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Gao J, Bonzel CL, Hong C, Varghese P, Zakir K, Gronsbell J. Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms. J Am Med Inform Assoc 2024; 31:640-650. [PMID: 38128118 PMCID: PMC10873838 DOI: 10.1093/jamia/ocad226] [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: 05/03/2023] [Revised: 09/22/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVE High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medical supervision required for phenotyping algorithm (PA) estimation and evaluation. To address this challenge, numerous weakly-supervised learning methods have been proposed. However, there is a paucity of methods for reliably evaluating the predictive performance of PAs when a very small proportion of the data is labeled. To fill this gap, we introduce a semi-supervised approach (ssROC) for estimation of the receiver operating characteristic (ROC) parameters of PAs (eg, sensitivity, specificity). MATERIALS AND METHODS ssROC uses a small labeled dataset to nonparametrically impute missing labels. The imputations are then used for ROC parameter estimation to yield more precise estimates of PA performance relative to classical supervised ROC analysis (supROC) using only labeled data. We evaluated ssROC with synthetic, semi-synthetic, and EHR data from Mass General Brigham (MGB). RESULTS ssROC produced ROC parameter estimates with minimal bias and significantly lower variance than supROC in the simulated and semi-synthetic data. For the 5 PAs from MGB, the estimates from ssROC are 30% to 60% less variable than supROC on average. DISCUSSION ssROC enables precise evaluation of PA performance without demanding large volumes of labeled data. ssROC is also easily implementable in open-source R software. CONCLUSION When used in conjunction with weakly-supervised PAs, ssROC facilitates the reliable and streamlined phenotyping necessary for EHR-based research.
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Affiliation(s)
- Jianhui Gao
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Chuan Hong
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States
| | - Paul Varghese
- Health Informatics, Verily Life Sciences, Cambridge, MA, United States
| | - Karim Zakir
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Jessica Gronsbell
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
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Ashley F, Allen L, Gronsbell J. Letter to the Editor on "Desired decision-making role and treatment satisfaction among trans people during medical transition: results from the ENIGI follow-up study". J Sex Med 2023; 20:1258-1259. [PMID: 37553086 DOI: 10.1093/jsxmed/qdad092] [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] [Received: 04/25/2023] [Revised: 04/25/2023] [Accepted: 05/03/2023] [Indexed: 08/10/2023]
Affiliation(s)
- Florence Ashley
- Faculty of Law, University of Alberta, Edmonton, AB T6G 2H5, Canada
| | - Luke Allen
- Private Practice, Las Vegas, NV 89119, United States
| | - Jessica Gronsbell
- Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada
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Hou J, Zhao R, Gronsbell J, Lin Y, Bonzel CL, Zeng Q, Zhang S, Beaulieu-Jones BK, Weber GM, Jemielita T, Wan SS, Hong C, Cai T, Wen J, Ayakulangara Panickan V, Liaw KL, Liao K, Cai T. Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies. J Med Internet Res 2023; 25:e45662. [PMID: 37227772 DOI: 10.2196/45662] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/31/2023] [Accepted: 04/05/2023] [Indexed: 05/26/2023] Open
Abstract
Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.
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Affiliation(s)
- Jue Hou
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Rachel Zhao
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jessica Gronsbell
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Yucong Lin
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Qingyi Zeng
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Sinian Zhang
- School of Statistics, Renmin University of China, Bejing, China
| | | | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | | | - Chuan Hong
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States
| | - Tianrun Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Jun Wen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | | | - Katherine Liao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States
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Yang S, Varghese P, Stephenson E, Tu K, Gronsbell J. Machine learning approaches for electronic health records phenotyping: a methodical review. J Am Med Inform Assoc 2023; 30:367-381. [PMID: 36413056 PMCID: PMC9846699 DOI: 10.1093/jamia/ocac216] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [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: 07/28/2022] [Revised: 09/27/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used. MATERIALS AND METHODS We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies. RESULTS Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions. DISCUSSION Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released. CONCLUSION Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.
