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Azhir A, Hügel J, Tian J, Cheng J, Bassett IV, Bell DS, Bernstam EV, Farhat MR, Henderson DW, Lau ES, Morris M, Semenov YR, Triant VA, Visweswaran S, Strasser ZH, Klann JG, Murphy SN, Estiri H. Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion. medRxiv 2024:2024.04.13.24305771. [PMID: 38699316 PMCID: PMC11065031 DOI: 10.1101/2024.04.13.24305771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.
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Hutch MR, Son J, Le TT, Hong C, Wang X, Shakeri Hossein Abad Z, Morris M, Gutiérrez-Sacristán A, Klann JG, Spiridou A, Batugo A, Bellazzi R, Benoit V, Bonzel CL, Bryant WA, Chiudinelli L, Cho K, Das P, González González T, Hanauer DA, Henderson DW, Ho YL, Loh NHW, Makoudjou A, Makwana S, Malovini A, Moal B, Mowery DL, Neuraz A, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Talbert J, Tan ALM, Tan BWL, Tan BWQ, Tibollo V, Tippman P, Verdy G, Yuan W, Avillach P, Gehlenborg N, Omenn GS, Visweswaran S, Cai T, Luo Y, Xia Z. Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study. PLOS Digit Health 2024; 3:e0000484. [PMID: 38620037 PMCID: PMC11018281 DOI: 10.1371/journal.pdig.0000484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 03/06/2024] [Indexed: 04/17/2024]
Abstract
Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.
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Affiliation(s)
- Meghan R. Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Jiyeon Son
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Trang T. Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Chuan Hong
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States of America
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Xuan Wang
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Zahra Shakeri Hossein Abad
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Alba Gutiérrez-Sacristán
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Anastasia Spiridou
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Ashley Batugo
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Vincent Benoit
- IT Department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - William A. Bryant
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Lorenzo Chiudinelli
- UOC Ricerca, Innovazione e Brand reputation, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Kelly Cho
- Population Health and Data Science, VA Boston Healthcare System, Boston Massachusetts, United States of America
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston Massachusetts, United States of America
| | - Priyam Das
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Darren W. Henderson
- Center for Clinical and Translational Science, University of Kentucky, Lexington, Kentucky, United States of America
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston Massachusetts, United States of America
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Kent Ridge, Singapore
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Simran Makwana
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bertrand Moal
- IAM Unit, Bordeaux University Hospital, Bordeaux, France
| | - Danielle L. Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | | | - Fernando J. Sanz Vidorreta
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
| | - Emily R. Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States of America
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston Massachusetts, United States of America
| | - Jeffery Talbert
- Division of Biomedical Informatics, University of Kentucky, Lexington, Kentucky, United States of America
| | - Amelia L. M. Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Byorn W. L. Tan
- Department of Medicine, National University Hospital, Singapore, Kent Ridge, Singapore
| | - Bryce W. Q. Tan
- Department of Medicine, National University Hospital, Singapore, Kent Ridge, Singapore
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Patric Tippman
- Institute of Medical Biometry and University of Freiburg, Medical Center, Freiburg, Germany
| | | | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Gilbert S. Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | | | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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Klann JG, Henderson DW, Morris M, Estiri H, Weber GM, Visweswaran S, Murphy SN. A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation. J Am Med Inform Assoc 2023; 30:1985-1994. [PMID: 37632234 PMCID: PMC10654861 DOI: 10.1093/jamia/ocad166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 07/25/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
OBJECTIVE Patients who receive most care within a single healthcare system (colloquially called a "loyalty cohort" since they typically return to the same providers) have mostly complete data within that organization's electronic health record (EHR). Loyalty cohorts have low data missingness, which can unintentionally bias research results. Using proxies of routine care and healthcare utilization metrics, we compute a per-patient score that identifies a loyalty cohort. MATERIALS AND METHODS We implemented a computable program for the widely adopted i2b2 platform that identifies loyalty cohorts in EHRs based on a machine-learning model, which was previously validated using linked claims data. We developed a novel validation approach, which tests, using only EHR data, whether patients returned to the same healthcare system after the training period. We evaluated these tools at 3 institutions using data from 2017 to 2019. RESULTS Loyalty cohort calculations to identify patients who returned during a 1-year follow-up yielded a mean area under the receiver operating characteristic curve of 0.77 using the original model and 0.80 after calibrating the model at individual sites. Factors such as multiple medications or visits contributed significantly at all sites. Screening tests' contributions (eg, colonoscopy) varied across sites, likely due to coding and population differences. DISCUSSION This open-source implementation of a "loyalty score" algorithm had good predictive power. Enriching research cohorts by utilizing these low-missingness patients is a way to obtain the data completeness necessary for accurate causal analysis. CONCLUSION i2b2 sites can use this approach to select cohorts with mostly complete EHR data.
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Affiliation(s)
- Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
| | - Darren W Henderson
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY 40506, United States
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
| | - Griffin M Weber
- Beth Israel Deaconess Medical Center, Boston, MA 02115, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States
- Research Information Science and Computing, Mass General Brigham, Somerville, MA 02145, United States
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4
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Dagliati A, Strasser ZH, Hossein Abad ZS, Klann JG, Wagholikar KB, Mesa R, Visweswaran S, Morris M, Luo Y, Henderson DW, Samayamuthu MJ, Tan BW, Verdy G, Omenn GS, Xia Z, Bellazzi R, Murphy SN, Holmes JH, Estiri H. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study. EClinicalMedicine 2023; 64:102210. [PMID: 37745021 PMCID: PMC10511779 DOI: 10.1016/j.eclinm.2023.102210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/26/2023] Open
Abstract
Background Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.
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Affiliation(s)
- Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Zachary H. Strasser
- Department of Medicine, Massachusetts General Hospital, Boston, United States
| | | | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, Boston, United States
| | | | - Rebecca Mesa
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, United States
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, United States
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, United States
| | - Darren W. Henderson
- University of Kentucky, Center for Clinical and Translational Science, Lexington, United States
| | | | - Bryce W.Q. Tan
- National University Hospital, Singapore Department of Medicine, Singapore
| | - Guillame Verdy
- Bordeaux University Hospital, IAM Unit, Bordeaux, France
| | - Gilbert S. Omenn
- University of Michigan, Department of Computational Medicine and Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, Ann Arbor, United States
| | - Zongqi Xia
- University of Pittsburgh Department of Neurology, Pittsburgh, United States
| | - Riccardo Bellazzi
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Shawn N. Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, United States
| | - John H. Holmes
- University of Pennsylvania Perelman School of Medicine, Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, Philadelphia, United States
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, United States
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5
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Strasser ZH, Dagliati A, Shakeri Hossein Abad Z, Klann JG, Wagholikar KB, Mesa R, Visweswaran S, Morris M, Luo Y, Henderson DW, Samayamuthu MJ, Omenn GS, Xia Z, Holmes JH, Estiri H, Murphy SN. A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework. PLOS Digit Health 2023; 2:e0000301. [PMID: 37490472 PMCID: PMC10368277 DOI: 10.1371/journal.pdig.0000301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/16/2023] [Indexed: 07/27/2023]
Abstract
Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.
