated with CDK4/6 inhibitors were included in the analysis. CVAEs occurred in 24% (35.9 per 100 person-years). CVAEs were slightly higher in patients who received CKD4/6 inhibitors compared with anthracyclines (P=0.063), with higher death rate associated with the development of AF/AFL or cardiomyopathy/heart failure in the CDK4/6 group. The development of cardiomyopathy/heart failure and AF/AFL was associated with increased all-cause death (adjusted hazard ratio [HR], 4.89 [95% CI, 2.98-8.05]; and 5.88 [95% CI, 3.56-9.73], respectively). Conclusions CVAEs may be more common with CDK4/6 inhibitors than previously recognized, with increased death rates in these patients who develop AF/AFL or heart failure. Further research is needed to definitively determine cardiovascular risk associated with these novel anticancer treatments.
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Affiliation(s)
- Michael G. Fradley
- Cardio‐Oncology Center of Excellence, Division of Cardiology, Department of MedicinePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPA
| | - Nam H. K. Nguyen
- Department of Pharmacotherapy and Translational ResearchCollege of Pharmacy, University of FloridaGainesvilleFL
| | - David Madnick
- Department of MedicineHospital of the University of PennsylvaniaPhiladelphiaPA
| | - Yiqing Chen
- Department of Biostatistics and Data ScienceUniversity of Texas Health Science Center at HoustonHoustonTX
| | - Angela DeMichele
- Department of Medicine, Division of Hematology & OncologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPA
| | - Igor Makhlin
- Department of Medicine, Division of Hematology & OncologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPA
| | - Susan Dent
- Duke Cancer InstituteDuke UniversityDurhamNC
| | - Benedicte Lefebvre
- Cardio‐Oncology Center of Excellence, Division of Cardiology, Department of MedicinePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPA
| | - Joseph Carver
- Cardio‐Oncology Center of Excellence, Division of Cardiology, Department of MedicinePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPA
| | - Jenica N. Upshaw
- Cardio‐Oncology Program, Division of CardiologyTufts Medical CenterBostonMA
| | - David DeRemer
- Department of Pharmacotherapy and Translational ResearchCollege of Pharmacy, University of FloridaGainesvilleFL
- Center for Pharmacogenomics and Precision Medicine, College of PharmacyUniversity of FloridaGainesvilleFL
- UF Health Cancer CenterGainesvilleFL
| | - Bonnie Ky
- Cardio‐Oncology Center of Excellence, Division of Cardiology, Department of MedicinePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPA
| | - Avirup Guha
- Cardio‐Oncology Program, Division of Cardiology, Department of Internal MedicineMedical College of Georgia at Augusta UniversityAugustaGA
- Cardio‐Oncology Program, Division of CardiologyThe Ohio State University Medical CenterColumbusOH
| | - Yan Gong
- Department of Pharmacotherapy and Translational ResearchCollege of Pharmacy, University of FloridaGainesvilleFL
- Center for Pharmacogenomics and Precision Medicine, College of PharmacyUniversity of FloridaGainesvilleFL
- UF Health Cancer CenterGainesvilleFL
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Huo T, Glueck DH, Shenkman EA, Muller KE. Stratified split sampling of electronic health records. BMC Med Res Methodol 2023; 23:128. [PMID: 37231360 DOI: 10.1186/s12874-023-01938-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/04/2023] [Indexed: 05/27/2023] Open
Abstract
Although superficially similar to data from clinical research, data extracted from electronic health records may require fundamentally different approaches for model building and analysis. Because electronic health record data is designed for clinical, rather than scientific use, researchers must first provide clear definitions of outcome and predictor variables. Yet an iterative process of defining outcomes and predictors, assessing association, and then repeating the process may increase Type I error rates, and thus decrease the chance of replicability, defined by the National Academy of Sciences as the chance of "obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data."[1] In addition, failure to account for subgroups may mask heterogeneous associations between predictor and outcome by subgroups, and decrease the generalizability of the findings. To increase chances of replicability and generalizability, we recommend using a stratified split sample approach for studies using electronic health records. A split sample approach divides the data randomly into an exploratory set for iterative variable definition, iterative analyses of association, and consideration of subgroups. The confirmatory set is used only to replicate results found in the first set. The addition of the word 'stratified' indicates that rare subgroups are oversampled randomly by including them in the exploratory sample at higher rates than appear in the population. The stratified sampling provides a sufficient sample size for assessing heterogeneity of association by testing for effect modification by group membership. An electronic health record study of the associations between socio-demographic factors and uptake of hepatic cancer screening, and potential heterogeneity of association in subgroups defined by gender, self-identified race and ethnicity, census-tract level poverty and insurance type illustrates the recommended approach.
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Affiliation(s)
- Tianyao Huo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 2004 Mowry Road; Room 2236-5, PO Box 100177, Gainesville, FL, 32608, USA
| | - Deborah H Glueck
- Department of Pediatrics, School of Medicine, University of Colorado, 12474 E. 19th Avenue, Building 402, Room 219 Main Stop F426, Aurora, CO, 80045, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 2004 Mowry Road; Room 2245, PO Box 100177, Gainesville, FL, 32608, USA
| | - Keith E Muller
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 2004 Mowry Road; Room 2244, PO Box 100177, Gainesville, FL, 32608, USA.
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15
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DeRemer DL, Nguyen NK, Guha A, Ahmad FS, Cooper-DeHoff RM, Pepine CJ, Fradley MG, Gong Y. Racial and Ethnic Differences in Cardiac Surveillance Evaluation of Patients Treated With Anthracycline-Based Chemotherapy. J Am Heart Assoc 2023; 12:e027981. [PMID: 37158063 DOI: 10.1161/jaha.122.027981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Background Anthracyclines remain a key treatment for many malignancies but can increase the risk of heart failure or cardiomyopathy. Specific guidelines recommend echocardiography and serum cardiac biomarkers such as BNP (B-type natriuretic peptide) or NT-proBNP (N-terminal proBNP) evaluation before and 6 to 12 months after treatment. Our objective was to evaluate associations between racial and ethnic groups in cardiac surveillance of survivors of cancer after exposure to anthracyclines. Methods and Results Adult patients in the OneFlorida Consortium without prior cardiovascular disease who received at least 2 cycles of anthracyclines were included in the analysis. Multivariable logistic regression was performed to estimate the odds ratios (ORs) and 95% CIs for receiving cardiac surveillance at baseline before anthracycline therapy, 6 months after, and 12 months after anthracycline exposure among different racial and ethnic groups. Among the entire cohort of 5430 patients, 63.4% had a baseline echocardiogram, with 22.3% receiving an echocardiogram at 6 months and 25% at 12 months. Non-Hispanic Black (NHB) patients had a lower likelihood of receiving a baseline echocardiogram than Non-Hispanic White (NHW) patients (OR, 0.75 [95% CI, 0.63-0.88]; P=0.0006) or any baseline cardiac surveillance (OR, 0.76 [95% CI, 0.64-0.89]; P=0.001). Compared with NHW patients, Hispanic patients received significantly less cardiac surveillance at the 6-month (OR, 0.84 [95% CI, 0.72-0.98]; P=0.03) and 12-month (OR, 0.85 [95% CI, 0.74-0.98]; P=0.03) time points, respectively. Conclusions There were significant racial and ethnic differences in cardiac surveillance among survivors of cancer at baseline and following anthracycline-based treatment in NHB and Hispanic cohorts. Health care providers need to be cognizant of these social inequities and initiate efforts to ensure recommended cardiac surveillance occurs following anthracyclines.
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Affiliation(s)
- David L DeRemer
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy University of Florida Gainesville FL USA
- University of Florida Health Cancer Center Gainesville FL USA
| | - Nam K Nguyen
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy University of Florida Gainesville FL USA
| | - Avirup Guha
- Cardio-Oncology Program, Georgia Cancer Center Medical College of Georgia at Augusta, University Augusta GA USA
| | - Faraz S Ahmad
- Department of Medicine, Division of Cardiology Northwestern Memorial Hospital Chicago IL USA
| | - Rhonda M Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy University of Florida Gainesville FL USA
- Division of Cardiovascular Medicine, Department of Medicine University of Florida Gainesville FL USA
| | - Carl J Pepine
- Division of Cardiovascular Medicine, Department of Medicine University of Florida Gainesville FL USA
| | - Michael G Fradley
- Cardio-Oncology Center of Excellence, Perelman School of Medicine University of Pennsylvania Philadelphia PA USA
| | - Yan Gong
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy University of Florida Gainesville FL USA
- University of Florida Health Cancer Center Gainesville FL USA
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Hu H, Laden F, Hart J, James P, Fishe J, Hogan W, Shenkman E, Bian J. A spatial and contextual exposome-wide association study and polyexposomic score of COVID-19 hospitalization. EXPOSOME 2023; 3:osad005. [PMID: 37089437 PMCID: PMC10118922 DOI: 10.1093/exposome/osad005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/22/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023]
Abstract
Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals' spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18-24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.