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Affiliation(s)
- Siyue Yang
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | | | - Ellen Stephenson
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Karen Tu
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jessica Gronsbell
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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Stephenson E, Yusuf A, Gronsbell J, Tu K, Melamed O, Mitiku T, Selby P, O'Neill B. Disruptions in Primary Care among People with Schizophrenia in Ontario, Canada, During the COVID-19 Pandemic. Can J Psychiatry 2022:7067437221140384. [PMID: 36453004 PMCID: PMC9720063 DOI: 10.1177/07067437221140384] [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] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
OBJECTIVE To investigate how primary care access, intensity and quality of care changed among patients living with schizophrenia before and after the onset of the COVID-19 pandemic in Ontario, Canada. METHODS This cohort study was performed using primary care electronic medical record data from the University of Toronto Practice-Based Research Network (UTOPIAN), a network of > 500 family physicians in Ontario, Canada. Data were collected during primary care visits from 2643 patients living with schizophrenia. Rates of primary care health service use (in-person and virtual visits with family physicians) and key preventive health indices indicated in antipsychotic monitoring (blood pressure readings, hemoglobin A1c, cholesterol and complete blood cell count [CBC] tests) were measured and compared in the 12 months before and after onset of the COVID-19 pandemic. RESULTS Access to in-person care dropped with the onset of the COVID-19 pandemic. During the first year of the pandemic only 39.5% of patients with schizophrenia had at least one in-person visit compared to 81.0% the year prior. There was a corresponding increase in virtual visits such that 78.0% of patients had a primary care appointment virtually during the pandemic period. Patients prescribed injectable antipsychotics were more likely to continue having more frequent in-person appointments during the pandemic than patients prescribed only oral or no antipsychotic medications. The proportion of patients who did not have recommended tests increased from 41.0% to 72.4% for blood pressure readings, from 48.9% to 60.2% for hemoglobin A1c, from 57.0% to 67.8% for LDL cholesterol and 45.0% to 56.0% for CBC tests during the pandemic. CONCLUSIONS There were substantial decreases in preventive care after the onset of the pandemic, although primary care access was largely maintained through virtual care. Addressing these deficiencies will be essential to promoting health equity and reducing the risk of poor health outcomes.
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Affiliation(s)
- Ellen Stephenson
- Department of Family and Community Medicine, Temerty Faculty of Medicine, 7938University of Toronto, Toronto, Ontario, Canada
| | - Abban Yusuf
- MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Jessica Gronsbell
- Department of Statistical Sciences, 7938University of Toronto, Toronto, Ontario, Canada
| | - Karen Tu
- Department of Family and Community Medicine, Temerty Faculty of Medicine, 7938University of Toronto, Toronto, Ontario, Canada.,Department of Family and Community Medicine, 8613North York General Hospital, Toronto, Ontario, Canada.,Department of Family and Community Medicine, 26625Toronto Western Hospital, Toronto, Ontario, Canada
| | - Osnat Melamed
- Department of Family and Community Medicine, Temerty Faculty of Medicine, 7938University of Toronto, Toronto, Ontario, Canada.,Centre for Mental Health and Addiction (CAMH), Toronto, Ontario, Canada
| | - Tezeta Mitiku
- Department of Psychiatry, 6363University of Ottawa, Ottawa, Ontario, Canada
| | - Peter Selby
- Department of Family and Community Medicine, Temerty Faculty of Medicine, 7938University of Toronto, Toronto, Ontario, Canada.,Centre for Mental Health and Addiction (CAMH), Toronto, Ontario, Canada
| | - Braden O'Neill
- Department of Family and Community Medicine, Temerty Faculty of Medicine, 7938University of Toronto, Toronto, Ontario, Canada.,MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Family and Community Medicine, St. Michael's Hospital, 508783Unity Health Toronto, Toronto, Ontario, Canada
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Gronsbell J, Liu M, Tian L, Cai T. Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling. J R Stat Soc Series B Stat Methodol 2022; 84:1353-1391. [PMID: 36275859 PMCID: PMC9586151 DOI: 10.1111/rssb.12502] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labeled data is selected uniformly at random from the population of interest. Stratified sampling, while posing additional analytical challenges, is highly applicable to many real world problems. Moreover, no SSL methods currently exist for estimating the prediction performance of a fitted model when the labeled data is not selected uniformly at random. In this paper, we propose a two-step SSL procedure for evaluating a prediction rule derived from a working binary regression model based on the Brier score and overall misclassification rate under stratified sampling. In step I, we impute the missing labels via weighted regression with nonlinear basis functions to account for stratified sampling and to improve efficiency. In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model. The final estimator is then obtained with the augmented imputations. We provide asymptotic theory and numerical studies illustrating that our proposals outperform their supervised counterparts in terms of efficiency gain. Our methods are motivated by electronic health record (EHR) research and validated with a real data analysis of an EHR-based study of diabetic neuropathy.