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Affiliation(s)
- Zachary H. Strasser
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Zahra Shakeri Hossein Abad
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Kavishwar B. Wagholikar
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Rebecca Mesa
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Darren W. Henderson
- Center for Clinical and Translation Science, University of Kentucky, Lexington, Kentucky, United States of America
| | | | | | - Gilbert S. Omenn
- Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - John H. Holmes
- Department of Biostatistics, Epidemiology, and Informatics; Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Shawn N. Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
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6
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Weber GM, Zhang HG, L'Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan ALM, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, Liu M, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Martins MR, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera Jiménez M, Prudente RA, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, Kohane IS, Cai T, South AM, Brat GA. Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study. J Med Internet Res 2021; 23:e34625. [PMID: 34889759 PMCID: PMC8672293 DOI: 10.2196/34625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/10/2021] [Indexed: 11/15/2022] Open
Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Anna Alloni
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Danilo F Amendola
- Clinical Research Unit, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Antonio Bellasi
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Michele Beraghi
- Information Technology Department, Azienda Socio-Sanitaria Territoriale di Pavia, Pavia, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Darren W Henderson
- Department of Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Katie Kirchoff
- Medical University of South Carolina, Charleston, SC, United States
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Ashok K Krishnamurthy
- Department of Computer Science, Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Sarah Maidlow
- Michigan Institute for Clinical & Health Research Informatics, University of Michigan, Ann Arbor, MI, United States
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | | | - Bertrand Moal
- Informatique et archivistique médicales unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics, Institute for Digital Medicine, National University Health System, Singapore, Singapore
| | - Marina P Okoshi
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Lav P Patel
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Robson A Prudente
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | | | - Fernando J Sanz Vidorreta
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | | | - Byorn Wl Tan
- Department of Medicine, National University Health System, Singapore, Singapore
| | - Suzana E Tanni
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | -
- see Authors' Contributions,
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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7
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Weber GM, Zhang HG, L'Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan ALM, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, Liu M, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Martins MR, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera Jiménez M, Prudente RA, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, Kohane IS, Cai T, South AM, Brat GA. International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study. J Med Internet Res 2021; 23:e31400. [PMID: 34533459 PMCID: PMC8510151 DOI: 10.2196/31400] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/02/2021] [Accepted: 09/02/2021] [Indexed: 02/06/2023] Open
Abstract
Background Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. Objective In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. Methods Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. Results Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. Conclusions Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.
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Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Anna Alloni
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Danilo F Amendola
- Clinical Research Unit, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Antonio Bellasi
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Michele Beraghi
- Information Technology Department, Azienda Socio-Sanitaria Territoriale di Pavia, Pavia, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Darren W Henderson
- Department of Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Katie Kirchoff
- Medical University of South Carolina, Charleston, SC, United States
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Ashok K Krishnamurthy
- Department of Computer Science, Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Sarah Maidlow
- Michigan Institute for Clinical & Health Research Informatics, University of Michigan, Ann Arbor, MI, United States
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | | | - Bertrand Moal
- Informatique et archivistique médicales unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics, Institute for Digital Medicine, National University Health System, Singapore, Singapore
| | - Marina P Okoshi
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Lav P Patel
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Robson A Prudente
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | | | - Fernando J Sanz Vidorreta
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | | | - Byorn Wl Tan
- Department of Medicine, National University Health System, Singapore, Singapore
| | - Suzana E Tanni
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | -
- see Authors' Contributions,
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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8
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Harris DR, Henderson DW, Corbeau A. Improving the Utility of Tobacco-Related Problem List Entries Using Natural Language Processing. AMIA Annu Symp Proc 2021; 2020:534-543. [PMID: 33936427 PMCID: PMC8075422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We present findings on using natural language processing to classify tobacco-related entries from problem lists found within patient's electronic health records. Problem lists describe health-related issues recorded during a patient's medical visit; these problems are typically followed up upon during subsequent visits and are updated for relevance or accuracy. The mechanics of problem lists vary across different electronic health record systems. In general, they either manifest as pre-generated generic problems that may be selected from a master list or as text boxes where a healthcare professional may enter free text describing the problem. Using commonly-available natural language processing tools, we classified tobacco-related problems into three classes: active-user, former-user, and non-user; we further demonstrate that rule-based post-processing may significantly increase precision in identifying these classes (+32%, +22%, +35% respectively). We used these classes to generate tobacco time-spans that reconstruct a patient's tobacco-use history and better support secondary data analysis. We bundle this as an open-source toolkit with flow visualizations indicating how patient tobacco-related behavior changes longitudinally, which can also capture and visualize contradicting information such as smokers being flagged as having never smoked.
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Affiliation(s)
- Daniel R Harris
- Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky, Lexington, Kentucky 40506
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY 40506
| | - Darren W Henderson
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY 40506
| | - Alexandria Corbeau
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY 40506
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9
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Harris DR, Henderson DW, Corbeau A. sig2db: a Workflow for Processing Natural Language from Prescription Instructions for Clinical Data Warehouses. AMIA Jt Summits Transl Sci Proc 2020; 2020:221-230. [PMID: 32477641 PMCID: PMC7233058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We present sig2db as an open-source solution for clinical data warehouses desiring to process natural language from prescription instructions, often referred to as "sigs". In electronic prescribing, the sig is typically an unstructured text field intended to capture all requirements for medication administration. The sig captures certain fields that the structured data may lack such as days supply, time of day, or meal-time considerations. Our open-source software package facilitates the workflow needed to process sigs into a structured format usable by clinical data warehouses. Our solution focuses on extracting concepts from prescriptions in order to understand the intended semantics by leveraging known natural language processing tools. We demonstrate the utility of concept extraction from sigs and present our findings in processing 1023 unique sigs from 5.7 million unique prescriptions.
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Affiliation(s)
- Daniel R Harris
- Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky, Lexington, Kentucky 40506
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY 40506
| | - Darren W Henderson
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY 40506
| | - Alexandria Corbeau
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY 40506
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10
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Fouladvand S, Hankosky ER, Bush H, Chen J, Dwoskin LP, Freeman PR, Henderson DW, Kantak K, Talbert J, Tao S, Zhang GQ. Predicting substance use disorder using long-term attention deficit hyperactivity disorder medication records in Truven. Health Informatics J 2019; 26:787-802. [PMID: 31106686 DOI: 10.1177/1460458219844075] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
About 20% of individuals with attention deficit hyperactivity disorder are first diagnosed during adolescence. While preclinical experiments suggest that adolescent-onset exposure to attention deficit hyperactivity disorder medication is an important factor in the development of substance use disorder phenotypes in adulthood, the long-term impact of attention deficit hyperactivity disorder medication initiated during adolescence has been largely unexplored in humans. Our analysis of 11,624 adolescent enrollees with attention deficit hyperactivity disorder in the Truven database indicates that temporal medication features, rather than stationary features, are the most important factors on the health consequences related to substance use disorder and attention deficit hyperactivity disorder medication initiation during adolescence.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Guo-Qiang Zhang
- University of Kentucky, USA; The University of Texas Health Science Center at Houston, USA
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11
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Hankosky ER, Bush HM, Dwoskin LP, Harris DR, Henderson DW, Zhang GQ, Freeman PR, Talbert JC. Retrospective analysis of health claims to evaluate pharmacotherapies with potential for repurposing: Association of bupropion and stimulant use disorder remission. AMIA Annu Symp Proc 2018; 2018:1292-1299. [PMID: 30815171 PMCID: PMC6371318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Drug repurposing is the identification of novel indication(s) for existing medications. Health claims data provide a burgeoning resource to evaluate pharmacotherapies with repurposing potential. To demonstrate a workflow for drug repurposing using claims data, we assessed the association between prescription of bupropion and stimulant use disorder (StUD) remission. Using the Truven Marketscan database, 96,156 individuals with a StUD were identified. Logistic regression was used to model the association between new bupropion prescriptions and remission while controlling for age, sex, region, StUD severity, antidepressant co-prescriptions, and comorbid mood and attention disorders. Prescription of bupropion within 30 days offirst documented StUD diagnosis increased odds of a subsequent remission diagnosis by 2.1 times (99% confidence interval: 1.09-3.89) in individuals with an amphetamine use disorder, but not those with a cocaine use disorder. This work provides a framework for reverse-translational drug repurposing, which may be applied to many other medical conditions.