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Affiliation(s)
- Hui Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Francine Laden
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jaime Hart
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Peter James
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Medicine, Harvard Pilgrim Healthcare, Boston, MA, USA
| | - Jennifer Fishe
- Department of Emergency Medicine, University of Florida College of Medicine—Jacksonville, Jacksonville, FL, USA
| | - William Hogan
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Elizabeth Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
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Xu J, Bian J, Fishe JN. Pediatric and adult asthma clinical phenotypes: a real world, big data study based on acute exacerbations. J Asthma 2023; 60:1000-1008. [PMID: 36039465 PMCID: PMC10011007 DOI: 10.1080/02770903.2022.2119865] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/22/2022] [Accepted: 08/29/2022] [Indexed: 10/14/2022]
Abstract
INTRODUCTION Asthma is a heterogeneous disease with a range of observable phenotypes. To date, the characterization of asthma phenotypes is mostly limited to allergic versus non-allergic disease. Therefore, the aim of this big data study was to computationally derive asthma subtypes from the OneFlorida Clinical Research Consortium. METHODS We obtained data from 2012-2020 from the OneFlorida Clinical Research Consortium. Longitudinal data for patients greater than two years of age who met inclusion criteria for an asthma exacerbation based on International Classification of Diseases codes. We used matrix factorization to extract information and K-means clustering to derive subtypes. The distributions of demographics, comorbidities, and medications were compared using Chi-square statistics. RESULTS A total of 39,807 pediatric patients and 23,883 adult patients met inclusion criteria. We identified five distinct pediatric subtypes and four distinct adult subtypes. Pediatric subtype P1 had the highest proportion of black patients, but the lowest use of inhaled corticosteroids and allergy medications. Subtype P2 had a predominance of patients with gastroesophageal reflux disease, whereas P3 had a predominance of patients with allergic disorders. Adult subtype A2 was the most severe and all patients were on biologic agents. Most of subtype A3 patients were not taking controller medications, whereas most patients (>90%) in subtypes A2 and A4 were taking corticosteroids and allergy medications. CONCLUSION We found five distinct pediatric asthma subtypes and four distinct adult asthma subtypes. Future work should externally validate these subtypes and characterize response to treatment by subtype to better guide clinical treatment of asthma.
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Affiliation(s)
- Jie Xu
- Department of Health Outcomes and Bioinformatics, University of Florida, Gainesville, Florida, USA
| | - Jiang Bian
- Department of Health Outcomes and Bioinformatics, University of Florida, Gainesville, Florida, USA
| | - Jennifer N Fishe
- Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, Florida, USA
- Department of Emergency Medicine, University of Florida College of Medicine - Jacksonville, Jacksonville, Florida, USA
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18
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Guo Y, Bian J, Chen Z, Fishe JN, Zhang D, Braithwaite D, George TJ, Shenkman EA, Licht JD. Cancer incidence after asthma diagnosis: Evidence from a large clinical research network in the United States. Cancer Med 2023; 12:11871-11877. [PMID: 36999938 PMCID: PMC10242315 DOI: 10.1002/cam4.5875] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 03/06/2023] [Accepted: 03/16/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Prior studies on the association between asthma and cancer show inconsistent results. This study aimed to generate additional evidence on the association between asthma and cancer, both overall, and by cancer type, in the United States. METHOD We conducted a retrospective cohort study using 2012-2020 electronic health records and claims data in the OneFlorida+ clinical research network. Our study population included a cohort of adult patients with asthma (n = 90,021) and a matching cohort of adult patients without asthma (n = 270,063). We built Cox proportional hazards models to examine the association between asthma diagnosis and subsequent cancer risk. RESULTS Our results showed that asthma patients were more likely to develop cancer compared to patients without asthma in multivariable analysis (hazard ratio [HR] = 1.36, 99% confidence interval [CI] = 1.29-1.44). Elevated cancer risk was observed in asthma patients without (HR = 1.60; 99% CI: 1.50-1.71) or with (HR = 1.11; 99% CI: 1.03-1.21) inhaled steroid use. However, in analyses of specific cancer types, cancer risk was elevated for nine of 13 cancers in asthma patients without inhaled steroid use but only for two of 13 cancers in asthma patients with inhaled steroid use, suggesting a protective effect of inhaled steroid use on cancer. CONCLUSION This is the first study to report a positive association between asthma and overall cancer risk in the US population. More in-depth studies using real-word data are needed to further explore the causal mechanisms of asthma on cancer risk.
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Affiliation(s)
- Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of MedicineUniversity of FloridaGainesvilleFloridaUSA
- University of Florida Health Cancer CenterGainesvilleFloridaUSA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of MedicineUniversity of FloridaGainesvilleFloridaUSA
- University of Florida Health Cancer CenterGainesvilleFloridaUSA
| | - Zhaoyi Chen
- Department of Health Outcomes and Biomedical Informatics, College of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Jennifer N. Fishe
- Department of Emergency Medicine, College of Medicine – JacksonvilleUniversity of FloridaJacksonvilleFloridaUSA
| | - Dongyu Zhang
- University of Florida Health Cancer CenterGainesvilleFloridaUSA
- Department of EpidemiologyUniversity of FloridaGainesvilleFloridaUSA
| | - Dejana Braithwaite
- University of Florida Health Cancer CenterGainesvilleFloridaUSA
- Department of EpidemiologyUniversity of FloridaGainesvilleFloridaUSA
| | - Thomas J. George
- Department of Health Outcomes and Biomedical Informatics, College of MedicineUniversity of FloridaGainesvilleFloridaUSA
- University of Florida Health Cancer CenterGainesvilleFloridaUSA
- Division of Hematology and Oncology, Department of Medicine, College of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Elizabeth A. Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of MedicineUniversity of FloridaGainesvilleFloridaUSA
- University of Florida Health Cancer CenterGainesvilleFloridaUSA
| | - Jonathan D. Licht
- University of Florida Health Cancer CenterGainesvilleFloridaUSA
- Division of Hematology and Oncology, Department of Medicine, College of MedicineUniversity of FloridaGainesvilleFloridaUSA
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19
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Chen A, Li Q, He X, Jaffee MS, Hogan WR, Wang F, Guo Y, Bian J. Impacts of Eligibility Criteria on Trial Participants' Age in Alzheimer's Disease Clinical Trials. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2023; 2022:368-376. [PMID: 37128470 PMCID: PMC10148327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Overly restricted and poorly designed eligibility criteria reduce the generalizability of the results from clinical trials. We conducted a study to identify and quantify the impacts of study traits extracted from eligibility criteria on the age of study populations in Alzheimer's Disease (AD) clinical trials. Using machine learning methods and SHapley Additive exPlanation (SHAP) values, we identified 30 and 34 study traits that excluded older patients from AD trials in our 2 generated target populations respectively. We also found that study traits had different magnitudes of impacts on the age distributions of the generated study populations across racial-ethnic groups. To our best knowledge, this was the first study that quantified the impact of eligibility criteria on the age of AD trial participants. Our research is a first step in addressing the overly restrictive eligibility criteria in AD clinical trials.
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Affiliation(s)
- Aokun Chen
- University of Florida, Gainesville, Florida, USA
| | - Qian Li
- University of Florida, Gainesville, Florida, USA
| | - Xing He
- University of Florida, Gainesville, Florida, USA
| | | | | | - Fei Wang
- Weill Cornell Medicine, New York City, New York, USA
| | - Yi Guo
- University of Florida, Gainesville, Florida, USA
| | - Jiang Bian
- University of Florida, Gainesville, Florida, USA
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20
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Zang C, Zhang Y, Xu J, Bian J, Morozyuk D, Schenck EJ, Khullar D, Nordvig AS, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner MG, Carton TW, Wang F, Kaushal R. Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative. Nat Commun 2023; 14:1948. [PMID: 37029117 PMCID: PMC10080528 DOI: 10.1038/s41467-023-37653-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 03/24/2023] [Indexed: 04/09/2023] Open
Abstract
Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Dmitry Morozyuk
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Dhruv Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Anna S Nordvig
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Russell L Rothman
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason P Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Kristin Lyman
- Louisiana Public Health Institute, New Orleans, LA, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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21
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Zhang Y, Hu H, Fokaidis V, V CL, Xu J, Zang C, Xu Z, Wang F, Koropsak M, Bian J, Hall J, Rothman RL, Shenkman EA, Wei WQ, Weiner MG, Carton TW, Kaushal R. Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program. ENVIRONMENTAL ADVANCES 2023; 11:100352. [PMID: 36785842 PMCID: PMC9907788 DOI: 10.1016/j.envadv.2023.100352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 01/27/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.
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Affiliation(s)
- Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Hui Hu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Vasilios Fokaidis
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Colby Lewis V
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Jie Xu
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Michael Koropsak
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Jiang Bian
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Jaclyn Hall
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Russell L Rothman
- Institute of Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Elizabeth A Shenkman
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Thomas W Carton
- Louisiana Public Health Institute, New Orleans, LA, United States
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
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22
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Zang C, Hou Y, Schenck E, Xu Z, Zhang Y, Xu J, Bian J, Morozyuk D, Khullar D, Nordvig A, Shenkman E, Rothman R, Block J, Lyman K, Zhang Y, Varma J, Weiner M, Carton T, Wang F, Kaushal R. Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative. RESEARCH SQUARE 2023:rs.3.rs-2592194. [PMID: 36945608 PMCID: PMC10029117 DOI: 10.21203/rs.3.rs-2592194/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Background Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1st, 2020, and November 30th, 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.