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Affiliation(s)
- Jessica Gronsbell
- Jessica Gronsbell is an Assistant Professor in the Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, CA Molei Liu is a Ph.D. student in the Department of Biostatistics, Harvard University, Boston, MA 02115, USA Lu Tian is an Associate Professor, Department of Biomedical Data Science, Stanford University, Palo Alto, California 94305, U.S.A Tianxi Cai is a Professor, Department of Biostatistics, Harvard University, Boston, MA 02115, USA
- The first two authors are equal contributors to this work
| | - Molei Liu
- Jessica Gronsbell is an Assistant Professor in the Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, CA Molei Liu is a Ph.D. student in the Department of Biostatistics, Harvard University, Boston, MA 02115, USA Lu Tian is an Associate Professor, Department of Biomedical Data Science, Stanford University, Palo Alto, California 94305, U.S.A Tianxi Cai is a Professor, Department of Biostatistics, Harvard University, Boston, MA 02115, USA
- The first two authors are equal contributors to this work
| | - Lu Tian
- Jessica Gronsbell is an Assistant Professor in the Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, CA Molei Liu is a Ph.D. student in the Department of Biostatistics, Harvard University, Boston, MA 02115, USA Lu Tian is an Associate Professor, Department of Biomedical Data Science, Stanford University, Palo Alto, California 94305, U.S.A Tianxi Cai is a Professor, Department of Biostatistics, Harvard University, Boston, MA 02115, USA
| | - Tianxi Cai
- Jessica Gronsbell is an Assistant Professor in the Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, CA Molei Liu is a Ph.D. student in the Department of Biostatistics, Harvard University, Boston, MA 02115, USA Lu Tian is an Associate Professor, Department of Biomedical Data Science, Stanford University, Palo Alto, California 94305, U.S.A Tianxi Cai is a Professor, Department of Biostatistics, Harvard University, Boston, MA 02115, USA
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Kalia S, Greiver M, Sullivan F, Sejdic E, Escobar M, Gronsbell J, O'Neill B, Meaney C, Pow C, Saarela O, Moineddin R, Chen T, Aliarzadeh B. Marginal structural models using calibrated weights with SuperLearner: application to longitudinal diabetes cohort. Int J Popul Data Sci 2022. [DOI: 10.23889/ijpds.v7i3.1783] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
Abstract
Although machine learning has permeated many disciplines, the convergence of causal methods and machine learning remains sparse in the existing literature. Our aim was to formulate a marginal structural model in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2. We were interested in estimating “diabetes care provision” in next calendar year using a composite measure of chronic disease prevention and screening elements. We demonstrated the application of dynamic treatment regimes using the National Diabetes Action Canada Repository in which we applied a collection of mainstream statistical learning algorithms. We generated an ensemble of statistical learning algorithms using the SuperLearner based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population with respect to the marginalization of the time-dependent confounding process. The covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2) may improve diabetes care provision in relation to treatment naïve cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with diabetes through the improvement of diabetes care provisions in primary care.
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Kalia S, Saarela O, Chen T, O'Neill B, Meaney C, Gronsbell J, Sejdic E, Escobar M, Aliarzadeh B, Moineddin R, Pow C, Sullivan F, Greiver M. Marginal structural models using calibrated weights with SuperLearner: application to type II diabetes cohort. IEEE J Biomed Health Inform 2022; 26:4197-4206. [PMID: 35588417 DOI: 10.1109/jbhi.2022.3175862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records contained in National Diabetes Repository. In particular, we generated an ensemble of statistical learning algorithms using the SuperLearner framework based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, and (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population generated from the marginalization of the time-dependent confounding process. Covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2i) may have improved diabetes care provisions in relation to treatment naive (i.e. no treatment) cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with type II diabetes through the improvement of diabetes care provisions in primary care.