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12
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Fouladvand S, Hankosky ER, Henderson DW, Bush H, Chen J, Dwoskin LP, Freeman PR, Kantak K, Talbert J, Tao S, Zhang GQ. Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model. 2018 IEEE Int Conf Healthc Inform Workshop (2018) 2018; 2018:49-50. [PMID: 31380010 PMCID: PMC6677134 DOI: 10.1109/ichi-w.2018.00014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
About 20% of individuals with attention deficit hyperactivity disorder (ADHD) are first diagnosed during adolescence. While preclinical experiments suggest that adolescent-onset exposure to ADHD medication is an important factor in the development of substance use disorder (SUD) phenotypes in adulthood, the long-term impact of ADHD medication initiated during adolescence has been largely unexplored in humans. Our analysis of 11,624 adolescent ADHD patients in the Truven database indicates that temporal medication features are the important factors on the health consequences related to SUD and ADHD medication initiation during adolescence.
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Affiliation(s)
- Sajjad Fouladvand
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY
- Department of Computer Science, University of Kentucky, Lexington, KY
| | - Emily R Hankosky
- Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY
| | - Darren W Henderson
- Department of Pharmacy Practice and Science, University of Kentucky, Lexington, KY
| | - Heather Bush
- Department of Biostatistics, University of Kentucky, Lexington, KY
| | - Jin Chen
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY
| | - Linda P Dwoskin
- Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY
| | - Patricia R Freeman
- Department of Pharmacy Practice and Science, University of Kentucky, Lexington, KY
| | - Kathleen Kantak
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
| | - Jeffery Talbert
- Department of Pharmacy Practice and Science, University of Kentucky, Lexington, KY
| | - Shiqiang Tao
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY
| | - Guo-Qiang Zhang
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY
- Department of Computer Science, University of Kentucky, Lexington, KY
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13
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Harris DR, Henderson DW, Talbert JC. Using Closure Tables to Enable Cross-Querying of Ontologies in Database-Driven Applications. IEEE EMBS Int Conf Biomed Health Inform 2017; 2017:493-496. [PMID: 28725879 PMCID: PMC5512279 DOI: 10.1109/bhi.2017.7897313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We demonstrate that closure tables are an effective data structure for developing database-driven applications that query biomedical ontologies and that require cross-querying between multiple ontologies. A closure table stores all available paths within a tree, even those without a direct parent-child relationship; additionally, a node can have multiple ancestors which gives the foundation for supporting linkages between controlled ontologies. We augment the meta-data structure of the ICD9 and ICD10 ontologies included in i2b2, an open source query tool for identifying patient cohorts, to utilize a closure table. We describe our experiences in incorporating existing mappings between ontologies to enable clinical and health researchers to identify patient populations using the ontology that best matches their preference and expertise.
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Affiliation(s)
- Daniel R Harris
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
| | - Darren W Henderson
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
| | - Jeffery C Talbert
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
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14
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Harris DR, Harper TJ, Henderson DW, Henry KW, Talbert JC. Informatics-based Challenges of Building Collaborative Healthcare Research and Analysis Networks from Rural Community Health Centers. IEEE EMBS Int Conf Biomed Health Inform 2017; 2016:513-516. [PMID: 28133639 DOI: 10.1109/bhi.2016.7455947] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We discuss informatics-based challenges of constructing large-scale collaborative networks for healthcare research and analysis from rural community health centers. These types of networks provide data access and analytic insights across multiple heterogeneous health centers for both healthcare professionals and biomedical researchers. Challenges fall into three general categories: data access, data integration, and technical infrastructure. Data access issues arise in balancing patient privacy, security, and utility; data integration issues persist from each site independently operating its desired electronic medical record; technical infrastructure challenges include creating an analysis and reporting hub capable of scaling across a large collaborative network. Other challenges, such as the difficulty of site recruitment, are important to discuss, but cannot be solved directly through informatics alone. We discuss these challenges and their potential solutions in the context of our implementation of the Kentucky Diabetes and Obesity Collaborative (KDOC). KDOC is a network of Federally-Qualified Community Health Centers (FQHCs) that established a collaborative infrastructure for research and analysis of obesity and diabetes in rural and under-served communities.
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Affiliation(s)
- Daniel R Harris
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
| | - Tamela J Harper
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
| | - Darren W Henderson
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
| | - Keith W Henry
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
| | - Jeffery C Talbert
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
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15
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Harris DR, Henderson DW. i2b2t2: Unlocking Visualization for Clinical Research. AMIA Jt Summits Transl Sci Proc 2016; 2016:98-104. [PMID: 27570658 PMCID: PMC5001764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We introduce a tool that extracts clinical data sets and provides visualizations from clinical data warehouses that use the Informatics for Integrating Biology and the Bedside (i2b2) query tool. Our tool, i2b2t2 (i2b2 to Tableau), can extract and visualize any i2b2 query into a portable format that researchers can easily explore without needing a highly technical or statistical background. This user-friendly format provides a quick visual summary of the queried population and is easily extendable to develop more intricate and robust visualizations. Extraction and visualization can be provided as a service by clinical data warehouses to expedite the release of data sets for research. i2b2t2 also encourages visualization as a self-service; a motivated researcher can develop custom visualizations for exploration or publication.
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16
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Harris DR, Henderson DW, Kavuluru R, Stromberg AJ, Johnson TR. Using common table expressions to build a scalable Boolean query generator for clinical data warehouses. IEEE J Biomed Health Inform 2014; 18:1607-13. [PMID: 25192572 DOI: 10.1109/jbhi.2013.2292591] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We present a custom, Boolean query generator utilizing common-table expressions (CTEs) that is capable of scaling with big datasets. The generator maps user-defined Boolean queries, such as those interactively created in clinical-research and general-purpose healthcare tools, into SQL. We demonstrate the effectiveness of this generator by integrating our study into the Informatics for Integrating Biology and the Bedside (i2b2) query tool and show that it is capable of scaling. Our custom generator replaces and outperforms the default query generator found within the Clinical Research Chart cell of i2b2. In our experiments, 16 different types of i2b2 queries were identified by varying four constraints: date, frequency, exclusion criteria, and whether selected concepts occurred in the same encounter. We generated nontrivial, random Boolean queries based on these 16 types; the corresponding SQL queries produced by both generators were compared by execution times. The CTE-based solution significantly outperformed the default query generator and provided a much more consistent response time across all query types (M = 2.03, SD = 6.64 versus M = 75.82, SD = 238.88 s). Without costly hardware upgrades, we provide a scalable solution based on CTEs with very promising empirical results centered on performance gains. The evaluation methodology used for this provides a means of profiling clinical data warehouse performance.
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Sachdev A, Barbara JAJ, Au V, Henderson DW, Bowden JJ. Symptomatic metastatic pulmonary calcification in a renal transplant recipient. Intern Med J 2013; 43:1046-7. [PMID: 24004396 DOI: 10.1111/imj.12243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2013] [Accepted: 07/07/2013] [Indexed: 11/28/2022]
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18
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Abstract
A simple method was developed for measuring extensive intact leaves of monocots on a minute-by-minute basis. Growth was markedly reduced by a slight reduction in leaf water potential. When plants mildly deficient in water were irrigated, growth resumed virtually instantly. The transitional rapid growth aftr watering suggests that water deficit increased cell extensibility.