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23
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Varma JK, Zang C, Carton TW, Block JP, Khullar DJ, Zhang Y, Weiner MG, Rothman RL, Schenck EJ, Xu Z, Lyman K, Bian J, Xu J, Shenkman EA, Maughan C, Castro-Baucom L, O’Brien L, Wang F, Kaushal R. Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER Program. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.15.23286012. [PMID: 36865304 PMCID: PMC9980238 DOI: 10.1101/2023.02.15.23286012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Importance The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. Objective To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. Design Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. Setting Healthcare facilities in New York and Florida. Participants Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. Exposure Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. Main Outcomes and Measures Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons with only negative tests during the 31-180 days after the last negative test. Results We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those with a negative test, (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). Conclusions and Relevance We documented a substantial relative risk of pulmonary embolism and large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.
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Affiliation(s)
- Jay K. Varma
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | | | - Jason P. Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA
| | - Dhruv J. Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
- Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Mark G. Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Russell L. Rothman
- Institute for Medicine and Public Health, Vanderbilt University Medical Center Nashville, TN
| | | | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Kristin Lyman
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Jiang Bian
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL
| | - Jie Xu
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL
| | - Elizabeth A. Shenkman
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL
| | | | | | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
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Xu J, Zhang H, Zhang H, Bian J, Wang F. Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design. Sci Rep 2023; 13:613. [PMID: 36635438 PMCID: PMC9837131 DOI: 10.1038/s41598-023-27856-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients' clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual's EHRs can determine the subphenotypes-homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same subgroup not only share similar clinical characteristics but also at similar risk levels of encountering severe adverse events (SAEs). We evaluated our algorithm on two previously conducted clinical trials with EHRs from the OneFlorida+ Clinical Research Consortium. Our model can clearly identify the patient subgroups who are more likely to suffer or not suffer from SAEs as subphenotypes in a transparent and interpretable way. Our approach identified a set of clinical topics and derived novel patient representations based on them. Each clinical topic represents a certain clinical event composition pattern learned from the patient EHRs. Tested on both trials, patient subgroup (#SAE=0) and patient subgroup (#SAE>0) can be well-separated by k-means clustering using the inferred topics. The inferred topics characterized as likely to align with the patient subgroup (#SAE>0) revealed meaningful combinations of clinical features and can provide data-driven recommendations for refining the exclusion criteria of clinical trials. The proposed supervised topic modeling approach can infer the clinical topics from the subphenotypes with or without SAEs. The potential rules for describing the patient subgroups with SAEs can be further derived to inform the design of clinical trial eligibility criteria.
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Affiliation(s)
- Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Hansi Zhang
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
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25
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Zhang H, Zang C, Xu Z, Zhang Y, Xu J, Bian J, Morozyuk D, Khullar D, Zhang Y, Nordvig AS, Schenck EJ, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner MG, Carton TW, Wang F, Kaushal R. Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes. Nat Med 2023; 29:226-235. [PMID: 36456834 PMCID: PMC9873564 DOI: 10.1038/s41591-022-02116-3] [Citation(s) in RCA: 60] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 11/02/2022] [Indexed: 12/05/2022]
Abstract
The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.
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Affiliation(s)
- Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Dmitry Morozyuk
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Dhruv Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yiye Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Anna S Nordvig
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Russell L Rothman
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason P Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Kristin Lyman
- Louisiana Public Health Institute, New Orleans, LA, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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Zheng Y, Bian J, Hart J, Laden F, Soo-Tung Wen T, Zhao J, Qin H, Hu H. PM 2.5 Constituents and Onset of Gestational Diabetes Mellitus: Identifying Susceptible Exposure Windows. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2022; 291:119409. [PMID: 37151750 PMCID: PMC10162772 DOI: 10.1016/j.atmosenv.2022.119409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Fine particulate matter (PM2.5) has been linked to gestational diabetes mellitus (GDM). However, PM2.5 is a complex mixture with large spatiotemporal heterogeneities, and women with early-onset GDM (i.e., diagnosed before 24th gestation week) have distinct maternal characteristics and a higher risk of worse health outcomes compared with those with late-onset GDM (i.e., diagnosed in or after 24th gestation week). We aimed to examine differential impacts of PM2.5 and its constituents on early- vs. late-onset GDM, and to identify corresponding susceptible exposure windows. We leveraged statewide linked electronic health records and birth records data in Florida in 2012-2017. Exposures to PM2.5 and its constituents (i.e., sulfate [SO4 2-], ammonium [NH4 +], nitrate [NO3 -], organic matter [OM], black carbon [BC], mineral dust [DUST], and sea-salt [SS]) were spatiotemporally linked to pregnant women based on their residential histories. Cox proportional hazards models and multinomial logistic regression were used to examine the associations of PM2.5 and its constituents with GDM and its onsets. Distributed non-linear lag models were implemented to identify susceptible exposure windows. Exposures to PM2.5, SO4 2-, NH4 +, and BC were statistically significantly associated with higher hazards of GDM. Exposures to PM2.5 during weeks 1-12 of gestation were positively associated with GDM. Associations of early-onset GDM with PM2.5 in the 1st and 2nd trimesters, SO4 2- in the 1st and 2nd trimesters, and NO3 - in the preconception and 1st trimester were considerably stronger than observations for late-onset GDM. Our findings suggest there are differential associations of PM2.5 and its constituents with early- vs. late-onset GDM, with different susceptible exposure windows. This study helps better understand the impacts of air pollution on GDM accounting for its physiological heterogeneity.
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Affiliation(s)
- Yi Zheng
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jaime Hart
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Francine Laden
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Tony Soo-Tung Wen
- Department of Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Huaizhen Qin
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Hui Hu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Yang G, Williams R, Wang L, Farhadfar N, Chen Y, Loiacono AT, Bian J, Holliday LS, Katz J, Gong Y. Medication-Related Osteonecrosis of the Jaw in Cancer Patients: Result from the OneFlorida Clinical Research Consortium. J Bone Miner Res 2022; 37:2466-2471. [PMID: 36151778 PMCID: PMC9772085 DOI: 10.1002/jbmr.4708] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/12/2022] [Accepted: 09/17/2022] [Indexed: 01/22/2023]
Abstract
Medication-related osteonecrosis of the jaw (MRONJ) is a rare but severely debilitating drug-induced bone disorder in the jawbone region. The first MRONJ was reported in 2003 after bisphosphonate (BP) exposure. Recently, other drugs, such as receptor activator of NF-κB ligand (RANKL) inhibitor denosumab and antiangiogenic agents, were also associated with MRONJ. The purpose of this study was to evaluate the incidence and risk factors for MRONJ related to BPs or denosumab in cancer patients in real-world clinical settings using data from the OneFlorida Clinical Research Consortium. We queried the electronic health records of participants with prescriptions of intravenous (IV) BPs or denosumab between January 1, 2012, and September 1, 2021, in the OneFlorida Consortium. Time to MRONJ diagnosis was evaluated using the Kaplan-Meier method, and Cox regression analysis was performed to estimate the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for MRONJ. A total of 5689 participants had one or more prescriptions of IV BP or denosumab within this study period and were included in this study. Among these participants, 52 (0.9%) had a diagnosis of MRONJ. The overall rate of MRONJ was 0.73%, 0.86%, and 3.50% in the cancer patients treated with IV BPs, denosumab, and sequential IV BPs and denosumab, respectively. The risk of MRONJ was similar in participants treated with denosumab alone compared to those treated with IV BPs alone (HR: 1.25, 95% CI: 0.66-2.34, p = .49). Patients with sequential prescription of IV BP and denosumab were at much higher risk for MRONJ, with an adjusted HR of 4.49, 95% CI of 1.96-10.28, p = .0004. In conclusion, in real-world clinical settings, the rates of MRONJ associated with IV BPs and denosumab were similar, while the sequential treatment of these two drug classes was associated with a much higher risk of MRONJ. © 2022 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Guang Yang
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, USA
- Department of Pharmacology, Northwestern University, Chicago IL, USA
| | - Roy Williams
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Lishu Wang
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Nosha Farhadfar
- Division of Hematology/Oncology, University of Florida College of Medicine, Gainesville FL, USA
| | - Yiqing Chen
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, USA
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center in Houston
| | - Alexander T. Loiacono
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville FL, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville FL, USA
| | - L. Shannon Holliday
- Department of Orthodontics, College of Dentistry, University of Florida, Gainesville, Florida, USA
| | - Joseph Katz
- Department of Oral Medicine, College of Dentistry, University of Florida, Gainesville FL, USA
| | - Yan Gong
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, USA
- University of Florida Health Cancer Center, University of Florida, Gainesville FL, USA
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Marsolo K, Kiernan D, Toh S, Phua J, Louzao D, Haynes K, Weiner M, Angulo F, Bailey C, Bian J, Fort D, Grannis S, Krishnamurthy AK, Nair V, Rivera P, Silverstein J, Zirkle M, Carton T. Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet®, the National Patient-Centered Clinical Research Network. J Am Med Inform Assoc 2022; 30:447-455. [PMID: 36451264 PMCID: PMC9933062 DOI: 10.1093/jamia/ocac229] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/03/2022] [Accepted: 11/16/2022] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVE This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network. MATERIAL AND METHODS Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population. RESULTS There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%. DISCUSSION The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population. CONCLUSIONS This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe.