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Tu K, Sarkadi Kristiansson R, Gronsbell J, de Lusignan S, Flottorp S, Goh LH, Hallinan CM, Hoang U, Kang SY, Kim YS, Li Z, Ling ZJ, Manski-Nankervis JA, Ng APP, Pace WD, Wensaas KA, Wong WC, Stephenson E. Changes in primary care visits arising from the COVID-19 pandemic: an international comparative study by the International Consortium of Primary Care Big Data Researchers (INTRePID). BMJ Open 2022; 12:e059130. [PMID: 35534063 PMCID: PMC9086267 DOI: 10.1136/bmjopen-2021-059130] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
INTRODUCTION Through the INTernational ConsoRtium of Primary Care BIg Data Researchers (INTRePID), we compared the pandemic impact on the volume of primary care visits and uptake of virtual care in Australia, Canada, China, Norway, Singapore, South Korea, Sweden, the UK and the USA. METHODS Visit definitions were agreed on centrally, implemented locally across the various settings in INTRePID countries, and weekly visit counts were shared centrally for analysis. We evaluated the weekly rate of primary care physician visits during 2019 and 2020. Rate ratios (RRs) of total weekly visit volume and the proportion of weekly visits that were virtual in the pandemic period in 2020 compared with the same prepandemic period in 2019 were calculated. RESULTS In 2019 and 2020, there were 80 889 386 primary care physician visits across INTRePID. During the pandemic, average weekly visit volume dropped in China, Singapore, South Korea, and the USA but was stable overall in Australia (RR 0.98 (95% CI 0.92 to 1.05, p=0.59)), Canada (RR 0.96 (95% CI 0.89 to 1.03, p=0.24)), Norway (RR 1.01 (95% CI 0.88 to 1.17, p=0.85)), Sweden (RR 0.91 (95% CI 0.79 to 1.06, p=0.22)) and the UK (RR 0.86 (95% CI 0.72 to 1.03, p=0.11)). In countries that had negligible virtual care prepandemic, the proportion of visits that were virtual were highest in Canada (77.0%) and Australia (41.8%). In Norway (RR 8.23 (95% CI 5.30 to 12.78, p<0.001), the UK (RR 2.36 (95% CI 2.24 to 2.50, p<0.001)) and Sweden (RR 1.33 (95% CI 1.17 to 1.50, p<0.001)) where virtual visits existed prepandemic, it increased significantly during the pandemic. CONCLUSIONS The drop in primary care in-person visits during the pandemic was a global phenomenon across INTRePID countries. In several countries, primary care shifted to virtual visits mitigating the drop in in-person visits.
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Affiliation(s)
- Karen Tu
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Departments of Research and Innovation and Family Medicine-North York General Hospital, Toronto Western Family Health Team-University Health Network, Toronto, Ontario, Canada
| | | | - Jessica Gronsbell
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Signe Flottorp
- Norwegian Institute of Public Health, Oslo, Norway
- Department of General Practice, University of Oslo, Oslo, Norway
| | - Lay Hoon Goh
- Division of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Uy Hoang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Seo Young Kang
- International Healthcare Center, Asan Medical Center, Seoul, South Korea
| | - Young Sik Kim
- Department of Family Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Zhou Li
- Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Zheng Jye Ling
- Division of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Amy Pui Pui Ng
- Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | | | - Knut-Arne Wensaas
- Research Unit for General Practice, NORCE Norwegian Research Centre AS, Bergen, Norway
| | - William Cw Wong
- Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ellen Stephenson
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
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Stephenson E, Tu K, Ji C, Butt D, Crampton N, Gronsbell J, O'Neill B. Effects of COVID-19 pandemic on anxiety and depression in primary care: A cohort study in Ontario, Canada. Ann Fam Med 2022; 20:2911. [PMID: 35947415 PMCID: PMC10548956 DOI: 10.1370/afm.20.s1.2911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Context Many people have experienced poorer mental health and increased distress during the COVID-19 pandemic. It is unclear to what extent this has resulted in increases in the number of patients presenting with anxiety and/or depression in primary care. Objective To determine if there are more patients are visiting their family doctor for anxiety/depression during the COVID-19 pandemic compared to before the pandemic, and to determine whether these effects varied based on patient demographic characteristics. Study Design A retrospective cohort study of family medicine patients from 2017-2020. Data Source Electronic medical records (EMRs) from the University of Toronto Practice Based-Research Network (UTOPIAN) Data Safe Haven. The majority of physicians in the UTOPIAN EMR database practice in the Greater Toronto Area, a high-COVID region of Canada. Population Studied Active family practice patients aged 10 and older with at least 1 year of EMR data. Outcome Measures Visits for anxiety and/or depression; prescriptions for antidepressant medications. Results Changes in visits for anxiety and depression during the COVID-19 pandemic were consistent with an increased demand for mental healthcare and an increase in the number of individuals with anxiety and depression. Increases in visits for anxiety and depression were larger for younger patients, women, and later in the pandemic. Among younger patients, prescriptions for antidepressants were substantially reduced during the first few months of the pandemic (April-May 2020) but incidences rates increased later in 2020. Increases in visit volume during the pandemic were consist with more frequent visits for anxiety/depression and more new patients presenting with anxiety or depression. Conclusion The COVID-19 pandemic has resulted in an increased demand for mental health services from family physicians. Increases in anxiety and depression were especially pronounced among younger female patients and increased throughout the pandemic. Our findings highlight the need for continued efforts to support and addresses mental health concerns in primary care.