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19
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Carlson BA, Mushinski JF, Henderson DW, Kwon SY, Crain PF, Lee BJ, Hatfield DL. 1-Methylguanosine in place of Y base at position 37 in phenylalanine tRNA is responsible for its shiftiness in retroviral ribosomal frameshifting. Virology 2001; 279:130-5. [PMID: 11145896 DOI: 10.1006/viro.2000.0692] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Many mammalian retroviruses express their protease and polymerase by ribosomal frameshifting. It was originally proposed that a specialized shifty tRNA promotes the frameshift event. We previously observed that phenylalanine tRNA(Phe) lacking the highly modified wybutoxosine (Y) base on the 3' side of its anticodon stimulated frameshifting, demonstrating that this tRNA is shifty. We now report the shifty tRNA(Phe) contains 1-methylguanosine (m(1)G) in place of Y and that the m(1)G form from rabbit reticulocytes stimulates frameshifting more efficiently than its m(1)G-containing counterpart from mouse neuroblastoma cells. The latter tRNA contains unmodified C and G nucleosides at positions 32 and 34, respectively, while the former tRNA contains the analogous 2'-O-methylated nucleosides at these positions. The data suggest that not only does the loss of a highly modified base from the 3' side of the anticodon render tRNA(Phe) shifty, but the modification status of the entire anticodon loop contributes to the degree of shiftiness. Possible biological consequences of these findings are discussed.
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Affiliation(s)
- B A Carlson
- Section on the Molecular Biology of Selenium, National Cancer Institute, Bethesda, Maryland, 20892, USA
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20
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Kuschak TI, Taylor C, McMillan-Ward E, Israels S, Henderson DW, Mushinski JF, Wright JA, Mai S. The ribonucleotide reductase R2 gene is a non-transcribed target of c-Myc-induced genomic instability. Gene 1999; 238:351-65. [PMID: 10570963 DOI: 10.1016/s0378-1119(99)00341-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The c-Myc oncoprotein is highly expressed in malignant cells of many cell types, but the mechanism by which it contributes to the transformation process is not fully understood. Here, we show for the first time that constitutive or activated overexpression of the c-myc gene in cultured mouse B lymphocytes is followed by chromosomal and extrachromosomal amplification as well as rearrangement of the ribonucleotide reductase R2 gene locus. Electron micrographs and fluorescent in situ hybridization (FISH) demonstrate the c-Myc-dependent generation of extrachromosomal elements, some of which contain R2 sequences. However, unlike other genes that have been shown to be targets of c-Myc-dependent genomic instability, amplification of the R2 gene is not associated with alterations in R2 mRNA or protein expression. These data suggest that c-Myc-dependent genomic instability involves a greater number of genes than previously anticipated, but not all of the genes that are amplified in this system are transcriptionally upregulated.
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Affiliation(s)
- T I Kuschak
- Manitoba Institute of Cell Biology and the Manitoba Cancer Treatment and Research Foundation, Winnipeg, Canada
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21
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Mai S, Hanley-Hyde J, Rainey GJ, Kuschak TI, Paul JT, Littlewood TD, Mischak H, Stevens LM, Henderson DW, Mushinski JF. Chromosomal and extrachromosomal instability of the cyclin D2 gene is induced by Myc overexpression. Neoplasia 1999; 1:241-52. [PMID: 10935479 PMCID: PMC1508077 DOI: 10.1038/sj.neo.7900030] [Citation(s) in RCA: 37] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/1999] [Accepted: 05/25/1999] [Indexed: 11/08/2022]
Abstract
We examined the expression of cyclins D1, D2, D3, and E in mouse B-lymphocytic tumors. Cyclin D2 mRNA was consistently elevated in plasmacytomas, which characteristically contain Myc-activating chromosome translocations and constitutive c-Myc mRNA and protein expression. We examined the nature of cyclin D2 overexpression in plasmacytomas and other tumors. Human and mouse tumor cell lines that exhibited c-Myc dysregulation displayed instability of the cyclin D2 gene, detected by Southern blot, fluorescent in situ hybridization (FISH), and in extrachromosomal preparations (Hirt extracts). Cyclin D2 instability was not seen in cells with low levels of c-Myc protein. To unequivocally demonstrate a role of c-Myc in the instability of the cyclin D2 gene, a Myc-estrogen receptor chimera was activated in two mouse cell lines. After 3 to 4 days of Myc-ER activation, instability at the cyclin D2 locus was seen in the form of extrachromosomal elements, determined by FISH of metaphase and interphase nuclei and of purified extrachromosomal elements. At the same time points, Northern and Western blot analyses detected increased cyclin D2 mRNA and protein levels. These data suggest that Myc-induced genomic instability may contribute to neoplasia by increasing the levels of a cell cycle-regulating protein, cyclin D2, via intrachromosomal amplification of its gene or generation of extrachromosomal copies.
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Affiliation(s)
- S Mai
- Manitoba Institute of Cell Biology, University of Manitoba, Winnipeg, Canada
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22
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Mushinski JF, Hanley-Hyde J, Rainey GJ, Kuschak TI, Taylor C, Fluri M, Stevens LM, Henderson DW, Mai S. Myc-induced cyclin D2 genomic instability in murine B cell neoplasms. Curr Top Microbiol Immunol 1999; 246:183-9; discussion 190-2. [PMID: 10396055 DOI: 10.1007/978-3-642-60162-0_23] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
Affiliation(s)
- J F Mushinski
- Molecular Genetics Section, National Cancer Institute, Bethesda, MD 20892-4255, USA
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23
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Henderson DW, Shilkin KB, Whitaker D. Reactive mesothelial hyperplasia vs mesothelioma, including mesothelioma in situ: a brief review. Am J Clin Pathol 1998; 110:397-404. [PMID: 9728617 DOI: 10.1093/ajcp/110.3.397] [Citation(s) in RCA: 103] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In biopsy tissue, discrimination between reactive mesothelial hyperplasia and epithelial mesothelioma can pose a major problem for the surgical pathologist. Confidence in the diagnosis is often proportional to the amount of tissue available for study and depends largely on findings of invasion and the extent and cytologic atypia of the lesion, because there is no marker specific for the mesothelium and that discriminates consistently among normal, hyperplastic, and neoplastic mesothelial tissue. Therefore, mesothelioma in situ is diagnosable only when invasive epithelial mesothelioma is demonstrable in the same specimen, in a follow-up biopsy specimen, or at autopsy. Comparison of 22 cases of mesothelioma in situ that fulfill these requirements for diagnosis with 141 invasive mesotheliomas and 78 reactive mesothelioses indicates that strong linear membrane-related labeling for epithelial membrane antigen and silver-labeled nucleolar organizer region-positive material that occupies 0.6677 microm2 or more of the nucleus in an atypical in situ mesothelial lesion of the pleura are found consistently in neoplastic mesothelial cells. Although these findings may engender suspicion of mesothelioma in situ in high-risk persons, the criteria for diagnosis of pure mesothelial lesions of this type are still under study. Mesothelioma in situ should be considered proved only when unequivocal invasion is identified in a different area of the pleura or at a different time; a diagnosis of pure mesothelioma in situ should not be made in patients not exposed to asbestos.
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Affiliation(s)
- D W Henderson
- Department of Anatomical Pathology, Flinders Medical Centre, Adelaide South Australia, Australia
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24
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Maucher C, Weissinger EM, Kremmer E, Baccarini M, Procyk K, Henderson DW, Wolff L, Kolch W, Kaspers B, Mushinski JF, Mischak H. Activation of bcl-2 suppressible 40 and 44 kDa p38-like kinases during apoptosis of early and late B lymphocytic cell lines. FEBS Lett 1998; 427:29-35. [PMID: 9613594 DOI: 10.1016/s0014-5793(98)00387-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Activation of several different kinases characterizes the induction of apoptosis. Abelson virus transformed pre-B lymphocytes undergo apoptosis within 24 h of serum deprivation, PKA activation or gamma-irradiation, and the activity of two kinases of ca. 40 and 44 kDa is specifically induced during this apoptotic process. Bcl-2 expression prevents both apoptosis and the induction of these kinases. Immunologic and substrate similarities indicate that these kinases are related to the p38 family of MAP kinases. More mature cells of the B lymphocytic lineage, plasmacytomas, also exhibit induction of these kinases when apoptosis is induced by withdrawal of serum or IL-6. Treatment of the pre-B cells with ICE protease inhibitors when apoptotic stimuli are delivered prevents induction of the kinase activity, and partially inhibits apoptosis. These findings indicate that the induction of these 40 and 44 kDa p38 related kinases is a common feature of apoptosis in mouse B lymphocytic cells and may represent a step downstream of ICE proteases in the signal cascade that leads to programmed cell death.