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Affiliation(s)
- Keith Marsolo
- Corresponding Author: Keith Marsolo, PhD, Department of Population Health Sciences, Duke University School of Medicine, 215 Morris Street, Durham, NC 27710, USA;
| | - Daniel Kiernan
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | | | - Darcy Louzao
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Kevin Haynes
- Scientific Affairs, HealthCore, Inc., Wilmington, Delaware, USA
| | - Mark Weiner
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Francisco Angulo
- Department of Medicine, Cook County Health and Hospital System, Chicago, Illinois, USA
| | - Charles Bailey
- Department of Pediatrics, Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jiang Bian
- Department of Health Outcomes and Bioinformatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Daniel Fort
- Center for Outcomes and Health Services Research, Ochsner Health, New Orleans, Louisiana, USA
| | - Shaun Grannis
- Regenstrief Institute, Indiana University, Indianapolis, Indiana, USA
| | | | | | | | - Jonathan Silverstein
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Thomas Carton
- Louisiana Public Health Institute, New Orleans, Louisiana, USA
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Kiernan D, Carton T, Toh S, Phua J, Zirkle M, Louzao D, Haynes K, Weiner M, Angulo F, Bailey C, Bian J, Fort D, Grannis S, Krishnamurthy AK, Nair V, Rivera P, Silverstein J, Marsolo K. Establishing a framework for privacy-preserving record linkage among electronic health record and administrative claims databases within PCORnet ®, the National Patient-Centered Clinical Research Network. BMC Res Notes 2022; 15:337. [PMID: 36316778 PMCID: PMC9620597 DOI: 10.1186/s13104-022-06243-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 10/21/2022] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVE The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network. RESULTS We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet®). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens. We queried the sites for patients with a clinical encounter in 2018 or 2019 and matched their tokens to determine whether overlap existed. We described patient overlap among the sites and generated a "deduplicated" table of patient demographic characteristics. Overlapping patients were found in 3 of the 6 site-pairs. Following deduplication, the total patient count was 3,108,515 (0.11% reduction), with the largest reduction in count for patients with an "Other/Missing" value for Sex; from 198 to 163 (17.6% reduction). The PPRL solution successfully links patients across data sources using distributed queries without directly accessing patient PII. The overlap queries and analysis performed in this pilot is being replicated across the full network to provide additional insight into patient linkages among a distributed research network.
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Affiliation(s)
- Daniel Kiernan
- grid.38142.3c000000041936754XDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215 USA
| | - Thomas Carton
- grid.468191.30000 0004 0626 8374Louisiana Public Health Institute, New Orleans, LA 70112 USA
| | - Sengwee Toh
- grid.38142.3c000000041936754XDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215 USA
| | | | - Maryan Zirkle
- grid.507100.30000 0004 6004 8305Cohen Veterans Bioscience, New York, NY 10018 USA
| | - Darcy Louzao
- grid.26009.3d0000 0004 1936 7961Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC 27710 USA
| | - Kevin Haynes
- grid.467616.40000 0001 0698 1725Scientific Affairs, HealthCore, Inc., Wilmington, DE 19801 USA
| | - Mark Weiner
- grid.5386.8000000041936877XDepartment of Medicine, Weill Cornell Medicine, New York, NY 10021 USA
| | - Francisco Angulo
- grid.428291.4Department of Medicine, Cook County Health and Hospital System, Chicago, IL 60612 USA
| | - Charles Bailey
- grid.239552.a0000 0001 0680 8770Applied Clinical Research Center, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Jiang Bian
- grid.15276.370000 0004 1936 8091College of Medicine, University of Florida, Gainesville, FL 32610 USA
| | - Daniel Fort
- grid.416735.20000 0001 0229 4979Center for Outcomes and Health Services Research, Ochsner Health, New Orleans, LA 70121 USA
| | - Shaun Grannis
- grid.257413.60000 0001 2287 3919Regenstrief Institute, Indiana University, Indianapolis, IN 46202 USA
| | | | | | - Pedro Rivera
- grid.429963.30000 0004 0628 3400OCHIN, Inc., Portland, OR 97201 USA
| | - Jonathan Silverstein
- grid.21925.3d0000 0004 1936 9000Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206 USA
| | - Keith Marsolo
- grid.26009.3d0000 0004 1936 7961Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC 27710 USA ,grid.26009.3d0000 0004 1936 7961Department of Population Health Sciences, Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC 27710 USA
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Zhang Y, Hu H, Fokaidis V, Lewis C, Xu J, Zang C, Xu Z, Wang F, Koropsak M, Bian J, Hall J, Rothman RL, Shenkman EA, Wei WQ, Weiner MG, Carton TW, Kaushal R. Identifying Contextual and Spatial Risk Factors for Post-Acute Sequelae of SARS-CoV-2 Infection: An EHR-based Cohort Study from the RECOVER Program. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.10.13.22281010. [PMID: 36263067 PMCID: PMC9580388 DOI: 10.1101/2022.10.13.22281010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the contextual and spatial risk factors for PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified contextual and spatial risk factors from nearly 200 environmental characteristics for 23 PASC symptoms and conditions of eight organ systems. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each contextual and spatial factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) contextual and spatial characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), criteria air pollutants (e.g., sulfur dioxide), particulate matter (PM 2.5 ) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, respiratory, blood, circulatory, endocrine, and other organ systems. Specific contextual and spatial risk factors for each PASC condition and symptom were different across New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular contextual and spatial characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.
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Affiliation(s)
- Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Hui Hu
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Vasilios Fokaidis
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Colby Lewis
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Jie Xu
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Michael Koropsak
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Jiang Bian
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL
| | - Jaclyn Hall
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL
| | - Russell L. Rothman
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN
| | - Elizabeth A. Shenkman
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Mark G. Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | | | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
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Goldberg D, Mantero A, Kaplan D, Delgado C, John B, Nuchovich N, Emanuel E, Reese PP. Accurate long-term prediction of death for patients with cirrhosis. Hepatology 2022; 76:700-711. [PMID: 35278226 PMCID: PMC9378359 DOI: 10.1002/hep.32457] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 03/03/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIMS Cirrhosis is a major cause of death and is associated with extensive health care use. Patients with cirrhosis have complex treatment choices due to risks of morbidity and mortality. To optimally counsel and treat patients with cirrhosis requires tools to predict their longer-term liver-related survival. We sought to develop and validate a risk score to predict longer-term survival of patients with cirrhosis. APPROACH AND RESULTS We conducted a retrospective cohort study of adults with cirrhosis with no major life-limiting comorbidities. Adults with cirrhosis within the Veterans Health Administration were used for model training and internal validation, and external validation used the OneFlorida Clinical Research Consortium. We used four model-building approaches including variables predictive of cirrhosis-related mortality, focused on discrimination at key time points (1, 3, 5, and 10 years). Among 30,263 patients with cirrhosis ≤75 years old without major life-limiting comorbidities and complete laboratory data during the baseline period, the boosted survival tree models had the highest discrimination, with 1-year, 3-year, 5-year, and 10-year survival rates of 0.77, 0.81, 0.84, and 0.88, respectively. The 1-year, 3-year, and 5-year discrimination was nearly identical in external validation. Secondary analyses with imputation of missing data and subgroups by etiology of liver disease had similar results to the primary model. CONCLUSIONS We developed and validated (internally and externally) a risk score to predict longer-term survival of patients with cirrhosis. This score would transform management of patients with cirrhosis in terms of referral to specialty care and treatment decision-making for non-liver-related care.
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Affiliation(s)
- David Goldberg
- Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL
| | - Alejandro Mantero
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL
| | - David Kaplan
- Division of Gastroenterology, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
| | - Cindy Delgado
- Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
| | - Binu John
- Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
- Bruce Carter VA Medica Center, Miami, FL
| | - Nadine Nuchovich
- Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
| | - Ezekiel Emanuel
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Peter P. Reese
- Renal-Electrolye and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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Hepatitis C virus (HCV) seroprevalence, RNA detection, and genotype distribution across Florida, 2015-2018. Prev Med 2022; 161:107136. [PMID: 35803347 PMCID: PMC9598903 DOI: 10.1016/j.ypmed.2022.107136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/14/2022] [Accepted: 07/02/2022] [Indexed: 11/21/2022]
Abstract
Chronic hepatitis C virus (HCV) infection is a leading cause of hepatocellular carcinoma (HCC) in the U.S. Due to high rates of HCV among baby boomers (born 1945-1965), it was recommended they receive universal screening. This was expanded to all U.S. adults in 2020 due to evidence of increasing rates of chronic HCV in younger adults. An assessment of HCV burden across demographics is crucial to understand the future burden of HCC and target under-screened adults for HCV. Using the OneFlorida Clinical Research Consortium, of more than one million individuals in Florida, all HCV antibody and viral RNA tests completed from 2015 to 2018 were identified. HCV seroprevalence, HCV viral load (active infection), and HCV genotype distribution by risk groups were assessed. Overall, HCV seroprevalence and active infection were highest among White non-Hispanic individuals, males, and baby boomers. However, odds of a positive HCV antibody test were higher among Black non-Hispanic individuals born before 1945 (aOR: 2.74; 95% CI: 1.98-3.78) or 1945-1965 (aOR: 1.46; 95% CI: 1.36-1.56) compared to White non-Hispanic individuals. In contrast, among individuals born after 1965, Black non-Hispanics were less likely than White non-Hispanics to test HCV antibody positive (aOR of 0.5-0.28). A similar age/race pattern was observed for active HCV infection. There was a higher prevalence of genotype 1A and 3 and lower prevalence of 1B in younger adults. Patterns of HCV seroprevalence and active HCV infection identified in our study support the recent shift from age and risk-based screening guidelines to universal adult screening.
