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Lim YMF, Asselbergs FW, Bagheri A, Denaxas S, Tay WT, Voors A, Lam CSP, Koudstaal S, Grobbee DE, Vaartjes I. Eligibility of Asian and European registry patients for phase III trials in heart failure with reduced ejection fraction. ESC Heart Fail 2024. [PMID: 38984466 DOI: 10.1002/ehf2.14751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/29/2024] [Accepted: 02/19/2024] [Indexed: 07/11/2024] Open
Abstract
AIMS Traditional approaches to designing clinical trials for heart failure (HF) have historically relied on expertise and past practices. However, the evolving landscape of healthcare, marked by the advent of novel data science applications and increased data availability, offers a compelling opportunity to transition towards a data-driven paradigm in trial design. This research aims to evaluate the scope and determinants of disparities between clinical trials and registries by leveraging natural language processing for the analysis of trial eligibility criteria. The findings contribute to the establishment of a robust design framework for guiding future HF trials. METHODS AND RESULTS Interventional phase III trials registered for HF on ClinicalTrials.gov as of the end of 2021 were identified. Natural language processing was used to extract and structure the eligibility criteria for quantitative analysis. The most common criteria for HF with reduced ejection fraction (HFrEF) were applied to estimate patient eligibility as a proportion of registry patients in the ASIAN-HF (N = 4868) and BIOSTAT-CHF registries (N = 2545). Of the 375 phase III trials for HF, 163 HFrEF trials were identified. In these trials, the most frequently encountered inclusion criteria were New York Heart Association (NYHA) functional class (69%), worsening HF (23%), and natriuretic peptides (18%), whereas the most frequent comorbidity-based exclusion criteria were acute coronary syndrome (64%), renal disease (55%), and valvular heart disease (47%). On average, 20% of registry patients were eligible for HFrEF trials. Eligibility distributions did not differ (P = 0.18) between Asian [median eligibility 0.20, interquartile range (IQR) 0.08-0.43] and European registry populations (median 0.17, IQR 0.06-0.39). With time, HFrEF trials became more restrictive, where patient eligibility declined from 0.40 in 1985-2005 to 0.19 in 2016-2022 (P = 0.03). When frequency among trials is taken into consideration, the eligibility criteria that were most restrictive were prior myocardial infarction, NYHA class, age, and prior HF hospitalization. CONCLUSIONS Based on 14 trial criteria, only one-fifth of registry patients were eligible for phase III HFrEF trials. Overall eligibility rates did not differ between the Asian and European patient cohorts.
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Affiliation(s)
- Yvonne Mei Fong Lim
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Institute for Clinical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia
| | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Ayoub Bagheri
- Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands
| | - Spiros Denaxas
- Institute of Health Informatics, UCL BHF Research Accelerator and Health Data Research UK, University College London, London, UK
- British Heart Foundation Data Science Center, London, UK
| | - Wan Ting Tay
- National Heart Centre Singapore, Singapore, Singapore
| | - Adriaan Voors
- Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Stefan Koudstaal
- Department of Cardiology, Groene Hart Ziekenhuis, Gouda, The Netherlands
| | - Diederick E Grobbee
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Julius Clinical, Zeist, The Netherlands
| | - Ilonca Vaartjes
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Kalankesh LR, Monaghesh E. Utilization of EHRs for clinical trials: a systematic review. BMC Med Res Methodol 2024; 24:70. [PMID: 38494497 PMCID: PMC10946197 DOI: 10.1186/s12874-024-02177-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/08/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND AND OBJECTIVE Clinical trials are of high importance for medical progress. This study conducted a systematic review to identify the applications of EHRs in supporting and enhancing clinical trials. MATERIALS AND METHODS A systematic search of PubMed was conducted on 12/3/2023 to identify relevant studies on the use of EHRs in clinical trials. Studies were included if they (1) were full-text journal articles, (2) were written in English, (3) examined applications of EHR data to support clinical trial processes (e.g. recruitment, screening, data collection). A standardized form was used by two reviewers to extract data on: study design, EHR-enabled process(es), related outcomes, and limitations. RESULTS Following full-text review, 19 studies met the predefined eligibility criteria and were included. Overall, included studies consistently demonstrated that EHR data integration improves clinical trial feasibility and efficiency in recruitment, screening, data collection, and trial design. CONCLUSIONS According to the results of the present study, the use of Electronic Health Records in conducting clinical trials is very helpful. Therefore, it is better for researchers to use EHR in their studies for easy access to more accurate and comprehensive data. EHRs collects all individual data, including demographic, clinical, diagnostic, and therapeutic data. Moreover, all data is available seamlessly in EHR. In future studies, it is better to consider the cost-effectiveness of using EHR in clinical trials.