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Poole SF, Gronsbell J, Winter D, Nickels S, Levy R, Fu B, Burq M, Saeb S, Edwards MD, Behr MK, Kumaresan V, Macalalad AR, Shah S, Prevost M, Snoad N, Brenner MP, Myers LJ, Varghese P, Califf RM, Washington V, Lee VS, Fromer M. A holistic approach for suppression of COVID-19 spread in workplaces and universities. PLoS One 2021; 16:e0254798. [PMID: 34383766 PMCID: PMC8360595 DOI: 10.1371/journal.pone.0254798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 07/02/2021] [Indexed: 12/05/2022] Open
Abstract
As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the "Community-Workplace" model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and physical distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue. Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.
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Affiliation(s)
- Sarah F. Poole
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Jessica Gronsbell
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Dale Winter
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Stefanie Nickels
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Roie Levy
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Bin Fu
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Maximilien Burq
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Sohrab Saeb
- Verily Life Sciences, South San Francisco, CA, United States of America
| | | | - Michael K. Behr
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Vignesh Kumaresan
- Verily Life Sciences, South San Francisco, CA, United States of America
| | | | - Sneh Shah
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Michelle Prevost
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Nigel Snoad
- Verily Life Sciences, South San Francisco, CA, United States of America
| | | | - Lance J. Myers
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Paul Varghese
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Robert M. Califf
- Verily Life Sciences, South San Francisco, CA, United States of America
| | | | - Vivian S. Lee
- Verily Life Sciences, South San Francisco, CA, United States of America
| | - Menachem Fromer
- Verily Life Sciences, South San Francisco, CA, United States of America
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12
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Nickels S, Edwards MD, Poole SF, Winter D, Gronsbell J, Rozenkrants B, Miller DP, Fleck M, McLean A, Peterson B, Chen Y, Hwang A, Rust-Smith D, Brant A, Campbell A, Chen C, Walter C, Arean PA, Hsin H, Myers LJ, Marks WJ, Mega JL, Schlosser DA, Conrad AJ, Califf RM, Fromer M. Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling. JMIR Ment Health 2021; 8:e27589. [PMID: 34383685 PMCID: PMC8386379 DOI: 10.2196/27589] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/16/2021] [Accepted: 04/29/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. OBJECTIVE This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. METHODS A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). RESULTS A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary-derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). CONCLUSIONS This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.
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Affiliation(s)
| | | | - Sarah F Poole
- Verily Life Sciences, South San Francisco, CA, United States
| | - Dale Winter
- Verily Life Sciences, South San Francisco, CA, United States
| | | | | | - David P Miller
- Verily Life Sciences, South San Francisco, CA, United States
| | - Mathias Fleck
- Verily Life Sciences, South San Francisco, CA, United States
| | - Alan McLean
- Verily Life Sciences, South San Francisco, CA, United States
| | - Bret Peterson
- Verily Life Sciences, South San Francisco, CA, United States
| | - Yuanwei Chen
- Verily Life Sciences, South San Francisco, CA, United States
| | - Alan Hwang
- Verily Life Sciences, South San Francisco, CA, United States
| | | | - Arthur Brant
- Verily Life Sciences, South San Francisco, CA, United States
| | - Andrew Campbell
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Chen Chen
- Verily Life Sciences, South San Francisco, CA, United States
| | - Collin Walter
- Verily Life Sciences, South San Francisco, CA, United States
| | - Patricia A Arean
- Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, United States
| | - Honor Hsin
- Verily Life Sciences, South San Francisco, CA, United States
| | - Lance J Myers
- Verily Life Sciences, South San Francisco, CA, United States
| | - William J Marks
- Verily Life Sciences, South San Francisco, CA, United States
| | - Jessica L Mega
- Verily Life Sciences, South San Francisco, CA, United States
| | | | - Andrew J Conrad
- Verily Life Sciences, South San Francisco, CA, United States
| | - Robert M Califf
- Verily Life Sciences, South San Francisco, CA, United States
| | - Menachem Fromer
- Verily Life Sciences, South San Francisco, CA, United States