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Chen Z, Zhang H, George TJ, Guo Y, Prosperi M, Guo J, Braithwaite D, Wang F, Kibbe W, Wagner L, Bian J. Simulating Colorectal Cancer Trials Using Real-World Data. JCO Clin Cancer Inform 2022; 6:e2100195. [PMID: 35839432 PMCID: PMC9848597 DOI: 10.1200/cci.21.00195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/04/2022] [Accepted: 06/02/2022] [Indexed: 02/02/2023] Open
Abstract
PURPOSE Using real-world data (RWD)-based trial simulation approach, we aim to simulate colorectal cancer (CRC) trials and examine both effectiveness and safety end points in different simulation scenarios. METHODS We identified five phase III trials comparing new treatment regimens with an US Food and Drug Administration-approved first-line treatment in patients with metastatic CRC (ie, fluorouracil, leucovorin, and irinotecan) as the standard-of-care (SOC) control arm. Using Electronic Health Record-derived data from the OneFlorida network, we defined the study populations and outcome measures using the protocols from the original trials. Our design scenarios were (1) simulation of the SOC fluorouracil, leucovorin, and irinotecan arm and (2) comparative effectiveness research (CER) simulation of the control and experimental arms. For each scenario, we adjusted for random assignment, sampling, and dropout. We used overall survival (OS) and severe adverse events (SAEs) to measure effectiveness and safety. RESULTS We conducted CER simulations for two trials, and SOC simulations for three trials. The effect sizes of our simulated trials were stable across all simulation runs. Compared with the original trials, we observed longer OS and higher mean number of SAEs in both CER and SOC simulation. In the two CER simulations, hazard ratios associated with death from simulations were similar to that reported in the original trials. Consistent with the original trials, we found higher risk ratios of SAEs in the experiment arm, suggesting potentially higher toxicities from the new treatment regimen. We also observed similar SAE rates across all simulations compared with the original trials. CONCLUSION In this study, we simulated five CRC trials, and tested two simulation scenarios with several different configurations demonstrated that our simulations can robustly generate effectiveness and safety outcomes comparable with the original trials using real-world data.
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Affiliation(s)
- Zhaoyi Chen
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL
- Cancer Informatics Share Resource, University of Florida Health Cancer Center, Gainesville, FL
| | - Hansi Zhang
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL
| | - Thomas J. George
- Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL
| | - Yi Guo
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL
- Cancer Informatics Share Resource, University of Florida Health Cancer Center, Gainesville, FL
| | - Mattia Prosperi
- Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, Gainesville, FL
| | - Jingchuan Guo
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL
| | - Dejana Braithwaite
- Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, Gainesville, FL
- Department of Surgery, College of Medicine, University of Florida, Gainesville, FL
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, NY
| | - Warren Kibbe
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Lynne Wagner
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL
- Cancer Informatics Share Resource, University of Florida Health Cancer Center, Gainesville, FL
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Liu X, Duan R, Luo C, Ogdie A, Moore JH, Kranzler HR, Bian J, Chen Y. Multisite learning of high-dimensional heterogeneous data with applications to opioid use disorder study of 15,000 patients across 5 clinical sites. Sci Rep 2022; 12:11073. [PMID: 35773438 PMCID: PMC9245877 DOI: 10.1038/s41598-022-14029-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/31/2022] [Indexed: 11/17/2022] Open
Abstract
Integrating data across institutions can improve learning efficiency. To integrate data efficiently while protecting privacy, we propose A one-shot, summary-statistics-based, Distributed Algorithm for fitting Penalized (ADAP) regression models across multiple datasets. ADAP utilizes patient-level data from a lead site and incorporates the first-order (ADAP1) and second-order gradients (ADAP2) of the objective function from collaborating sites to construct a surrogate objective function at the lead site, where model fitting is then completed with proper regularizations applied. We evaluate the performance of the proposed method using both simulation and a real-world application to study risk factors for opioid use disorder (OUD) using 15,000 patient data from the OneFlorida Clinical Research Consortium. Our results show that ADAP performs nearly the same as the pooled estimator but achieves higher estimation accuracy and better variable selection than the local and average estimators. Moreover, ADAP2 successfully handles heterogeneity in covariate distributions.
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Affiliation(s)
- Xiaokang Liu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Chongliang Luo
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, 423 Guardian Drive, Philadelphia, PA, 19104, USA
- Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Alexis Ogdie
- Department of Medicine, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90096, USA
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine and the VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida Health Cancer Center, Gainesville, FL, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
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Braithwaite D, Karanth SD, Slatore CG, Zhang D, Bian J, Meza R, Jeon J, Tammemagi M, Schabath M, Wheeler M, Guo Y, Hochhegger B, Kaye FJ, Silvestri GA, Gould MK. Personalised Lung Cancer Screening (PLuS) study to assess the importance of coexisting chronic conditions to clinical practice and policy: protocol for a multicentre observational study. BMJ Open 2022; 12:e064142. [PMID: 35732383 PMCID: PMC9226937 DOI: 10.1136/bmjopen-2022-064142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/30/2022] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION Lung cancer is the leading cause of cancer death in the USA and worldwide, and lung cancer screening (LCS) with low-dose CT (LDCT) has the potential to improve lung cancer outcomes. A critical question is whether the ratio of potential benefits to harms found in prior LCS trials applies to an older and potentially sicker population. The Personalised Lung Cancer Screening (PLuS) study will help close this knowledge gap by leveraging real-world data to fully characterise LCS recipients. The principal goal of the PLuS study is to characterise the comorbidity burden of individuals undergoing LCS and quantify the benefits and harms of LCS to enable informed decision-making. METHODS AND ANALYSIS PLuS is a multicentre observational study designed to assemble an LCS cohort from the electronic health records of ~40 000 individuals undergoing annual LCS with LDCT from 2016 to 2022. Data will be integrated into a unified repository to (1) examine the burden of multimorbidity by race/ethnicity, socioeconomic status and age; (2) quantify potential benefits and harms; and (3) use the observational data with validated simulation models in the Cancer Intervention and Surveillance Modeling Network (CISNET) to provide LCS outcomes in the real-world US population. We will fit a multivariable logistic regression model to estimate the adjusted ORs of comorbidity, functional limitations and impaired pulmonary function adjusted for relevant covariates. We will also estimate the cumulative risk of LCS outcomes using discrete-time survival models. To our knowledge, this is the first study to combine observational data and simulation models to estimate the long-term impact of LCS with LDCT. ETHICS AND DISSEMINATION The study was approved by the Kaiser Permanente Southern California Institutional Review Board and VA Portland Health Care System. The results will be disseminated through publications and presentations at national and international conferences. Safety considerations include protection of patient confidentiality.
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Affiliation(s)
- Dejana Braithwaite
- Department of Surgery, University of Florida, Gainesville, Florida, USA
- Cancer Center, UF Health, Gainesville, Florida, USA
| | - Shama D Karanth
- Cancer Center, UF Health, Gainesville, Florida, USA
- Institute on Aging, University of Florida, Gainesville, Florida, USA
| | - Christopher G Slatore
- Center to Improve Veteran Involvement in Care, Portland VA Medical Center, Portland, Oregon, USA
| | - Dongyu Zhang
- Cancer Center, UF Health, Gainesville, Florida, USA
- Department of Epidemiology, University of Florida, Gainesville, Florida, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Martin Tammemagi
- Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada
| | - Mattthew Schabath
- Department of Cancer Epidemiology, H Lee Moffitt Cancer Center and Research Center Inc, Tampa, Florida, USA
| | - Meghann Wheeler
- Department of Epidemiology, University of Florida, Gainesville, Florida, USA
| | - Yi Guo
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Bruno Hochhegger
- Department of Radiology, University of Florida, Gainesville, Florida, USA
| | - Frederic J Kaye
- Division of Hematology and Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Gerard A Silvestri
- Division of Pulmonary and Critical Care Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Michael K Gould
- Department of Health Systems Science, Kaiser Permanente Bernard J Tyson School of Medicine, Pasadena, California, USA
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Zhang H, Zang C, Xu Z, Zhang Y, Xu J, Bian J, Morozyuk D, Khullar D, Zhang Y, Nordvig AS, Schenck EJ, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner M, Carton TW, Wang F, Kaushal R. Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER Initiative. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022. [PMID: 35665007 DOI: 10.1101/2022.05.21.22275412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated, or newly incident in the post-acute SARS-CoV-2 infection period of COVID-19 patients. Most studies have examined these conditions individually without providing concluding evidence on co-occurring conditions. To answer this question, this study leveraged electronic health records (EHRs) from two large clinical research networks from the national Patient-Centered Clinical Research Network (PCORnet) and investigated patients' newly incident diagnoses that appeared within 30 to 180 days after a documented SARS-CoV-2 infection. Through machine learning, we identified four reproducible subphenotypes of PASC dominated by blood and circulatory system, respiratory, musculoskeletal and nervous system, and digestive system problems, respectively. We also demonstrated that these subphenotypes were associated with distinct patterns of patient demographics, underlying conditions present prior to SARS-CoV-2 infection, acute infection phase severity, and use of new medications in the post-acute period. Our study provides novel insights into the heterogeneity of PASC and can inform stratified decision-making in the treatment of COVID-19 patients with PASC conditions.