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Affiliation(s)
- Leila R Kalankesh
- Tabriz Health Services Management Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Elham Monaghesh
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
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Patel PC, Tsionas MG, Devaraj S. Relative bed allocation for COVID-19 patients, EHR investments, and COVID-19 mortality outcomes. PLoS One 2023; 18:e0286210. [PMID: 37883479 PMCID: PMC10602360 DOI: 10.1371/journal.pone.0286210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 05/10/2023] [Indexed: 10/28/2023] Open
Abstract
Managing flexibility in the relative bed allocation for COVID-19 and non-COVID-19 patients was a key challenge for hospitals during the COVID-19 pandemic. Based on organizational information processing theory (OIPT), we propose that the local electronic health record (EHR) systems could improve patient outcomes through improved bed allocation in the local area. In an empirical analysis of county-level weekly hospital data in the US, relative capacity of beds in hospitals with higher EHR was associated with lower 7-, 14-, and 21-day forward-looking COVID-19 death rate at the county-level. Testing for cross-state variation in non-pharmaceutical interventions along contiguous county border-pair analysis to control for spatial correlation varying between state variations in non-pharmaceutical intervention policies, 2SLS analysis using quality ratings, and using foot-traffic data at the US hospitals our findings are generally supported. The findings have implications for policymakers and stakeholders of the local healthcare supply chains and EHR systems.
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Affiliation(s)
- Pankaj C. Patel
- Villanova School of Business, Villanova University, Villanova, Pennsylvania, United States of America
| | - Mike G. Tsionas
- Montpellier Business School, France and Lancaster University Management School, Lancaster, United Kingdom
| | - Srikant Devaraj
- Center for Business and Economic Research, Miller College of Business, Ball State University, Muncie, Indiana, United States of America
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Su Q, Cheng G, Huang J. A review of research on eligibility criteria for clinical trials. Clin Exp Med 2023; 23:1867-1879. [PMID: 36602707 PMCID: PMC9815064 DOI: 10.1007/s10238-022-00975-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023]
Abstract
The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries.
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Affiliation(s)
- Qianmin Su
- Department of Computer Science, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, No. 333 Longteng Road, Shanghai, 201620, China.
| | - Gaoyi Cheng
- Department of Computer Science, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, No. 333 Longteng Road, Shanghai, 201620, China
| | - Jihan Huang
- Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
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Fang Y, Liu H, Idnay B, Ta C, Marder K, Weng C. A data-driven approach to optimizing clinical study eligibility criteria. J Biomed Inform 2023; 142:104375. [PMID: 37141977 PMCID: PMC10262300 DOI: 10.1016/j.jbi.2023.104375] [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: 11/04/2022] [Revised: 04/26/2023] [Accepted: 04/27/2023] [Indexed: 05/06/2023]
Abstract
OBJECTIVE Feasible, safe, and inclusive eligibility criteria are crucial to successful clinical research recruitment. Existing expert-centered methods for eligibility criteria selection may not be representative of real-world populations. This paper presents a novel model called OPTEC (OPTimal Eligibility Criteria) based on the Multiple Attribute Decision Making method boosted by an efficient greedy algorithm. METHODS It systematically identifies the optimal criteria combination for a given medical condition with the optimal tradeoff among feasibility, patient safety, and cohort diversity. The model offers flexibility in attribute configurations and generalizability to various clinical domains. The model was evaluated on two clinical domains (i.e., Alzheimer's disease and Neoplasm of pancreas) using two datasets (i.e., MIMIC-III dataset and NewYork-Presbyterian/Columbia University Irving Medical Center (NYP/CUIMC) database). RESULTS We simulated the process of automatically optimizing eligibility criteria according to user-specified prioritization preferences and generated recommendations based on the top-ranked criteria combination accordingly (top 0.41-2.75%) with OPTEC. Harnessing the power of the model, we designed an interactive criteria recommendation system and conducted a case study with an experienced clinical researcher using the think-aloud protocol. CONCLUSIONS The results demonstrated that OPTEC could be used to recommend feasible eligibility criteria combinations, and to provide actionable recommendations for clinical study designers to construct a feasible, safe, and diverse cohort definition during early study design.