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13
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Stephenson E, O'Neill B, Gronsbell J, Butt DA, Crampton N, Ji C, Kalia S, Meaney C, Tu K. Changes in family medicine visits across sociodemographic groups after the onset of the COVID-19 pandemic in Ontario: a retrospective cohort study. CMAJ Open 2021; 9:E651-E658. [PMID: 34131028 PMCID: PMC8248562 DOI: 10.9778/cmajo.20210005] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND It has been suggested that the COVID-19 pandemic has worsened socioeconomic disparities in access to primary care. Given these concerns, we investigated whether the pandemic affected visits to family physicians differently across sociodemographic groups. METHODS We conducted a retrospective cohort study using electronic medical records from family physician practices within the University of Toronto Practice-Based Research Network. We evaluated primary care visits for a fixed cohort of patients who were active within the database as of Jan. 1, 2019, to estimate the number of patients who visited their family physician (visitor rate) and the number of distinct visits (visit volume) between Jan. 1, 2019, to June 30, 2020. We compared trends in visitor rate and visit volume during the pandemic (Mar. 14 to June 30, 2020) with the same period in the previous year (Mar. 14 to June 30, 2019) across sociodemographic factors, including age, sex, neighbourhood income, material deprivation and ethnic concentration. RESULTS We included 365 family physicians and 372 272 patients. Compared with the previous year, visitor rates during the pandemic period dropped by 34.5%, from 357 visitors per 1000 people to 292 visitors per 1000 people. Declines in visit volume during the pandemic were less pronounced (21.8% fewer visits), as the mean number of visits per patient increased during the pandemic (from 1.64 to 1.96). The declines in visitor rate and visit volume varied based on patient age and sex, but not socioeconomic status. INTERPRETATION Although the number of visits to family physicians dropped substantially during the first few weeks of the COVID-19 pandemic in Ontario, patients from communities with low socioeconomic status did not appear to be disproportionately affected. In this primary care setting, the pandemic appears not to have worsened socioeconomic disparities in access to care.
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Affiliation(s)
- Ellen Stephenson
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont.
| | - Braden O'Neill
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont
| | - Jessica Gronsbell
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont
| | - Debra A Butt
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont
| | - Noah Crampton
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont
| | - Catherine Ji
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont
| | - Sumeet Kalia
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont
| | - Christopher Meaney
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont
| | - Karen Tu
- Department of Family and Community Medicine (Stephenson, O'Neill, Butt, Crampton, Ji, Kalia, Meaney, Tu), University of Toronto; MAP Centre for Urban Health Solutions (O'Neill), St. Michael's Hospital; North York General Hospital (Tu); Department of Statistical Sciences (Gronsbell), University of Toronto; Scarborough Health Network (Butt); Toronto Western Hospital Family Health Team (Crampton, Ji, Tu), University Health Network, Toronto, Ont
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14
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Liao KP, Sun J, Cai TA, Link N, Hong C, Huang J, Huffman JE, Gronsbell J, Zhang Y, Ho YL, Castro V, Gainer V, Murphy SN, O'Donnell CJ, Gaziano JM, Cho K, Szolovits P, Kohane IS, Yu S, Cai T. High-throughput multimodal automated phenotyping (MAP) with application to PheWAS. J Am Med Inform Assoc 2021; 26:1255-1262. [PMID: 31613361 DOI: 10.1093/jamia/ocz066] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 04/08/2019] [Accepted: 04/26/2019] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). MATERIALS AND METHODS We developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the Unified Medical Language System. Along with health care utilization, aggregated ICD and NLP counts were jointly analyzed by fitting an ensemble of latent mixture models. The multimodal automated phenotyping (MAP) algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying participants with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort phenome-wide association studies (PheWAS) for 2 single nucleotide polymorphisms with known associations. RESULTS The MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes. CONCLUSION The MAP approach increased the accuracy of phenotype definition while maintaining scalability, thereby facilitating use in studies requiring large-scale phenotyping, such as PheWAS.