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Affiliation(s)
- C Maucher
- Institut für Klinische Molekularbiologie und Tumorgenetik, GSF, Munich, Germany.
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25
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Wolanski KD, Whitaker D, Shilkin KB, Henderson DW. The use of epithelial membrane antigen and silver-stained nucleolar organizer regions testing in the differential diagnosis of mesothelioma from benign reactive mesothelioses. Cancer 1998; 82:583-90. [PMID: 9452278 DOI: 10.1002/(sici)1097-0142(19980201)82:3<583::aid-cncr22>3.0.co;2-x] [Citation(s) in RCA: 50] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND The accurate diagnosis of pleural lesions obtained from small closed biopsy is difficult. As yet there is no single reliable test to distinguish between malignant and benign mesothelial tissue. METHODS Immunostaining of epithelial membrane antigen (EMA) and the quantitation of silver stained nucleolar organizer regions (AgNORs) each were applied to benign and malignant histologic sections of pleural and peritoneal biopsies. The usefulness of these stains was tested both individually and in combination in the diagnosis of epithelial malignant mesothelioma. RESULTS One hundred and three of the 141 malignant lesions (73%) were immunoreactive for EMA but only 3 of the 73 benign lesions (4%) reacted equivocally, and none positively. The average count of AgNORs/cell in malignant lesions (n = 80) was elevated compared with benign cases (n = 26), but a significant overlap was exhibited in the AgNOR count and this form of analysis was considered to be of little value in distinguishing benign from malignant mesothelial processes. Much less overlap was observed when the average AgNOR area was measured. By using the maximum benign AgNOR area of 0.6677 microm2 as the upper threshold, 51 cases (63.8%) were identified as malignant; the test demonstrated 100% specificity and 63.8% sensitivity. By combining the EMA and AgNOR results, 76 of 80 of the malignant mesothelioma cases (95%) tested positive for at least 1 of the tests with no false-positive results identified. CONCLUSIONS This study confirms the usefulness of EMA in diagnosing malignant and benign mesothelial lesions, and demonstrates the enhanced diagnostic value of combining EMA immunoreaction with the average area of AgNOR per cell, thereby increasing sensitivity in the diagnosis of epithelial malignant mesothelioma.
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Affiliation(s)
- K D Wolanski
- Western Australian Centre for Pathology & Medical Research, Queen Elizabeth II Medical Centre, Nedlands
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26
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Stirling JW, Henderson DW, Rozenbilds MA, Skinner JM, Filipic M. Crystalloidal paraprotein deposits in the cornea: an ultrastructural study of two new cases with tubular crystalloids that contain IgG kappa light chains and IgG gamma heavy chains. Ultrastruct Pathol 1997; 21:337-44. [PMID: 9205998 DOI: 10.3109/01913129709021931] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The fine structure and immunoprotein content of the crystalloids are described in two cases of paraproteinemic crystalloidal keretopathy, both of which had clinical features thought by the referring ophthalmologists to be those of atypical lattice-type corneal dystrophy (presumably because of lattice-like lines). Most keratocytes in one case were surrounded by a mantle of densely packed tubular crystalloids. Individual tubules were annular in cross section with mean dimensions as follows: overall diameter, 29.32 nm (SD 1.26); internal diameter (core), 8.53 nm (SD 1.12); wall thickness, 10.39 nm (SD 0.85) (n = 10). Crystalloids were extracellular and found only in the corneal stroma, with none in Bowman's layer or Descemet's membrane. In the second case, the tubules had a similar distribution but formed geometric arrays with no clear relationship to, or envelopment of the keratocytes. The tubules were thin-walled, with mean dimensions as follows: overall diameter, 26.12 nm (SD 1.12); internal diameter (core), 15.46 nm (SD 1.12); wall thickness, 5.33 nm (SD 0) (n = 10). In both cases the tubules were kappa-light chain- and gamma-chain-positive. Laboratory investigations revealed the presence of two IgM-kappa paraproteins and an IgG-kappa paraprotein in the serum of the first patient. The second patient had an IgG-kappa paraproteinemia and bone marrow changes consistent with low-grade non-Hodgkin's lymphoma. These cases emphasize and extend the morphological range of corneal IgG crystalloids; the second case also demonstrates that corneal IgG crystalloids may be an early indicator of un underlying immunoproliferative disease.
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Affiliation(s)
- J W Stirling
- Department of Pathology, Flinders Medical Centre, Bedford Park, Australia.
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27
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Comin CE, de Klerk NH, Henderson DW. Malignant mesothelioma: current conundrums over risk estimates and whither electron microscopy for diagnosis? Ultrastruct Pathol 1997; 21:315-20. [PMID: 9205996 DOI: 10.3109/01913129709021929] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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28
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Abstract
Inhalation of asbestos fibers increases the risk of bronchial carcinoma. It has been claimed that asbestosis is a necessary prerequisite for the malignancy, but epidemiologic studies usually do not have enough statistical strength to prove that asbestos-exposed patients without asbestosis are without risk. Several recent studies do actually indicate that there is a risk for such patients. In addition, case-referent studies of patients with lung cancer show an attributable risk for asbestos of 6% to 23%, which is much higher than the actual occurrence of asbestosis among these patients. Thus there is an increasing body of evidence that, at low exposure levels, asbestos produces a slight increase in the relative risk of lung cancer even in the absence of asbestosis. Consequently, all exposure to asbestos must be minimized.
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Affiliation(s)
- G Hillerdal
- Department of Lung Medicine, Karolinska Hospital, Stockholm, Sweden
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29
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Abstract
A case of epithelioid sarcoma of the forearm is described. The radiological and pathological features, natural history of the tumour and its treatment are reviewed.
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Affiliation(s)
- S D Hanley
- Department of Radiology, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia
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30
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Weissinger EM, Byrd LG, Henderson DW, Mushinski JF. Overexpression of v-abl uniquely cooperates with c-myc dysregulation in induction of plasma cell tumors, bypassing the need for T-lymphocytic help and overcoming T-lymphocytic interference. Int J Cancer 1996; 67:142-7. [PMID: 8690515 DOI: 10.1002/(sici)1097-0215(19960703)67:1<142::aid-ijc23>3.0.co;2-f] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
We have investigated the effects of T lymphocytes on induction of mouse plasma cell tumors. We show that ABL-MYC, a plasmacytomagenic retrovirus that constitutively expresses v-abl and c-myc, is able to induce plasmacytomas in 100% of athymic BALB/c mice, with or without intraperitoneal pristane pretreatment. Other induction regimens are ineffective under these conditions, indicating that the combination of v-abl and c-myc oncogenes is uniquely able to transform plasma cells in mice that are deficient in T lymphocytes. Furthermore, in the absence of pristane, ABL-MYC-infected athymic congenics developed plasmacytomas in half the time required for euthymic BALB/c mice, suggesting that T lymphocytes can have a negative effect and can retard, but not totally inhibit, the outgrowth of plasmacytomas. This phenomenon could not be appreciated in other regimens of plasmacytoma induction, because only ABL-MYC is sufficient to induce plasmacytomas in athymic mice or in euthymic mice in the absence of pristane pretreatment.