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Affiliation(s)
- Yilu Fang
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Hao Liu
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Betina Idnay
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Casey Ta
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Karen Marder
- Department of Neurology, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
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Chen A, Li Q, He X, Jaffee MS, Hogan WR, Wang F, Guo Y, Bian J. Impacts of Eligibility Criteria on Trial Participants' Age in Alzheimer's Disease Clinical Trials. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2023; 2022:368-376. [PMID: 37128470 PMCID: PMC10148327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Overly restricted and poorly designed eligibility criteria reduce the generalizability of the results from clinical trials. We conducted a study to identify and quantify the impacts of study traits extracted from eligibility criteria on the age of study populations in Alzheimer's Disease (AD) clinical trials. Using machine learning methods and SHapley Additive exPlanation (SHAP) values, we identified 30 and 34 study traits that excluded older patients from AD trials in our 2 generated target populations respectively. We also found that study traits had different magnitudes of impacts on the age distributions of the generated study populations across racial-ethnic groups. To our best knowledge, this was the first study that quantified the impact of eligibility criteria on the age of AD trial participants. Our research is a first step in addressing the overly restrictive eligibility criteria in AD clinical trials.
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Affiliation(s)
- Aokun Chen
- University of Florida, Gainesville, Florida, USA
| | - Qian Li
- University of Florida, Gainesville, Florida, USA
| | - Xing He
- University of Florida, Gainesville, Florida, USA
| | | | | | - Fei Wang
- Weill Cornell Medicine, New York City, New York, USA
| | - Yi Guo
- University of Florida, Gainesville, Florida, USA
| | - Jiang Bian
- University of Florida, Gainesville, Florida, USA
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Sprangers B, Perazella MA, Lichtman SM, Rosner MH, Jhaveri KD. Improving Cancer Care for Patients With CKD: The Need for Changes in Clinical Trials. Kidney Int Rep 2022; 7:1939-1950. [PMID: 36090489 PMCID: PMC9458993 DOI: 10.1016/j.ekir.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/10/2022] [Accepted: 06/06/2022] [Indexed: 11/06/2022] Open
Abstract
Chemotherapeutic agents used to treat cancer generally have narrow therapeutic indices along with potentially serious adverse toxicities. Many cancer drugs are at least partially excreted through the kidney and, thus, the availability of accurate data on safe and effective dosing of these drugs in patients with chronic kidney disease (CKD) is essential to guide treatment decisions. Typically, during drug development, initial clinical studies only include patients with normal or only mildly impaired kidney function. In subsequent preregistration studies, a limited number of patients with more severe kidney dysfunction are included. Data obtained from patients with either severe kidney dysfunction (here defined as an estimated glomerular filtration rate [eGFR] < 30 ml/min or stage 4G CKD) or end-stage kidney disease (ESKD) requiring kidney replacement treatment are particularly limited before drug registration and only a minority of new drug applications to the US Food and Drug Administration (FDA) include data from this population. Unfortunately, limited data and/or other safety concerns may result in a manufacturer statement that the drug is contraindicated in patients with advanced kidney disease, which hinders access to potentially beneficial drugs for these patients. This systemic exclusion of patients with CKD from cancer drug trials remains an unsolved problem, which prevents provision of optimal clinical care for these patients, raises questions of inclusion, diversity, and equity. In addition, with the aging of the population, there are increasing numbers of patients with CKD and cancer who face these issues. In this review, we evaluate the scientific basis to exclude patients with CKD from cancer trials and propose a comprehensive strategy to address this problem.