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Affiliation(s)
- Katherine P Liao
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Jiehuan Sun
- Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tianrun A Cai
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Nicholas Link
- Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jie Huang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | | | - Yichi Zhang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,University of Rhode Island, Kingston, RI, USA
| | - Yuk-Lam Ho
- Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | | | | | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Partners Healthcare Systems, Summerville, MA, USA.,Massachusetts General Hospital, Boston, MA, USA
| | - Christopher J O'Donnell
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - J Michael Gaziano
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Peter Szolovits
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sheng Yu
- Center for Statistical Science, Tsinghua University, Beijing, China.,Department of Industrial Engineering, Tsinghua University, Beijing, China.,Institute for Data Science, Tsinghua University, Beijing, China
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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15
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Gronsbell J, Tian L. Response to the letter by Guogen Shan, Hua Zhang, and Tao Jiang. Stat Med 2020; 39:3024-3025. [DOI: 10.1002/sim.8583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 04/09/2020] [Indexed: 11/10/2022]
Affiliation(s)
- Jessica Gronsbell
- Biomedical Data Science Stanford University School of Medicine Stanford California USA
| | - Lu Tian
- Biomedical Data Science Stanford University School of Medicine Stanford California USA
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16
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Gronsbell J, Hong C, Nie L, Lu Y, Tian L. Exact inference for the random-effect model for meta-analyses with rare events. Stat Med 2019; 39:252-264. [PMID: 31820458 DOI: 10.1002/sim.8396] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 09/14/2019] [Accepted: 09/20/2019] [Indexed: 11/10/2022]
Abstract
Meta-analysis allows for the aggregation of results from multiple studies to improve statistical inference for the parameter of interest. In recent years, random-effect meta-analysis has been employed to synthesize estimates of incidence rates of adverse events across heterogeneous clinical trials to evaluate treatment safety. However, the validity of existing approaches relies on asymptotic approximation as the number of studies becomes large. In practice, a limited number of trials are typically available for analysis. Moreover, adverse events are typically rare; thus, study-specific incidence rate estimates may be unstable or undefined. In this paper, we present a method for construction of an exact confidence interval for the location parameter of the beta-binomial model through inversion of exact tests. The coverage level of the proposed confidence interval is guaranteed to achieve at least the nominal level, regardless of the number of studies or the with-in study sample size, making it particularly applicable to the study of rare-event data.
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Affiliation(s)
- Jessica Gronsbell
- Department of Biomedical Data Science, Stanford University, Stanford, California
| | - Chuan Hong
- Department of Biostatistics, Harvard T.H. School of Public Health, Boston, Massachusetts
| | - Lei Nie
- DBII/OB/OTS/Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Ying Lu
- Department of Biomedical Data Science, Stanford University, Stanford, California
| | - Lu Tian
- Department of Biomedical Data Science, Stanford University, Stanford, California
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17
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Gronsbell J, Minnier J, Yu S, Liao K, Cai T. Automated feature selection of predictors in electronic medical records data. Biometrics 2019; 75:268-277. [PMID: 30353541 DOI: 10.1111/biom.12987] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Accepted: 10/01/2018] [Indexed: 01/29/2023]
Abstract
The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype data. Historically, EHR algorithms for annotating phenotypes have been either rule-based or trained with billing codes and gold standard labels curated via labor intensive medical chart review. These simplistic algorithms tend to have unpredictable portability across institutions and low accuracy for many disease phenotypes due to imprecise billing codes. Recently, more sophisticated machine learning algorithms have been developed to improve the robustness and accuracy of EHR phenotyping algorithms. These algorithms are typically trained via supervised learning, relating gold standard labels to a wide range of candidate features including billing codes, procedure codes, medication prescriptions and relevant clinical concepts extracted from narrative notes via Natural Language Processing (NLP). However, due to the time intensiveness of gold standard labeling, the size of the training set is often insufficient to build a generalizable algorithm with the large number of candidate features extracted from EHR. To reduce the number of candidate predictors and in turn improve model performance, we present an automated feature selection method based entirely on unlabeled observations. The proposed method generates a comprehensive surrogate for the underlying phenotype with an unsupervised clustering of disease status based on several highly predictive features such as diagnosis codes and mentions of the disease in text fields available in the entire set of EHR data. A sparse regression model is then built with the estimated outcomes and remaining covariates to identify those features most informative of the phenotype of interest. Relying on the results of Li and Duan (1989), we demonstrate that variable selection for the underlying phenotype model can be achieved by fitting the surrogate-based model. We explore the performance of our methods in numerical simulations and present the results of a prediction model for Rheumatoid Arthritis (RA) built on a large EHR data mart from the Partners Health System consisting of billing codes and NLP terms. Empirical results suggest that our procedure reduces the number of gold-standard labels necessary for phenotyping thereby harnessing the automated power of EHR data and improving efficiency.