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Sandifer PA, Juster RP, Seeman TE, Lichtveld MY, Singer BH. Allostatic load in the context of disasters. Psychoneuroendocrinology 2022; 140:105725. [PMID: 35306472 PMCID: PMC8919761 DOI: 10.1016/j.psyneuen.2022.105725] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/28/2022] [Accepted: 03/10/2022] [Indexed: 11/18/2022]
Abstract
Environmental disasters, pandemics, and other major traumatic events such as the Covid-19 pandemic or war contribute to psychosocial stress which manifests in a wide range of mental and physical consequences. The increasing frequency and severity of such events suggest that the adverse effects of toxic stress are likely to become more widespread and pervasive in the future. The allostatic load (AL) model has important elements that lend themselves well for identifying adverse health effects of disasters. Here we examine several articulations of AL from the standpoint of using AL to gauge short- and long-term health effects of disasters and to provide predictive capacity that would enable mitigation or prevention of some disaster-related health consequences. We developed a transdisciplinary framework combining indices of psychosocial AL and physiological AL to produce a robust estimate of overall AL in people affected by disasters and other traumatic events. In conclusion, we urge researchers to consider the potential of using AL as a component in a proposed disaster-oriented human health observing system.
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Affiliation(s)
- Paul A Sandifer
- Center for Coastal Environmental and Human Health, School of Sciences and Mathematics, College of Charleston, 66 George Street, Charleston, SC 29424, USA.
| | - Robert-Paul Juster
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Canada
| | - Teresa E Seeman
- David Geffen School of Medicine at UCLA, University of California Los Angeles, CA, USA
| | - Maureen Y Lichtveld
- Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Burton H Singer
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
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Carlson AM, Sollero CV, Nair KV, Sillau S, Wu Q, Gyang T, Li Z, Armstrong MJ. Prevalence of multiple sclerosis and treatment utilization in a large, highly diverse population. Mult Scler Relat Disord 2022; 61:103784. [DOI: 10.1016/j.msard.2022.103784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/16/2022] [Accepted: 03/28/2022] [Indexed: 10/18/2022]
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Hewitt KC, Marra DE, Block C, Cysique LA, Drane DL, Haddad MM, Łojek E, McDonald CR, Reyes A, Eversole K, Bowers D. Central Nervous System Manifestations of COVID-19: A Critical Review and Proposed Research Agenda. J Int Neuropsychol Soc 2022; 28:311-325. [PMID: 33858556 PMCID: PMC10035233 DOI: 10.1017/s1355617721000345] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE On March 11, 2020, the World Health Organization declared an outbreak of a new viral entity, coronavirus 2019 (COVID-19), to be a worldwide pandemic. The characteristics of this virus, as well as its short- and long-term implications, are not yet well understood. The objective of the current paper was to provide a critical review of the emerging literature on COVID-19 and its implications for neurological, neuropsychiatric, and cognitive functioning. METHOD A critical review of recently published empirical research, case studies, and reviews pertaining to central nervous system (CNS) complications of COVID-19 was conducted by searching PubMed, PubMed Central, Google Scholar, and bioRxiv. RESULTS After considering the available literature, areas thought to be most pertinent to clinical and research neuropsychologists, including CNS manifestations, neurologic symptoms/syndromes, neuroimaging, and potential long-term implications of COVID-19 infection, were reviewed. CONCLUSION Once thought to be merely a respiratory virus, the scientific and medical communities have realized COVID-19 to have broader effects on renal, vascular, and neurological body systems. The question of cognitive deficits is not yet well studied, but neuropsychologists will undoubtedly play an important role in the years to come.
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Affiliation(s)
- Kelsey C. Hewitt
- Emory University School of Medicine, Department of Neurology, Atlanta, GA 30329, USA
| | - David E. Marra
- University of Florida, Department of Clinical and Health Psychology, Gainesville, FL 32610, USA
| | - Cady Block
- Emory University School of Medicine, Department of Neurology, Atlanta, GA 30329, USA
| | - Lucette A. Cysique
- University of New South Wales, Department of Psychology, The Alfred Hospital, Melbourne, 3004, Australia
- St. Vincent’s Applied Medical Research Centre, Sydney, New South Wales, 2011, Australia
| | - Daniel L. Drane
- Emory University School of Medicine, Department of Neurology, Atlanta, GA 30329, USA
- Emory University, Department of Pediatrics, Atlanta, GA 30322, USA
| | - Michelle M. Haddad
- Emory University, Department of Rehabilitation Medicine, Atlanta, GA 30329, USA
| | - Emilia Łojek
- University of Warsaw, Department of Psychology, Warszawa, 00-183, Poland
| | - Carrie R. McDonald
- University of California-San Diego, Department of Psychiatry, La Jolla, CA 92093, USA
| | - Anny Reyes
- University of California-San Diego, Department of Psychiatry, La Jolla, CA 92093, USA
| | - Kara Eversole
- James Madison University, Department of Graduate Psychology, Harrisonburg, VA 22807, USA
| | - Dawn Bowers
- University of Florida, Department of Clinical and Health Psychology, Gainesville, FL 32610, USA
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Yang S, Shih YCT, Huo J, Mehta HJ, Wu Y, Salloum RG, Alvarado M, Zhang D, Braithwaite D, Guo Y, Bian J. Procedural complications associated with invasive diagnostic procedures after lung cancer screening with low-dose computed tomography. Lung Cancer 2022; 165:141-144. [PMID: 35124410 PMCID: PMC9250944 DOI: 10.1016/j.lungcan.2021.12.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/24/2021] [Accepted: 12/29/2021] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Although the National Lung Screening Trial (NLST) has proven low-dose computed tomography (LDCT) is effective for lung cancer screening, little is known about complication rates from invasive diagnostic procedures (IDPs) after LDCT in real-world settings. In this study, we used the real-world data from a large clinical research network to estimate the complication rates associated with IDPs after LDCT. METHODS Using 2014-2021 electronic health records and claims data from the OneFlorida clinical research network, we identified case individuals who underwent an IDP (i.e., cytology or needle biopsy, bronchoscopy, thoracic surgery, and other surgery) within 12 months of their first LDCT. We matched each case with one control individual who underwent an LDCT but without any IDPs. We calculated 3-month incremental complication rates as the difference in the complication rate between the case and control groups by IDP and complication severity. RESULTS Among 7,041 individuals who underwent an LDCT, 301 (4.3%) subsequently had an IDP within 12 months following the LDCT. The overall 3-month incremental complication rate was 16.6% (95% confidence interval [CI]: 9.9% - 23.1%), higher than that reported in the NLST (9.4%). The overall incremental complication rate was 5.6% (95% CI: 1.9% - 9.6%) for major, 8.6% (95% CI: 3.1% - 14.1%) for intermediate, and 13.2% (95% CI: 8.1% - 18.5%) for minor complications. CONCLUSIONS It is important to ensure adherence to clinical guidelines for nodule management and downstream work-up to minimize potential harms from screening.
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Affiliation(s)
- Shuang Yang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Ya-Chen Tina Shih
- Department of Health Services Research, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jinhai Huo
- Bristol-Myers Squibb, Princeton Pike, NJ, United States
| | - Hiren J Mehta
- Division of Pulmonary, Critical Care, and Sleep Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Ramzi G Salloum
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Michelle Alvarado
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
| | - Dongyu Zhang
- Cancer Control and Population Sciences Program, University of Florida, Gainesville, FL, United States
| | - Dejana Braithwaite
- Cancer Control and Population Sciences Program, University of Florida, Gainesville, FL, United States
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.
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Fishe J, Zheng Y, Lyu T, Bian J, Hu H. Environmental effects on acute exacerbations of respiratory diseases: A real-world big data study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:150352. [PMID: 34555607 PMCID: PMC8627495 DOI: 10.1016/j.scitotenv.2021.150352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/11/2021] [Accepted: 09/11/2021] [Indexed: 05/06/2023]
Abstract
BACKGROUND The effects of weather periods, race/ethnicity, and sex on environmental triggers for respiratory exacerbations are not well understood. This study linked the OneFlorida network (~15 million patients) with an external exposome database to analyze environmental triggers for asthma, bronchitis, and COPD exacerbations while accounting for seasonality, sex, and race/ethnicity. METHODS This is a case-crossover study of OneFlorida database from 2012 to 2017 examining associations of asthma, bronchitis, and COPD exacerbations with exposures to heat index, PM 2.5 and O 3. We spatiotemporally linked exposures using patients' residential addresses to generate average exposures during hazard and control periods, with each case serving as its own control. We considered age, sex, race/ethnicity, and neighborhood deprivation index as potential effect modifiers in conditional logistic regression models. RESULTS A total of 1,148,506 exacerbations among 533,446 patients were included. Across all three conditions, hotter heat indices conferred increasing exacerbation odds, except during November to March, where the opposite was seen. There were significant differences when stratified by race/ethnicity (e.g., for asthma in April, May, and October, heat index quartile 4, odds were 1.49 (95% confidence interval (CI) 1.42-1.57) for Non-Hispanic Blacks and 2.04 (95% CI 1.92-2.17) for Hispanics compared to 1.27 (95% CI 1.19-1.36) for Non-Hispanic Whites). Pediatric patients' odds of asthma and bronchitis exacerbations were significantly lower than adults in certain circumstances (e.g., for asthma during June - September, pediatric odds 0.71 (95% CI 0.68-0.74) and adult odds 0.82 (95% CI 0.79-0.85) for the highest quartile of PM 2.5). CONCLUSION This study of acute exacerbations of asthma, bronchitis, and COPD found exacerbation risk after exposure to heat index, PM 2.5 and O 3 varies by weather period, age, and race/ethnicity. Future work can build upon these results to alert vulnerable populations to exacerbation triggers.