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Affiliation(s)
- Ben Sprangers
- Department of Microbiology and Immunology, Laboratory of Molecular Immunology, Rega Institute, KU Leuven, Leuven, Belgium
- Division of Nephrology, University Hospitals Leuven, Leuven, Belgium
| | - Mark A. Perazella
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Medical Center, West Haven, Connecticut, USA
| | - Stuart M. Lichtman
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Mitchell H. Rosner
- Division of Nephrology, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Kenar D. Jhaveri
- Division of Kidney Diseases and Hypertension, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Great Neck, New York, USA
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Rogers JR, Pavisic J, Ta CN, Liu C, Soroush A, Cheung YK, Hripcsak G, Weng C. Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety. J Biomed Inform 2022; 127:104032. [PMID: 35189334 PMCID: PMC8920749 DOI: 10.1016/j.jbi.2022.104032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 10/19/2022]
Abstract
OBJECTIVE To present an approach on using electronic health record (EHR) data that assesses how different eligibility criteria, either individually or in combination, can impact patient count and safety (exemplified by all-cause hospitalization risk) and further assist with criteria selection for prospective clinical trials. MATERIALS AND METHODS Trials in three disease domains - relapsed/refractory (r/r) lymphoma/leukemia; hepatitis C virus (HCV); stages 3 and 4 chronic kidney disease (CKD) - were analyzed as case studies for this approach. For each disease domain, criteria were identified and all criteria combinations were used to create EHR cohorts. Per combination, two values were derived: (1) number of eligible patients meeting the selected criteria; (2) hospitalization risk, measured as the hazard ratio between those that qualified and those that did not. From these values, k-means clustering was applied to derive which criteria combinations maximized patient counts but minimized hospitalization risk. RESULTS Criteria combinations that reduced hospitalization risk without substantial reductions on patient counts were as follows: for r/r lymphoma/leukemia (23 trials; 9 criteria; 623 patients), applying no infection and adequate absolute neutrophil count while forgoing no prior malignancy; for HCV (15; 7; 751), applying no human immunodeficiency virus and no hepatocellular carcinoma while forgoing no decompensated liver disease/cirrhosis; for CKD (10; 9; 23893), applying no congestive heart failure. CONCLUSIONS Within each disease domain, the more drastic effects were generally driven by a few criteria. Similar criteria across different disease domains introduce different changes. Although results are contingent on the trial sample and the EHR data used, this approach demonstrates how EHR data can inform the impact on safety and available patients when exploring different criteria combinations for designing clinical trials.
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Affiliation(s)
- James R. Rogers
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - Jovana Pavisic
- Department of Pediatrics, Division of Pediatric Hematology, Oncology, and Stem Cell Transplantation, Columbia University Irving Medical Center, New York, NY
| | - Casey N. Ta
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - Ali Soroush
- Department of Biomedical Informatics, Columbia University, New York, NY,Division of Gastroenterology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | | | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY,Medical Informatics Services, New York-Presbyterian Hospital, New York, NY
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY, United States.
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Kim JH, Butler AM, Ta CN, Sun Y, Maurer MS, Weng C. The potential role of EHR data in optimizing eligibility criteria definition for cardiovascular outcome trials. Int J Med Inform 2021; 156:104587. [PMID: 34624661 DOI: 10.1016/j.ijmedinf.2021.104587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/06/2021] [Accepted: 09/18/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Cardiovascular outcome trials (CVOTs) include patients with high risks for cardiovascular events based on specific inclusion criteria. Little is known about the impact of such inclusion criteria on patient accrual and the incidence rate of cardiovascular events. MATERIALS AND METHODS We evaluated the impact of criteria on the accrual and the number of cardiovascular events in a cohort of 1544 diabetes patients identified from the clinical data warehouse of New York Presbyterian Hospital / Columbia University Irving Medical Center. RESULTS The highest incidence rate of the composite events (i.e., cardiovascular mortality, stroke, and myocardial infarction) was observed when the inclusion criteria seek patients with underlying cardiovascular diseases or age ≥ 60 with at least two of the risk factors including duration of diabetes, hypertension, dyslipidemia, smoking status, and albuminuria. CONCLUSION Our study shows that the electronic health records could be utilized to optimize the inclusion criteria while balancing study inclusiveness and number of events.
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Affiliation(s)
- Jae Hyun Kim
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Alex M Butler
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Casey N Ta
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Yingcheng Sun
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Mathew S Maurer
- Division of Cardiology, Department of Medicine, Columbia University, New York, NY 10032, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
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10
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Sun Y, Butler A, Diallo I, Kim JH, Ta C, Rogers JR, Liu H, Weng C. A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data. Appl Clin Inform 2021; 12:816-825. [PMID: 34496418 DOI: 10.1055/s-0041-1733846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. OBJECTIVES This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. METHODS We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. RESULTS We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. CONCLUSION This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.
