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Sasso EH, Mabey B, Flake DD, Hitraya E, Chin CL, Ben-Shachar R, Gutin A, Lanchbury JS, Curtis JR. External validation of a multi-biomarker-based score for predicting risk of cardiovascular disease in patients with rheumatoid arthritis. PLoS One 2024; 19:e0296459. [PMID: 38709770 PMCID: PMC11073667 DOI: 10.1371/journal.pone.0296459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 12/13/2023] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND A multi-biomarker disease activity (MBDA)-based cardiovascular disease (CVD) risk score was developed and internally validated in a Medicare cohort to predict 3-year risk for myocardial infarction (MI), stroke or CVD death in patients with rheumatoid arthritis (RA). It combines the MBDA score, leptin, MMP-3, TNF-R1, age and four clinical variables. We are now externally validating it in a younger RA cohort. METHODS Claims data from a private aggregator were linked to MBDA test data to create a cohort of RA patients ≥18 years old. A univariable Cox proportional hazards regression model was fit using the MBDA-based CVD risk score as sole predictor of time-to-a-CVD event (hospitalized MI or stroke). Hazard ratio (HR) estimate was determined for all patients and for clinically relevant subgroups. A multivariable Cox model evaluated whether the MBDA-based CVD risk score adds predictive information to clinical data. RESULTS 49,028 RA patients (340 CVD events) were studied. Mean age was 52.3 years; 18.3% were male. HR for predicting 3-year risk of a CVD event by the MBDA-based CVD risk score in the full cohort was 3.99 (95% CI: 3.51-4.49, p = 5.0×10-95). HR were also significant for subgroups based on age, comorbidities, disease activity, and drug use. In a multivariable model, the MBDA-based CVD risk score added significant information to hypertension, diabetes, tobacco use, history of CVD, age, sex and CRP (HR = 2.27, p = 1.7×10-7). CONCLUSION The MBDA-based CVD risk score has been externally validated in an RA cohort that is younger than and independent of the Medicare cohort that was used for development and internal validation.
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Affiliation(s)
- Eric H. Sasso
- Medical and Scientific Affairs, Crescendo Bioscience, South San Francisco, CA, United States of America
- Myriad Genetics Laboratories, Myriad, Salt Lake City, UT, United States of America
| | - Brent Mabey
- Myriad Genetics Laboratories, Myriad, Salt Lake City, UT, United States of America
| | - Darl D. Flake
- Myriad Genetics Laboratories, Myriad, Salt Lake City, UT, United States of America
| | - Elena Hitraya
- Medical and Scientific Affairs, Crescendo Bioscience, South San Francisco, CA, United States of America
- Myriad Genetics Laboratories, Myriad, Salt Lake City, UT, United States of America
| | - Cheryl L. Chin
- Medical and Scientific Affairs, Crescendo Bioscience, South San Francisco, CA, United States of America
- Myriad Genetics Laboratories, Myriad, Salt Lake City, UT, United States of America
| | - Rotem Ben-Shachar
- Myriad Genetics Laboratories, Myriad, Salt Lake City, UT, United States of America
| | - Alexander Gutin
- Myriad Genetics Laboratories, Myriad, Salt Lake City, UT, United States of America
| | - Jerry S. Lanchbury
- Myriad Genetics Laboratories, Myriad, Salt Lake City, UT, United States of America
| | - Jeffrey R. Curtis
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, United States of America
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Mickley JP, Grove AF, Rouzrokh P, Yang L, Larson AN, Sanchez-Sotello J, Maradit Kremers H, Wyles CC. A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence. Arthritis Care Res (Hoboken) 2024; 76:590-599. [PMID: 37849415 DOI: 10.1002/acr.25260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/27/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023]
Abstract
The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.
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Leaviss J, Carroll C, Essat M, van der Windt D, Grainge MJ, Card T, Riley R, Abhishek A. Prognostic factors for liver, blood and kidney adverse events from glucocorticoid sparing immune-suppressing drugs in immune-mediated inflammatory diseases: a prognostic systematic review. RMD Open 2024; 10:e003588. [PMID: 38199851 PMCID: PMC10806492 DOI: 10.1136/rmdopen-2023-003588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/23/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Immune-suppressing drugs can cause liver, kidney or blood toxicity. Prognostic factors for these adverse-events are poorly understood. PURPOSE To ascertain prognostic factors associated with liver, blood or kidney adverse-events in people receiving immune-suppressing drugs. DATA SOURCES MEDLINE, Web of Science, EMBASE and the Cochrane library (01 January 1995 to 05 January 2023), and supplementary sources. DATA EXTRACTION AND SYNTHESIS Data were extracted by one reviewer using a modified CHARMS-PF checklist and validated by another. Two independent reviewers assessed risk of bias using Quality in Prognostic factor Studies tool and assessed the quality of evidence using a Grading of Recommendations Assessment, Development and Evaluation-informed framework. RESULTS Fifty-six studies from 58 papers were included. High-quality evidence of the following associations was identified: elevated liver enzymes (6 studies) and folate non-supplementation (3 studies) are prognostic factors for hepatotoxicity in those treated with methotrexate; that mercaptopurine (vs azathioprine) (3 studies) was a prognostic factor for hepatotoxicity in those treated with thiopurines; that mercaptopurine (vs azathioprine) (3 studies) and poor-metaboliser status (4 studies) were prognostic factors for cytopenia in those treated with thiopurines; and that baseline elevated liver enzymes (3 studies) are a prognostic factor for hepatotoxicity in those treated with anti-tumour necrosis factors. Moderate and low quality evidence for several other demographic, lifestyle, comorbidities, baseline bloods/serologic or treatment-related prognostic factors were also identified. LIMITATIONS Studies published before 1995, those with less than 200 participants and not published in English were excluded. Heterogeneity between studies included different cut-offs for prognostic factors, use of different outcome definitions and different adjustment factors. CONCLUSIONS Prognostic factors for target-organ damage were identified which may be further investigated for their potential role in targeted (risk-stratified) monitoring. PROSPERO REGISTRATION NUMBER CRD42020208049.