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Affiliation(s)
- Jennifer Fishe
- Department of Emergency Medicine, University of Florida College of Medicine - Jacksonville, United States of America; Center for Data Solutions, University of Florida College of Medicine - Jacksonville, United States of America.
| | - Yi Zheng
- Department of Epidemiology, University of Florida College of Medicine & College of Public Health and Health Professions, United States of America
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States of America
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States of America
| | - Hui Hu
- Department of Epidemiology, University of Florida College of Medicine & College of Public Health and Health Professions, United States of America
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He Z, Tian S, Erdengasileng A, Charness N, Bian J. Temporal Subtyping of Alzheimer's Disease Using Medical Conditions Preceding Alzheimer's Disease Onset in Electronic Health Records. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2022:226-235. [PMID: 35854753 PMCID: PMC9285183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Subtyping of Alzheimer's disease (AD) can facilitate diagnosis, treatment, prognosis and disease management. It can also support the testing of new prevention and treatment strategies through clinical trials. In this study, we employed spectral clustering to cluster 29,922 AD patients in the OneFlorida Data Trust using their longitudinal EHR data of diagnosis and conditions into four subtypes. These subtypes exhibit different patterns of progression of other conditions prior to the first AD diagnosis. In addition, according to the results of various statistical tests, these subtypes are also significantly different with respect to demographics, mortality, and prescription medications after the AD diagnosis. This study could potentially facilitate early detection and personalized treatment of AD as well as data-driven generalizability assessment of clinical trials for AD.
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Affiliation(s)
- Zhe He
- Florida State University, Tallahassee, Florida USA
| | - Shubo Tian
- Florida State University, Tallahassee, Florida USA
| | | | | | - Jiang Bian
- University of Florida, Gainesville, Florida USA
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Vadaparampil ST, Fuzzell LN, Rathwell J, Reich RR, Shenkman E, Nelson DR, Kobetz E, Jones PD, Roetzheim R, Giuliano AR. HCV testing: Order and completion rates among baby boomers obtaining care from seven health systems in Florida, 2015-2017. Prev Med 2021; 153:106222. [PMID: 32721414 PMCID: PMC7854771 DOI: 10.1016/j.ypmed.2020.106222] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 07/16/2020] [Accepted: 07/20/2020] [Indexed: 12/18/2022]
Abstract
Many U.S. residents infected with hepatitis C virus (HCV) are baby boomers (born 1945-1965), who remain undiagnosed. Past CDC and USPSTF guidelines recommended one-time HCV testing for all baby boomers, with newer guidelines recommending universal screening for all adults. This retrospective cohort study examined electronic medical records for patient visits from 2015 to 2017 within the OneFlorida Data Trust and University of South Florida Health system. We assessed percentages of HCV tests ordered and completed across four age groups (those born before 1945, 1945-1965, 1966-1985, and after 1985). In 2019, we used logistic regression to examine factors associated with HCV test ordering and completion among baby boomers, including age, race, sex, number of primary care visits, HIV status, hepatitis diagnosis, and liver cancer history. All age groups had low rates of HCV test orders. 4.4% of baby boomers had a test ordered in 2015, and 6.7% in 2016. Of those, 94.5% and 89.7% completed testing, respectively. All other races/ethnicities had lower likelihood of testing completion than Whites (Blacks (aOR 0.82, 95%, CI 0.75-0.91); Asians (0.69, 0.52-0.92); Hispanics (0.29, 0.26-0.32)), although test orders were higher for Asians (1.48, 1.37-1.61) and Blacks (1.78, 1.73-1.82). Tests ordered (11.42, 10.94-11.92) and completed (2.25, 1.94-2.60) were more likely among those with hepatitis history. Test orders were more likely for HIV-positive patients (3.68, 3.45-3.93), but completion was less likely (0.67, 0.57-0.78). Interventions are needed to increase testing rates so that HCV infections are treated early, mitigating HCV-related morbidity and mortality, especially related to liver cancer.
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Affiliation(s)
- Susan T Vadaparampil
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center, United States of America
| | - Lindsay N Fuzzell
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center, United States of America.
| | - Julie Rathwell
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, United States of America; Center for Immunization and Infection Research in Cancer, H. Lee Moffitt Cancer Center, United States of America
| | - Richard R Reich
- Department of Biostatistics, H. Lee Moffitt Cancer Center, United States of America
| | | | - David R Nelson
- Department of Medicine, University of Florida, United States of America
| | - Erin Kobetz
- Sylvester Comprehensive Cancer Center, Department of Public Health Sciences, University of Miami School of Medicine, Miami, FL, United States of America
| | - Patricia D Jones
- Department of Medicine, Gastroenterology and Hepatology, University of Miami Miller School of Medicine, United States of America
| | - Richard Roetzheim
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center, United States of America; University of South Florida, Department of Family Medicine, United States of America
| | - Anna R Giuliano
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, United States of America; Center for Immunization and Infection Research in Cancer, H. Lee Moffitt Cancer Center, United States of America
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Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing Artificial Intelligence for Clinical Decision-Making. Front Digit Health 2021; 3:645232. [PMID: 34713115 PMCID: PMC8521931 DOI: 10.3389/fdgth.2021.645232] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 06/01/2021] [Indexed: 11/13/2022] Open
Abstract
Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.
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Affiliation(s)
- Chris Giordano
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Meghan Brennan
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Basma Mohamed
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Parisa Rashidi
- J. Clayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - François Modave
- Department of Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States
| | - Patrick Tighe
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States
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Hogan WR, Shenkman EA, Robinson T, Carasquillo O, Robinson PS, Essner RZ, Bian J, Lipori G, Harle C, Magoc T, Manini L, Mendoza T, White S, Loiacono A, Hall J, Nelson D. The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope. J Am Med Inform Assoc 2021; 29:686-693. [PMID: 34664656 PMCID: PMC8922180 DOI: 10.1093/jamia/ocab221] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/03/2021] [Accepted: 09/29/2021] [Indexed: 01/22/2023] Open
Abstract
The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium ("OneFlorida"). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97-0.99, recall 0.75), thereby linking patients' EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network ("PCORnet"), where OneFlorida is 1 of 9 clinical research networks. The Data Trust's robust, centralized, statewide data are a valuable and relatively unique research resource.
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Affiliation(s)
- William R Hogan
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA,Corresponding Author: William R. Hogan, MD, MS, FACMI, Clinical & Translational Research Building, 2004 Mowry Road, PO Box 100219, Gainesville, FL 32610, USA;
| | - Elizabeth A Shenkman
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | | | | | | | | | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | | | - Christopher Harle
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA,UF Health, Gainesville, Florida, USA
| | | | - Lizabeth Manini
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Tona Mendoza
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Sonya White
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Alex Loiacono
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Jackie Hall
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
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Liu Y, Siddiqi KA, Cook RL, Bian J, Squires PJ, Shenkman EA, Prosperi M, Jayaweera DT. Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation. Methods Inf Med 2021; 60:84-94. [PMID: 34592777 PMCID: PMC8672443 DOI: 10.1055/s-0041-1735619] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Electronic health record (EHR)-based computable phenotype algorithms allow researchers to efficiently identify a large virtual cohort of Human Immunodeficiency Virus (HIV) patients. Built upon existing algorithms, we refined, improved, and validated an HIV phenotype algorithm using data from the OneFlorida Data Trust, a repository of linked claims data and EHRs from its clinical partners, which provide care to over 15 million patients across all 67 counties in Florida. METHODS Our computable phenotype examined information from multiple EHR domains, including clinical encounters with diagnoses, prescription medications, and laboratory tests. To identify an HIV case, the algorithm requires the patient to have at least one diagnostic code for HIV and meet one of the following criteria: have 1+ positive HIV laboratory, have been prescribed with HIV medications, or have 3+ visits with HIV diagnostic codes. The computable phenotype was validated against a subset of clinical notes. RESULTS Among the 15+ million patients from OneFlorida, we identified 61,313 patients with confirmed HIV diagnosis. Among them, 8.05% met all four inclusion criteria, 69.7% met the 3+ HIV encounters criteria in addition to having HIV diagnostic code, and 8.1% met all criteria except for having positive laboratories. Our algorithm achieved higher sensitivity (98.9%) and comparable specificity (97.6%) relative to existing algorithms (77-83% sensitivity, 86-100% specificity). The mean age of the sample was 42.7 years, 58% male, and about half were Black African American. Patients' average follow-up period (the time between the first and last encounter in the EHRs) was approximately 4.6 years. The median number of all encounters and HIV-related encounters were 79 and 21, respectively. CONCLUSION By leveraging EHR data from multiple clinical partners and domains, with a considerably diverse population, our algorithm allows more flexible criteria for identifying patients with incomplete laboratory test results and medication prescribing history compared with prior studies.