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Affiliation(s)
- E M Weissinger
- Laboratory of Genetics, National Cancer Institute, Bethesda, MD, USA
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31
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Kieser A, Seitz T, Adler HS, Coffer P, Kremmer E, Crespo P, Gutkind JS, Henderson DW, Mushinski JF, Kolch W, Mischak H. Protein kinase C-zeta reverts v-raf transformation of NIH-3T3 cells. Genes Dev 1996; 10:1455-66. [PMID: 8666230 DOI: 10.1101/gad.10.12.1455] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
We have identified protein kinase C-zeta (PKC-zeta) as a novel suppressor of neoplastic transformation caused by the v-raf oncogene. PKC-zeta overexpression drastically retards proliferation, abolishes anchorage-independent growth, and reverts the morphological transformation of v-raf-transformed NIH-3T3 cells. The molecular basis for this effect appears to be a specific induction of junB and egr-1 expression, triggered synergistically by PKC-zeta via a Raf/Mek/MAPK-independent mechanism and v-raf. junB-promoter/CAT assays revealed that PKC-zeta directly targets the junB promoter. The induction of junB and egr-1 is linked to the v-raf transformation-suppressing effect of PKC-zeta as constitutive expression of junB and egr-1 but not of c-jun also abolishes anchorage-independent growth of v-raf-transformed NIH-3T3 cells. Moreover, junB overexpression leads to a retardation of proliferation in these cells. PKC-zeta interferes with the serum inducibility of an AP-1 reporter plasmid in v-raf-transformed NIH-3T3 cells, indicating that PKC-zeta antagonizes transformation and proliferation by down-modulating AP-1 function via induction of junB. In summary, our data suggest that PKC-zeta counteracts v-raf transformation by modulating the expression of the transcription factors junB and egr-1.
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Affiliation(s)
- A Kieser
- Institut für Klinische Molekularbiologie und Tumorgenetik, Forschungszentrum für Umwelt and Gesundheit, München, Germany
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32
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Ghadially FN, Stirling JW, Henderson DW. Case for the panel. Unusual ultrastructural association between erythrocytes and glomerular endothelial cells in patients with membranoproliferative glomerulonephritis. Ultrastruct Pathol 1996; 20:285-90. [PMID: 8727072 DOI: 10.3109/01913129609016326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- F N Ghadially
- Department of Laboratory Medicine, Ottawa Civic Hospital, Ontario, Canada
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33
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Carstens HB, Ghadially FN, Henderson DW, Stirling JW. Case for the panel. Weibel-Palade body-like lamellar structure in angiosarcoma. Ultrastruct Pathol 1995; 19:137-43. [PMID: 7792951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- H B Carstens
- Department of Pathology, University of Louisville, Kentucky, USA
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34
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Abstract
A mobile educator unit is a cost-effective teaching tool that can be easily implemented in an acute hospital setting to assist in teaching patients, visitors, and personnel about health and wellness. The educator unit cannot replace the face-to-face interactions between professional and patient or visitor, but it can supplement and make information more readily available than a stationary display.
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35
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Abstract
The relationship between occupational or environmental exposure to asbestos and the development of mesothelioma, typically after prolonged latency, has been accepted as one of cause and effect. Most studies have concluded that asbestos is not mutagenic to mammalian cells in vitro. We have studied the potential of crocidolite asbestos to induce mutations in a stable mesothelioma cell line, using a mutation assay that measures mutation at the autosomal HLA-A locus and permits clonal growth of mutant cells. The mesothelioma cell line chosen is more akin to the in vivo target cells of asbestos than human peripheral blood lymphocytes used in previous studies. Exposure of mesothelioma cells in culture to both 200 micrograms/ml and 50 micrograms/ml crocidolite for 72 hr did not result in a statistically significant difference in the mutation frequency (MF) in the HLA-A assay when compared to the spontaneous MF in these cells. Mutations in the mesothelioma cells were classified according to their molecular basis. Notwithstanding the lack of statistically significant change in overall MF, molecular analysis of mutants obtained following exposure of mesothelioma cells to crocidolite demonstrated a statistically significant increase in the class of mutations arising from loss of heterozygosity (LOH) events involving the selection locus (HLA-A) and more distal loci. Mutations following exposure to 200 micrograms/ml and 50 micrograms/ml crocidolite showed a greater frequency of LOH than did spontaneous mutants (P < 0.01 and P < 0.001, respectively). These results correlate with those obtained in an earlier study using lymphocytes. The mesothelioma cell-based assay may be useful in detecting the mutagenicity of other asbestiform fibers and man-made fibers.
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Affiliation(s)
- K Both
- Department of Haematology, Flinders Medical Centre, Adelaide, Australia
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36
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Abstract
Although asbestos and erionite are proven human carcinogens, most studies have concluded that these fibres are not mutagenic to mammalian cells in vitro. We have studied the potential of these fibres and chrysotile fibres to induce mutations in human peripheral lymphocytes, using a mutation assay that measures mutation at the autosomal HLA-A locus. Exposure of lymphocytes in culture to 400 micrograms/ml of crocidolite or erionite for 72 hr did not result in a statistically significant increase in the mutation frequency (MF) in the HLA-A assay, although a trend towards increased MF was observed. Exposure to 400 micrograms/ml chrysotile resulted in no increase in MF; however a significant increase was observed at 50 micrograms/ml. Mutations in somatic cells can be classified according to their molecular basis. Molecular analysis of mutants obtained following exposure of lymphocytes to crocidolite and erionite demonstrated a statistically significant increase in the class of mutations arising from loss-of-heterozygosity (LOH) events involving the selection locus (HLA-A) and more distal loci. Mutations following exposure to crocidolite and erionite showed a greater frequency of LOH than did spontaneous mutants (p < 0.02 and p < 0.005 respectively). Mutants following exposure to chrysotile did not display a significant difference in LOH when compared with spontaneous mutants. Thus, although an increase in overall mutation frequency following fibre exposure did not achieve statistical significance, the modest increase seen following exposure to erionite and crocidolite is translated into a highly significant change in those components of the spectrum of mutations which result in LOH.
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MESH Headings
- Alleles
- Asbestos, Crocidolite/toxicity
- Asbestos, Serpentine/toxicity
- Cells, Cultured
- Chi-Square Distribution
- Chromosome Deletion
- Chromosomes, Human, Pair 6/drug effects
- Cloning, Molecular
- Gene Deletion
- Genes, MHC Class I/drug effects
- HLA-A Antigens/genetics
- Heterozygote
- Humans
- Lymphocytes/drug effects
- Mutagenesis, Site-Directed
- Mutagenicity Tests
- Polymorphism, Restriction Fragment Length
- Repetitive Sequences, Nucleic Acid
- Zeolites/toxicity
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Affiliation(s)
- K Both
- Department of Haematology, Flinders Medical Centre, Adelaide, South Australia
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37
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Roden RB, Weissinger EM, Henderson DW, Booy F, Kirnbauer R, Mushinski JF, Lowy DR, Schiller JT. Neutralization of bovine papillomavirus by antibodies to L1 and L2 capsid proteins. J Virol 1994; 68:7570-4. [PMID: 7523700 PMCID: PMC237204 DOI: 10.1128/jvi.68.11.7570-7574.1994] [Citation(s) in RCA: 123] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
We have generated four mouse monoclonal antibodies (MAbs) to bovine papillomavirus virions that bound type-specific, adjacent, and conformationally dependent epitopes on the L1 major capsid protein. All four MAbs were neutralizing at ratios of 1 MAb molecule per 5 to 25 L1 molecules, but only three effectively blocked binding of the virus to the cell surface. Therefore, antibodies can prevent papillomavirus infection by at least two mechanisms: inhibition of cell surface receptor binding and a subsequent step in the infectious pathway. The neutralizing epitopes of the bovine papillomavirus L2 minor capsid protein were mapped to the N-terminal half of L2 by blocking the neutralizing activity of full-length L2 antiserum with bacterially expressed peptides of L2. In addition, rabbit antiserum raised against amino acids 45 to 173 of L2 had a neutralizing titer of 1,000, confirming that at least part of the N terminus of L2 is exposed on the virion surface.