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Affiliation(s)
- Yingcheng Sun
- Department of Biomedical Informatics, Columbia University, New York, New York, United States
| | - Alex Butler
- Department of Biomedical Informatics, Columbia University, New York, New York, United States.,Department of Medicine, Columbia University, New York, New York, United States
| | - Ibrahim Diallo
- Department of Biomedical Informatics, Columbia University, New York, New York, United States
| | - Jae Hyun Kim
- Department of Biomedical Informatics, Columbia University, New York, New York, United States
| | - Casey Ta
- Department of Biomedical Informatics, Columbia University, New York, New York, United States
| | - James R Rogers
- Department of Biomedical Informatics, Columbia University, New York, New York, United States
| | - Hao Liu
- Department of Biomedical Informatics, Columbia University, New York, New York, United States
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, United States
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Israel A, Schäffer AA, Cicurel A, Cheng K, Sinha S, Schiff E, Feldhamer I, Tal A, Lavie G, Ruppin E. Identification of drugs associated with reduced severity of COVID-19 - a case-control study in a large population. eLife 2021; 10:e68165. [PMID: 34313216 PMCID: PMC8321549 DOI: 10.7554/elife.68165] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 07/07/2021] [Indexed: 12/21/2022] Open
Abstract
Background Until coronavirus disease 2019 (COVID-19) drugs specifically developed to treat COVID-19 become more widely accessible, it is crucial to identify whether existing medications have a protective effect against severe disease. Toward this objective, we conducted a large population study in Clalit Health Services (CHS), the largest healthcare provider in Israel, insuring over 4.7 million members. Methods Two case-control matched cohorts were assembled to assess which medications, acquired in the last month, decreased the risk of COVID-19 hospitalization. Case patients were adults aged 18 to 95 hospitalized for COVID-19. In the first cohort, five control patients, from the general population, were matched to each case (n=6202); in the second cohort, two non-hospitalized SARS-CoV-2 positive control patients were matched to each case (n=6919). The outcome measures for a medication were: odds ratio (OR) for hospitalization, 95% confidence interval (CI), and the p-value, using Fisher's exact test. False discovery rate was used to adjust for multiple testing. Results Medications associated with most significantly reduced odds for COVID-19 hospitalization include: ubiquinone (OR=0.185, 95% CI [0.058 to 0.458], p<0.001), ezetimibe (OR=0.488, 95% CI [0.377 to 0.622], p<0.001), rosuvastatin (OR=0.673, 95% CI [0.596 to 0.758], p<0.001), flecainide (OR=0.301, 95% CI [0.118 to 0.641], p<0.001), and vitamin D (OR=0.869, 95% CI [0.792 to 0.954], p<0.003). Remarkably, acquisition of artificial tears, eye care wipes, and several ophthalmological products were also associated with decreased risk for hospitalization. Conclusions Ubiquinone, ezetimibe, and rosuvastatin, all related to the cholesterol synthesis pathway were associated with reduced hospitalization risk. These findings point to a promising protective effect which should be further investigated in controlled, prospective studies. Funding This research was supported in part by the Intramural Research Program of the National Institutes of Health, NCI.
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Affiliation(s)
- Ariel Israel
- Division of Planning and Strategy, Clalit Health ServicesTel AvivIsrael
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of HealthBethesdaUnited States
| | - Assi Cicurel
- Division of Planning and Strategy, Clalit Health ServicesTel AvivIsrael
- Clalit Health Services, Southern District and Faculty of Health Sciences, Ben-Gurion University of the NegevBeer-ShevaIsrael
| | - Kuoyuan Cheng
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of HealthBethesdaUnited States
| | - Sanju Sinha
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of HealthBethesdaUnited States
| | - Eyal Schiff
- Sheba Medical Center, Tel-Aviv UniversityRamat GanIsrael
| | - Ilan Feldhamer
- Division of Planning and Strategy, Clalit Health ServicesTel AvivIsrael
| | - Ameer Tal
- Division of Planning and Strategy, Clalit Health ServicesTel AvivIsrael
| | - Gil Lavie
- Division of Planning and Strategy, Clalit Health ServicesTel AvivIsrael
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of TechnologyHaifaIsrael
| | - Eytan Ruppin
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of HealthBethesdaUnited States
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12
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Weng C, Rogers JR. AI uses patient data to optimize selection of eligibility criteria for clinical trials. Nature 2021; 592:512-513. [PMID: 33828278 DOI: 10.1038/d41586-021-00845-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 2021; 592:629-633. [PMID: 33828294 DOI: 10.1038/s41586-021-03430-5] [Citation(s) in RCA: 120] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 03/08/2021] [Indexed: 01/04/2023]
Abstract
There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging1-3. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety.
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He Z, Erdengasileng A, Luo X, Xing A, Charness N, Bian J. How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov. JAMIA Open 2021; 4:ooab032. [PMID: 34056559 PMCID: PMC8083215 DOI: 10.1093/jamiaopen/ooab032] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/15/2021] [Accepted: 04/13/2021] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability. METHODS We analyzed 3765 COVID-19 studies registered in the largest public registry-ClinicalTrials.gov, leveraging natural language processing (NLP) and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population. RESULTS Our analysis included 2295 interventional studies and 1470 observational studies. Most trials did not explicitly exclude older adults with common chronic conditions. However, known risk factors such as diabetes and hypertension were considered by less than 5% of trials based on their trial description. Pregnant women were excluded by 34.9% of the studies. CONCLUSIONS Most COVID-19 clinical studies included both genders and older adults. However, risk factors such as diabetes, hypertension, and pregnancy were under-represented, likely skewing the population that was sampled. A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.