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Affiliation(s)
- Yiyang Liu
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Khairul A. Siddiqi
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Robert L. Cook
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Patrick J. Squires
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, United States
| | - Elizabeth A. Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Dushyantha T. Jayaweera
- Department of Medicine, Miller School of Medicine, University of Miami, Miami, Florida, United States
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Theis RP, Blackburn K, Lipori G, Harle CA, Alvarado MM, Carek PJ, Zemon N, Howard A, Salloum RG, Shenkman EA. Implementation context for addressing social needs in a learning health system: a qualitative study. J Clin Transl Sci 2021; 5:e201. [PMID: 35047213 PMCID: PMC8727713 DOI: 10.1017/cts.2021.842] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 12/25/2022] Open
Abstract
INTRODUCTION Unmet social needs contribute to growing health disparities and rising health care costs. Strategies to collect and integrate information on social needs into patients' electronic health records (EHRs) show promise for connecting patients with community resources. However, gaps remain in understanding the contextual factors that impact implementing these interventions in clinical settings. METHODS We conducted qualitative interviews with patients and focus groups with providers (January-September 2020) in two primary care clinics to inform the implementation of a module that collects and integrates patient-reported social needs information into the EHR. Questions addressed constructs within the Theoretical Framework for Acceptability and the Consolidated Framework for Implementation Research. Data were coded deductively using team-based framework analysis, followed by inductive coding and matrix analyses. RESULTS Forty patients participated in interviews, with 20 recruited at the clinics and 20 from home. Two focus groups were conducted with a total of 12 providers. Factors salient to acceptability and feasibility included patients' discomfort answering sensitive questions, concerns about privacy, difficulty reading/understanding module content, and technological literacy. Rapport with providers was a facilitator for patients to discuss social needs. Providers stressed that limited time with patients would be a barrier, and expressed concerns about the lack of available community resources. CONCLUSION Findings highlight the need for flexible approaches to assessing and discussing social needs with patients. Feasibility of the intervention is contingent upon support from the health system to facilitate social needs assessment and discussion. Further study of availability of community resources is needed to ensure intervention effectiveness.
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Affiliation(s)
- Ryan P. Theis
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
| | - Katherine Blackburn
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
| | - Gloria Lipori
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
- Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA
| | - Christopher A. Harle
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
| | - Michelle M. Alvarado
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA
| | - Peter J. Carek
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Nadine Zemon
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
| | - Angela Howard
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
| | - Ramzi G. Salloum
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
| | - Elizabeth A. Shenkman
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- Learning Health System Program, Clinical and Translational Science Institute, University of Florida,Gainesville, FL, USA
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Savitz DA, Hu H. Ambient heat and stillbirth in Northern and Central Florida. ENVIRONMENTAL RESEARCH 2021; 199:111262. [PMID: 33974845 PMCID: PMC8638076 DOI: 10.1016/j.envres.2021.111262] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/24/2021] [Accepted: 04/26/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Elevated temperature is well-recognized as a health hazard, and may be particularly harmful to pregnant women, including increasing risk of stillbirth. We conducted a study in Northern and Central Florida, an area prone to periodic extreme heat but with significant seasonal variation, focusing on the most socioeconomically vulnerable populations least able to mitigate the impact of heat. METHODS We obtained electronic health records data from the OneFlorida Data Trust for the period 2012-2017, with 1876 stillbirths included in the analysis. We used a case-crossover design to examine the risk of stillbirth associated with acute exposures to elevated heat prior to the outcome, contrasting the case period (the week preceding the stillbirth) with a control period (the week prior to the case period and the week after the stillbirth). Average heat index and maximum warning level during the case and control periods of each woman were assigned by ZIP code. Conditional logistic regression models were used to assess the association between stillbirth and heat exposure, controlling for PM2.5 and O3. RESULTS The adjusted odds ratio showed no overall association with stillbirth except for a weak association for exposure above the 90th percentile which was larger among the most socioeconomically deprived and non-Hispanic Black women. In the hot months, there was a clear association for all indices of heat exposure, but largest again for the most socioeconomically deprived population (aOR = 2.4, 95% CI: 1.2-5.2 in the 4th vs. 1st quartile) and among non-Hispanic Black women (aOR = 1.8, 95% CI: 1.0-3.2 in the 4th vs. 1st quartile). CONCLUSIONS Our results provide further evidence that elevated ambient heat is related to stillbirth and encourage a focus on the most susceptible individuals and possible clinical pathways.
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Affiliation(s)
- David A Savitz
- Department of Epidemiology Brown University School of Public Health Providence, RI, USA.
| | - Hui Hu
- Department of Epidemiology College of Public Health and Health Professions & College of Medicine University of Florida Gainesville, FL, USA
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Testing the Use of Data Drawn from the Electronic Health Record to Compare Quality. Pediatr Qual Saf 2021; 6:e432. [PMID: 34345748 PMCID: PMC8322494 DOI: 10.1097/pq9.0000000000000432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 01/10/2021] [Indexed: 11/01/2022] Open
Abstract
Introduction Health systems spend $1.5 billion annually reporting data on quality, but efficacy and utility for benchmarking are limited due, in part, to limitations of data sources. Our objective was to implement and evaluate measures of pediatric quality for three conditions using electronic health record (EHR)-derived data. Methods PCORnet networks standardized EHR-derived data to a common data model. In 13 health systems from 2 networks for 2015, we implemented the National Quality Forum measures: % children with sickle cell anemia who received a transcranial Doppler; % children on antipsychotics who had metabolic screening; and % pediatric acute otitis media with amoxicillin prescribed. Manual chart review assessed measure accuracy. Results Only 39% (N = 2,923) of 7,278 children on antipsychotics received metabolic screening (range: 20%-54%). If the measure indicated screening was performed, the chart agreed 88% of the time [95% confidence interval (CI): 81%-94%]; if it indicated screening was not done, the chart agreed 86% (95% CI: 78%-93%). Only 69% (N = 793) of 1,144 children received transcranial Doppler screening (range across sites: 49%-88%). If the measure indicated screening was performed, the chart agreed 98% of the time (95% CI: 94%-100%); if it indicated screening was not performed, the chart agreed 89% (95% CI: 82%-95%). For acute otitis media, chart review identified many qualifying cases missed by the National Quality Forum measure, which excluded a common diagnostic code. Conclusions Measures of healthcare quality developed using EHR-derived data were valid and identified wide variation among network sites. This data can facilitate the identification and spread of best practices.
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Marra DE, Busl KM, Robinson CP, Bruzzone MJ, Miller AH, Chen Z, Guo Y, Lyu T, Bian J, Smith GE. Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series. Crit Care Explor 2021; 3:e0456. [PMID: 34136827 PMCID: PMC8202548 DOI: 10.1097/cce.0000000000000456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
To determine if early CNS symptoms are associated with severe coronavirus disease 2019. DESIGN A retrospective, observational case series study design. SETTING Electronic health records were reviewed for patients from five healthcare systems across the state of Florida, United States. PATIENTS A clinical sample (n = 36,615) of patients with confirmed diagnosis of coronavirus disease 2019 were included. Twelve percent (n = 4,417) of the sample developed severe coronavirus disease 2019, defined as requiring critical care, mechanical ventilation, or diagnosis of acute respiratory distress syndrome, sepsis, or severe inflammatory response syndrome. INTERVENTIONS None. MEASUREMENT AND MAIN RESULTS We reviewed the electronic health record for diagnosis of early CNS symptoms (encephalopathy, headache, ageusia, anosmia, dizziness, acute cerebrovascular disease) between 14 days before the diagnosis of coronavirus disease 2019 and 8 days after the diagnosis of coronavirus disease 2019, or before the date of severe coronavirus disease 2019 diagnosis, whichever came first. Hierarchal logistic regression models were used to examine the odds of developing severe coronavirus disease 2019 based on diagnosis of early CNS symptoms. Severe coronavirus disease 2019 patients were significantly more likely to have early CNS symptoms (32.8%) compared with nonsevere patients (6.11%; χ2[1] = 3,266.08, p < 0.0001, φ = 0.29). After adjusting for demographic variables and pertinent comorbidities, early CNS symptoms were significantly associated with severe coronavirus disease 2019 (odds ratio = 3.21). Diagnosis of encephalopathy (odds ratio = 14.38) was associated with greater odds of severe coronavirus disease 2019; whereas diagnosis of anosmia (odds ratio = 0.45), ageusia (odds ratio = 0.46), and headache (odds ratio = 0.63) were associated with reduced odds of severe coronavirus disease 2019. CONCLUSIONS Early CNS symptoms, and specifically encephalopathy, are differentially associated with risk of severe coronavirus disease 2019 and may serve as an early marker for differences in clinical disease course. Therapies for early coronavirus disease 2019 are scarce, and further identification of subgroups at risk may help to advance understanding of the severity trajectories and enable focused treatment.
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Affiliation(s)
- David E. Marra
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL
| | - Katharina M. Busl
- Department of Neurology, UF College of Medicine, University of Florida, Gainesville, FL
- Department of Neurosurgery, UF College of Medicine, University of Florida, Gainesville, FL
| | - Christopher P. Robinson
- Department of Neurology, UF College of Medicine, University of Florida, Gainesville, FL
- Department of Neurosurgery, UF College of Medicine, University of Florida, Gainesville, FL
| | - Maria J. Bruzzone
- Department of Neurology, UF College of Medicine, University of Florida, Gainesville, FL
| | - Amber H. Miller
- Department of Neurology, UF College of Medicine, University of Florida, Gainesville, FL
| | - Zhaoyi Chen
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL
| | - Glenn E. Smith
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL
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