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Affiliation(s)
- R B Roden
- Laboratory of Cellular Oncology, National Cancer Institute, Bethesda, Maryland 20892
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38
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Horsfall DJ, Mayne K, Ricciardelli C, Rao M, Skinner JM, Henderson DW, Marshall VR, Tilley WD. Age-related changes in guinea pig prostatic stroma. J Transl Med 1994; 70:753-63. [PMID: 8196369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND The histology of benign disease of the human prostate (benign prostatic hyperplasia) is heterogeneous. No other species demonstrates the same complexity, and current animal models do not appear to fully encompass the stromal and epithelial developmental changes involved in the human disease. This study describes age-related changes in the prostatic smooth muscle stroma of guinea pigs and humans, which may be pertinent to some aspects of the disease process in humans. EXPERIMENTAL DESIGN Histologic and ultrastructural changes were examined and measured in the prostates of guinea pigs during aging (2 weeks to 31 months). Similar measurements were also made in human prostatic tissues during aging and the development of benign prostatic pathology. RESULTS Morphometric analyses of prostates in guinea pigs and men demonstrated similar changes in stromal volume with age. The stromal volume proportion of the prostate in both species decreases at puberty due to the expansion of the epithelial cell compartment, and is followed by a progressive increase during adulthood until a maximum stromal content of approximately 75% of total tissue volume is reached at age 2 years in guinea pigs, and at age 70 years in men. The pathognomic feature of nodularity and the dramatic increase in gland size observed during the late stages of human benign prostatic disease did not occur in the guinea pig prostate. Ultrastructural analysis of guinea pig prostatic smooth muscle cells identified a progressive hypertrophy (approximately 10-fold) from prepuberty through to old age. Two-thirds of smooth muscle cells in the prostatic stroma of aging individuals of both species demonstrated perinuclear organelles (rough endoplasmic reticulum and free ribosomes) that were not present in younger individuals. CONCLUSIONS The prominent histologic features of the guinea pig prostate during aging are increased stromal mass, significant stromal fibrosis, and occasional prostatitis. These features are frequently observed in men with clinical benign prostatic hyperplasia. The age-related increases in prostatic smooth muscle cell size and content of perinuclear organelles in the guinea pig suggest a re-activation of cellular synthetic activity. The similarity in some features of the prostatic smooth muscle stroma between aging men and guinea pigs suggests that there may be common pathophysiologic processes. We conclude that the guinea pig could be a useful model for examination of the age-related hypertrophy of the smooth muscle cell and the processes inducing reversion to a more synthetic smooth muscle cell phenotype.
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Affiliation(s)
- D J Horsfall
- Department of Surgery, Flinders University School of Medicine, Flinders Medical Centre, Bedford Park, South Australia
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39
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Jones DN, Henderson DW, Morton S, Sage MR. Cryptogenic organizing pneumonia with atypical histopathological features. Australas Radiol 1994; 38:41-5. [PMID: 8147799 DOI: 10.1111/j.1440-1673.1994.tb00123.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
A case report of cryptogenic organizing pneumonia (COP); also known as bronchiolitis obliterans organizing pneumonia (BOOP) is presented. The histopathologic findings of COP are well documented in the literature and typically consist of organizing pneumonia of uniform appearance. This case report describes, in addition to the classic findings, more acute exudative inflammation not usually associated with this condition. Variation in the evolution of the pneumonic process is one of the reasons for reporting this case. The promotion of awareness of this treatable condition is the other reason for reporting this case. When multifocal areas of consolidation are demonstrated radiologically, particularly when peripheral and basal, and in the correct clinical setting, the possibility of COP should be entertained. This condition responds dramatically to steroid therapy, and has a good prognosis.
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Affiliation(s)
- D N Jones
- Department of Radiology, Flinders Medical Centre, Bedford Park, Australia
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40
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Weissinger EM, Henderson DW, Mischak H, Goodnight J, Mushinski JF. Induction of plasmacytomas that secrete monoclonal anti-peptide antibodies by retroviral transformation. J Immunol Methods 1994; 168:123-30. [PMID: 8288888 DOI: 10.1016/0022-1759(94)90216-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
ABL-MYC, a retrovirus that coexpresses v-abl and c-myc, was used to infect six BALB/c mice that had been immunized twice with a KLH-conjugated peptide that consisted of the 18 carboxyterminal amino acids of protein kinase C-eta (PKC-eta). All mice developed transplantable, monoclonal plasmacytomas, and five out of six plasmacytomas secreted antigen-specific antibodies, even after transplantation. All these antibodies recognized PKC-eta on Western blots of crude cell lysates and did not cross react with other isoforms of the PKC family.
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Affiliation(s)
- E M Weissinger
- Laboratory of Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
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41
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Henderson DW, Stirling JW, Lipsett J, Rozenbilds MA, Roberts-Thomson PJ, Coster DJ. Paraproteinemic crystalloidal keratopathy: an ultrastructural study of two cases, including immunoelectron microscopy. Ultrastruct Pathol 1993; 17:643-68. [PMID: 8122330 DOI: 10.3109/01913129309027800] [Citation(s) in RCA: 19] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The ultrastructural appearances of corneal crystalloidal deposits are described in two patients with an IgG-kappa paraproteinemia of uncertain pathogenesis. The crystalloids in one patient were overwhelmingly intracellular and were found mainly in stromal keratocytes, but also in basal corneal epithelial cells and the limbal vascular endothelium. Four types of crystalloid or immunoprotein-containing granules were recognizable in this case: 1) fibrillary crystalloids with a curvilinear filamentous substructure; 2) angulated geometric crystalloids that often had a linear filamentous substructure and transverse or oblique periodicity; 3) cordlike crystalloids; and 4) lysosomelike granules with amorphous contents. Immunoelectron microscopy demonstrated that all of these structures labeled for kappa-light chains, and rectangular type 2 crystalloids showed approximately a twofold greater concentration of the colloidal gold probe than the type 1 fibrillary crystalloids. The evidence suggested development of the crystalloids within lysosomes, with a progression from the granules containing amorphous material, through fibrillary crystalloids, to the geometric structures. The circumferential distribution of the corneal deposits, as well as the presence of vascular endothelial crystalloids and reduplication of external laminae around limbal blood vessels, suggests that the crystalloids originated predominantly or entirely from the blood, with transport of immunoprotein across damaged limbal microvasculature. The abnormal vasculature may also have contributed to corneal edema, which in turn may have exacerbated corneal opacification. The crystalloidal deposits in the other case were exclusively extracellular; they were located beneath and between corneal basal epithelial cells, and predominantly as a mantle around individual keratocytes. The crystalloids in this case consisted overwhelmingly of thick-walled tubules about 40 nm in diameter that labeled for both kappa-light chains and gamma chains with the colloidal gold immunoprobe. In addition, lucent vesicles within keratocytes were found only in sections labeled for kappa-light chains and were positive. The factors that might contribute to the formation of corneal crystalloidal deposits in immunoproliferative disorders are discussed, and include: 1) an inherent propensity for crystallization of some immunoglobulins or kappa-light chains, perhaps because of abnormal molecular structure; and 2) local factors in the cornea that might promote deposition and crystallization of immunoprotein, such as temperature, pH, the water content, and extracellular matrix components.