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Affiliation(s)
- Zhe He
- School of Information, Florida State University, Tallahassee, Florida, USA
| | | | - Xiao Luo
- Department of Computer Information and Graphics Technology, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana, USA
| | - Aiwen Xing
- Department of Statistics, Florida State University, Tallahassee, Florida, USA
| | - Neil Charness
- Department of Psychology, Florida State University, Tallahassee, Florida, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA
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Israel A, Schäffer AA, Cicurel A, Feldhamer I, Tal A, Cheng K, Sinha S, Schiff E, Lavie G, Ruppin E. Identification of drugs associated with reduced severity of COVID-19: A case-control study in a large population. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2020.10.13.20211953. [PMID: 33083810 PMCID: PMC7574266 DOI: 10.1101/2020.10.13.20211953] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Until COVID-19 drugs specifically developed to treat COVID-19 become more widely accessible, it is crucial to identify whether existing medications have a protective effect against severe disease. Towards this objective, we conducted a large population study in Clalit Health Services (CHS), the largest healthcare provider in Israel, insuring over 4.7 million members. METHODS Two case-control matched cohorts were assembled to assess which medications, acquired in the last month, decreased the risk of COVID-19 hospitalization. Case patients were adults aged 18-95 hospitalized for COVID-19. In the first cohort, five control patients, from the general population, were matched to each case (n=6202); in the second cohort, two non-hospitalized SARS-CoV-2 positive control patients were matched to each case (n=6919). The outcome measures for a medication were: odds ratio (OR) for hospitalization, 95% confidence interval (CI), and the p-value, using Fisher's exact test. False discovery rate was used to adjust for multiple testing. RESULTS Medications associated with most significantly reduced odds for COVID-19 hospitalization include: ubiquinone (OR=0.185, 95% CI (0.058 to 0.458), p<0.001), ezetimibe (OR=0.488, 95% CI ((0.377 to 0.622)), p<0.001), rosuvastatin (OR=0.673, 95% CI (0.596 to 0.758), p<0.001), flecainide (OR=0.301, 95% CI (0.118 to 0.641), p<0.001), and vitamin D (OR=0.869, 95% CI (0.792 to 0.954), p<0.003). Remarkably, acquisition of artificial tears, eye care wipes, and several ophthalmological products were also associated with decreased risk for hospitalization. CONCLUSIONS Ubiquinone, ezetimibe and rosuvastatin, all related to the cholesterol synthesis pathway were associated with reduced hospitalization risk. These findings point to a promising protective effect which should be further investigated in controlled, prospective studies. FUNDING This research was supported in part by the Intramural Research Program of the National Institutes of Health, NCI.
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Affiliation(s)
- Ariel Israel
- Division of Planning and Strategy, Clalit Health Services, Tel Aviv 62098, Israel
| | - Alejandro A. Schäffer
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 20892
| | - Assi Cicurel
- Division of Planning and Strategy, Clalit Health Services, Tel Aviv 62098, Israel
- Clalit Health Services, Southern District and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Ilan Feldhamer
- Division of Planning and Strategy, Clalit Health Services, Tel Aviv 62098, Israel
| | - Ameer Tal
- Division of Planning and Strategy, Clalit Health Services, Tel Aviv 62098, Israel
| | - Kuoyuan Cheng
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 20892
| | - Sanju Sinha
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 20892
| | - Eyal Schiff
- Sheba Medical Center, Tel-Aviv University, Ramat Gan 52621, Israel
| | - Gil Lavie
- Division of Planning and Strategy, Clalit Health Services, Tel Aviv 62098, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa 3109601, Israel
| | - Eytan Ruppin
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 20892
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16
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Bakken S. The maturation of clinical research informatics as a subdomain of biomedical informatics. J Am Med Inform Assoc 2021; 28:1-2. [PMID: 33450764 DOI: 10.1093/jamia/ocaa312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 11/23/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Columbia University, New York, New York, USA.,Department of Biomedical Informatics, Columbia University, New York, New York, USA.,Data Science Institute, Columbia University, New York, New York, USA
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