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Affiliation(s)
- Jessica Gronsbell
- Department of Biomedical Data Science, Stanford University, Stanford, California
| | - Jessica Minnier
- OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, Oregon
| | - Sheng Yu
- Center for Statistical Science, Tsinghua University, Beijing, China
| | | | - Tianxi Cai
- Department of Biostatistics, Harvard University, Boston, Massachusetts
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18
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Yu S, Ma Y, Gronsbell J, Cai T, Ananthakrishnan AN, Gainer VS, Churchill SE, Szolovits P, Murphy SN, Kohane IS, Liao KP, Cai T. Enabling phenotypic big data with PheNorm. J Am Med Inform Assoc 2019; 25:54-60. [PMID: 29126253 DOI: 10.1093/jamia/ocx111] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 09/14/2017] [Indexed: 01/20/2023] Open
Abstract
Objective Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. Methods The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. Results We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference. Conclusion The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data.
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Affiliation(s)
- Sheng Yu
- Center for Statistical Science, Tsinghua University, Beijing, China.,Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Yumeng Ma
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Jessica Gronsbell
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tianrun Cai
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Vivian S Gainer
- Research Information Science and Computing, Partners HealthCare, Charlestown, MA, USA
| | - Susanne E Churchill
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Peter Szolovits
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shawn N Murphy
- Research Information Science and Computing, Partners HealthCare, Charlestown, MA, USA.,Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Katherine P Liao
- Department of Medicine, Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA
| | - Tianxi Cai
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Kumamaru KK, Kumamaru H, Bateman BT, Gronsbell J, Cai T, Liu J, Higgins LD, Aoki S, Ohtomo K, Rybicki FJ, Patorno E. Limited Hospital Variation in the Use and Yield of CT for Pulmonary Embolism in Patients Undergoing Total Hip or Total Knee Replacement Surgery. Radiology 2016; 281:826-834. [PMID: 27228331 DOI: 10.1148/radiol.2016152765] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To evaluate the variation among U.S. hospitals in overall use and yield of in-hospital computed tomographic (CT) pulmonary angiography (PA) in patients undergoing total hip replacement (THR) or total knee replacement (TKR) surgery. Materials and Methods Patients in the Premier Research Database who underwent elective TKR or THR between 2007 and 2011 were enrolled in this HIPAA-compliant, institutional review board-approved retrospective observational study. The informed consent requirement was waived. Hospitals were categorized into low, medium, and high tertiles of CT PA use to compare baseline patient- and hospital-level characteristics and pulmonary embolism (PE) positivity rates. To further investigate between-hospital variation in CT PA use, a hierarchical logistic regression model that included hospital-specific random effects and fixed patient- and hospital-level effects was used. The intraclass correlation coefficient (ICC) was used to measure the amount of variability in CT PA use attributable to between-hospital variation. Results The cohort included 205 198 patients discharged from 178 hospitals (median of 734.5 patients discharged per hospital; interquartile range, 316-1461 patients) with 3647 CT PA studies (1.8%). The crude frequency of CT PA scans among the hospitals ranged from 0% to 6.2% (median, 1.6%); more than 90% of the hospitals performed CT PA in less than 3% of their patients. The mean hospital-level PE positivity rate was 12.3% (median, 9.1%); there was no significant difference in PE positivity rate across low through high CT PA use tertiles (11.3%, 11.9%, 12.9%, P = .37). After adjustment for hospital- and patient-level factors, the remaining amount of interhospital variation was relatively low (ICC, 9.0%). Conclusion Limited interhospital variation in use and yield of in-hospital CT PA was observed among patients undergoing TKR or THR in the United States. © RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Kanako K Kumamaru
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Hiraku Kumamaru
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Brian T Bateman
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Jessica Gronsbell
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Tianxi Cai
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Jun Liu
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Laurence D Higgins
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Shigeki Aoki
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Kuni Ohtomo
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Frank J Rybicki
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
| | - Elisabetta Patorno
- From the Applied Imaging Science Laboratory, Department of Radiology (K.K.K., F.J.R.), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine (H.K., B.T.B., J.L., E.P.), and Department of Orthopedics (L.D.H.), Brigham and Women's Hospital & Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120; Department of Radiology, Juntendo University, Tokyo, Japan (K.K.K., S.A.); Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Mass (B.T.B.); Department of Biostatistics, Harvard University, Boston, Mass (J.G., T.C.); and Department of Radiology, University of Tokyo, Tokyo, Japan (K.O.)
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