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Affiliation(s)
- D W Henderson
- Department of Histopathology, Flinders Medical Centre, Bedford Park, South Australia
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42
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Mischak H, Goodnight J, Henderson DW, Osada S, Ohno S, Mushinski JF. Unique expression pattern of protein kinase C-theta: high mRNA levels in normal mouse testes and in T-lymphocytic cells and neoplasms. FEBS Lett 1993; 326:51-5. [PMID: 8325388 DOI: 10.1016/0014-5793(93)81759-s] [Citation(s) in RCA: 42] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
A 2.2-kb cDNA that contains the entire coding region of mouse protein kinase C-theta (PKC-theta) was cloned from skeletal muscle mRNA using reverse transcription and the polymerase chain reaction (PCR). This clone was used as a probe to study the expression of this PKC isoform in normal and transformed hemopoietic cells and other normal tissues. By far the highest steady-state level of PKC-theta mRNA was found as a 2.8-kb transcript on a Northern blot of poly(A)+ RNA from testes. High levels were also found in skeletal muscle, spleen, T lymphomas and purified normal T lymphocytes, but these tissues and cells expressed two transcripts, 3.3 kb and 3.8 kb. Lower levels of similar size transcripts were found in normal brain, B lymphocytes and B-lymphocytic tumors and cell lines.
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Affiliation(s)
- H Mischak
- Molecular Genetics Section, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
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43
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Mackay B, Ordoñez N, Stirling JW, Henderson DW, Papadimitriou JM. Case for the panel. Unusual organelles in an epithelioid angiosarcoma. Ultrastruct Pathol 1993; 17:153-9. [PMID: 8316963 DOI: 10.3109/01913129309084035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- B Mackay
- Department of Pathology, University of Texas, M.D. Anderson Cancer Center, Houston 77030
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44
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Abstract
Cryptococcosis presenting as an intrabronchial mass is not a recognized cause of complete lung collapse. This case report illustrates this extremely rare manifestation of pulmonary cryptococcosis, which mimicked primary pulmonary carcinoma clinically, radiologically and bronchoscopically.
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Affiliation(s)
- E A Carter
- Department of Radiology, Flinders Medical Centre, Bedford Park, Australia
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45
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Stevens MW, Leong AS, Fazzalari NL, Dowling KD, Henderson DW. Cytopathology of malignant mesothelioma: a stepwise logistic regression analysis. Diagn Cytopathol 1992; 8:333-41. [PMID: 1638933 DOI: 10.1002/dc.2840080405] [Citation(s) in RCA: 41] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Twenty-four cytologic features, previously reported to be useful in the distinction of malignant mesothelioma, adenocarcinoma, and benign mesothelial proliferation in serous effusions were assessed. Forty-four cases of malignant mesotheliomas, 46 cases of metastatic adenocarcinomas, and 30 cases of benign mesothelial proliferations were examined for these parameters. When these cytologic features were subjected to a stepwise logistic regression analysis, five features were selected to distinguish malignant mesothelioma from adenocarcinoma. These were true papillary aggregates, multinucleation with atypia, cell-to-cell apposition, acinus-like structures, and balloon-like vacuolation, the latter two features being characteristic of adenocarcinoma. The four variables selected to distinguish malignant mesothelioma from benign mesothelial proliferations were nuclear pleomorphism, macronucleoli, cell-in-cell engulfment, and monolayer cell groups, the latter being a feature of benign proliferations. Using these selected variables, the logistic model correctly predicted 95.4% of cases of malignant mesothelioma versus 100% of adenocarcinoma and 100% of malignant mesotheliomas versus 90% of benign mesothelial proliferations. The results of regression analysis suggest that many of the previously described cytologic features are not important diagnostic discriminators.
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Affiliation(s)
- M W Stevens
- Division of Tissue Pathology, Institute of Medical and Veterinary Science, Adelaide, South Australia
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46
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Whitaker D, Henderson DW, Shilkin KB. The concept of mesothelioma in situ: implications for diagnosis and histogenesis. Semin Diagn Pathol 1992; 9:151-61. [PMID: 1609157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The concept of mesothelioma in situ is explored by a detailed examination of seven patients, subsequently proven to have pleural malignant mesothelioma, who initially had no evidence of gross tumor and for whom biopsy material was available at this early presentation. The tissue was assessed by routine microscopy, the immunoperoxidase technique for epithelial membrane antigen and silver staining for nucleolar organizer regions. Tiny lesions of the pleura that merged with or were adjacent to microscopically flat monolayered or folded mesothelium with cytological atypia were observed. The atypical cells reacted positively to epithelial membrane antigen, and the nucleolar organizer region counts were elevated. These observations are considered to support the possibility of the presence of mesothelioma in situ. These findings are discussed in the light of the proposed concept of mesothelioma in situ, its histogenesis, and its possible clinical relevance.
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Affiliation(s)
- D Whitaker
- Department of Histopathology, Sir Charles Gairdner Hospital, Perth, Australia
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47
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Mierau GW, Agostini R, Beals TF, Carlén B, Dardick I, Henderson DW, Pysher TJ, Weeks DA, Yowell RL. The role of electron microscopy in evaluating ciliary dysfunction: report of a workshop. Ultrastruct Pathol 1992; 16:245-54. [PMID: 1557823 DOI: 10.3109/01913129209074565] [Citation(s) in RCA: 25] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
This report summarizes the proceedings of a workshop organized with the purpose of bringing together many of those with substantial experience in this troublesome area of pathology for an active interchange of ideas, opinions, problems, and solutions. Recognition was given the fact that current knowledge and technical capabilities are woefully inadequate for dealing with the diagnostic questions now being asked. Until such time as these inadequacies can be remedied, a very conservative approach to the interpretation of ultrastructural studies is advocated.
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Affiliation(s)
- G W Mierau
- Department of Pathology, Loma Linda University Medical Center, California
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48
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49
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Affiliation(s)
- J W Stirling
- Flinders Medical Centre, Bedford Park, South Australia
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50
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Ricciardelli C, Horsfall DJ, Skinner JM, Henderson DW, Marshall VR, Tilley WD. Development and characterization of primary cultures of smooth muscle cells from the fibromuscular stroma of the guinea pig prostate. In Vitro Cell Dev Biol 1989; 25:1016-24. [PMID: 2592295 DOI: 10.1007/bf02624135] [Citation(s) in RCA: 25] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Primary cultures of smooth muscle cells (SMCs) were obtained by a two-step enzymatic digestion of guinea pig prostatic stroma. Ultrastructural morphology and growth characteristics of these cells conformed to those reported for SMCs isolated from vascular and visceral tissue sources. Electron microscopic examination indicated that the cells assumed modified myofibroblastoid features in culture. Microfilaments with associated dense bodies were markedly depleted in cultured smooth muscle cells, in comparison with those of the parent tissue. Cultured cells also possessed increased content of rough endoplasmic reticulum indicating the increased secretory or protein-synthetic capacity of the cells. Immunoperoxidase staining for cytoskeletal markers using monoclonal antibodies to desmin and vimentin supported the ultrastructural observations, suggesting a decline in desmin-staining intermediate filaments during "modulation" to the myofibroblastoid form. Despite this depletion of smooth muscle-specific differentiation markers and reversion to more general mesenchymal properties, the cells retained the ability to contract on challenge with norepinephrine, and grew in the characteristic "hill and valley" pattern on attaining confluence. Inasmuch as the estrogen and androgen receptor expression of the parent stromal tissue is also retained, these primary cell cultures should provide a useful model to study regulation of prostatic development.
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Affiliation(s)
- C Ricciardelli
- Department of Surgery, Flinders Medical Centre, Bedford Park, South Australia
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