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Affiliation(s)
- Joanna Leaviss
- SCHARR, The University of Sheffield, Sheffield, Yorkshire, UK
| | | | - Munira Essat
- SCHARR, The University of Sheffield, Sheffield, Yorkshire, UK
| | | | - Matthew J Grainge
- Academic Unit of Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Tim Card
- Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK
| | - Richard Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
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Oja M, Tamm S, Mooses K, Pajusalu M, Talvik HA, Ott A, Laht M, Malk M, Lõo M, Holm J, Haug M, Šuvalov H, Särg D, Vilo J, Laur S, Kolde R, Reisberg S. Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned. JAMIA Open 2023; 6:ooad100. [PMID: 38058679 PMCID: PMC10697784 DOI: 10.1093/jamiaopen/ooad100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023] Open
Abstract
Objective To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented. Materials and Methods We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150 824 patients) from 2012 to 2019 (MAITT dataset). For the sample, complete information from all 3 databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools. Results In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary. Discussion During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions. Conclusion For a representative 10% random sample, we successfully transferred complete records from 3 national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence.
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Affiliation(s)
- Marek Oja
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Sirli Tamm
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Kerli Mooses
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Maarja Pajusalu
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Harry-Anton Talvik
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
- STACC, 51009 Tartu, Estonia
| | - Anne Ott
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Marianna Laht
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Maria Malk
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Marcus Lõo
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Johannes Holm
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Markus Haug
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Hendrik Šuvalov
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Dage Särg
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
- STACC, 51009 Tartu, Estonia
| | - Jaak Vilo
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
- STACC, 51009 Tartu, Estonia
| | - Sven Laur
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Raivo Kolde
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
| | - Sulev Reisberg
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
- STACC, 51009 Tartu, Estonia
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Hughes N, Rijnbeek PR, van Bochove K, Duarte-Salles T, Steinbeisser C, Vizcaya D, Prieto-Alhambra D, Ryan P. Evaluating a novel approach to stimulate open science collaborations: a case series of "study-a-thon" events within the OHDSI and European IMI communities. JAMIA Open 2022; 5:ooac100. [PMID: 36406796 PMCID: PMC9670330 DOI: 10.1093/jamiaopen/ooac100] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 10/25/2022] [Accepted: 11/02/2022] [Indexed: 10/09/2023] Open
Abstract
OBJECTIVE We introduce and review the concept of a study-a-thon as a catalyst for open science in medicine, utilizing harmonized real world, observation health data, tools, skills, and methods to conduct network studies, generating insights for those wishing to use study-a-thons for future research. MATERIALS AND METHODS A series of historical study-a-thons since 2017 to present were reviewed for thematic insights as to the opportunity to accelerate the research method to conduct studies across therapeutic areas. Review of publications and experience of the authors generated insights to illustrate the conduct of study-a-thons, key learning, and direction for those wishing to conduct future such study-a-thons. RESULTS A review of six study-a-thons have provided insights into their scientific impact, and 13 areas of insights for those wishing to conduct future study-a-thons. Defining aspects of the study-a-thon method for rapid, collaborative research through network studies reinforce the need to clear scientific rationale, tools, skills, and methods being collaboratively to conduct a focused study. Well-characterized preparatory, execution and postevent phases, coalescing skills, experience, data, clinical input (ensuring representative clinical context to the research query), and well-defined, logical steps in conducting research via the study-a-thon method are critical. CONCLUSIONS A study-a-thon is a focused multiday research event generating reliable evidence on a specific medical topic across different countries and health systems. In a study-a-thon, a multidisciplinary team collaborate to create an accelerated contribution to scientific evidence and clinical practice. It critically accelerates the research process, without inhibiting the quality of the research output and evidence generation, through a reproducible process.
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Affiliation(s)
- N Hughes
- Epidemiology, Janssen R&D, Beerse, Belgium
| | - P R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - T Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - D Vizcaya
- Bayer Pharmaceuticals, Sant Joan Despi, Spain
| | | | - P Ryan
- Epidemiology, Janssen R&D, Titusville, New Jersey, USA
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