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Bramlage P, Lanzinger S, Hess E, Fahrner S, Heyer CHJ, Friebe M, Buschmann I, Danne T, Holl RW, Seufert J. Renal function deterioration in adult patients with type-2 diabetes. BMC Nephrol 2020; 21:312. [PMID: 32727401 PMCID: PMC7391505 DOI: 10.1186/s12882-020-01952-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 07/15/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To explore, in a large group of patients with type-2 diabetes (T2DM), renal function decline in terms of the slope of the estimated glomerular filtration rate (eGFR) over time, and to find out how classical risk factors, such as the presence of hypertension, dyslipidemia and microalbuminuria, affect the renal function. METHODS The analysis included 32,492 adult T2DM patients from the DIVE/DPV registries who had serial eGFR determinations and information on the presence of microalbuminuria, hypertension and dyslipidemia available. RESULTS Patients had a mean age of 66.3 years, 52.6% were male with a mean BMI of 31.7 kg/m2. The mean eGFR was 78.4 ± 21.4 mL/min/1.73m2. The results showed that the prevalence of renal function impairment understood as chronic kidney disease (CKD) is considerable (53.0%) in a population of patients with T2DM and has a high incidence rate of 6.6% within a year. Serial determinations of the eGFR are, however, infrequent (7.8% of all patients) and these patients are characterised by the presence of a high-risk profile for CKD, such as hypertension (88.1%) and dyslipidemia (66.1%). Over a three-year time period, 30.9% of the patients had an eGFR slope of -12 mL/min/1.73m2 or more; and more than a doubled proportion of patients with an eGFR < 30 mL/min/1.73 m2 (3.8% vs. 1.8%; p < 0.001). Hypertension and albuminuria contributed to renal function decline while dyslipidemia did not negatively affect the slope. CONCLUSION CKD is highly prevalent in patients with T2DM. Serial surveillance of the glomerular filtration rate is, however, not established in clinical practice, which would be necessary as indicated by a doubling of patients with an eGFR < 30 mL/min/1.73 m2 within 3 years. Moreover, the use of renin-angiotensin blocking agents was low, pointing at considerable room for improvement. Taken together we conclude that a closer surveillance of patients with diabetes based on the presence of further risk factors is mandatory combined with a mandatory prescription of RAS blocking agents once microalbuminuria and / or renal function deterioration develops.
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Affiliation(s)
- Peter Bramlage
- Institute for Pharmacology and Preventive Medicine, Bahnhofstrasse 20, 49661 Cloppenburg, Germany
| | - Stefanie Lanzinger
- Institut für Epidemiologie und medizinische Biometrie, ZIBMT, Universität Ulm, Ulm, Germany
- Deutsches Zentrum für Diabetesforschung e.V, München-Neuherberg, Germany
| | - Eva Hess
- Diabetologische Schwerpunktpraxis Dres, Hess, Worms, Germany
| | - Simon Fahrner
- Medizinische Klinik, SRH Klinik Sigmaringen, Pfullendorf, Germany
| | | | | | - Ivo Buschmann
- Department of Angiology, Medical University of Brandenburg, Brandenburg, Germany
| | - Thomas Danne
- Kinderkrankenhaus auf der Bult, Diabeteszentrum für Kinder und Jugendliche, Hannover, Germany
| | - Reinhard W. Holl
- Institut für Epidemiologie und medizinische Biometrie, ZIBMT, Universität Ulm, Ulm, Germany
- Deutsches Zentrum für Diabetesforschung e.V, München-Neuherberg, Germany
| | - Jochen Seufert
- Universitätsklinikum Freiburg, Medizinische Fakultät, Freiburg, Germany
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Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020; 9:jcm9041107. [PMID: 32294906 PMCID: PMC7230205 DOI: 10.3390/jcm9041107] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 02/07/2023] Open
Abstract
Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Karthik Kovvuru
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Swetha R. Kanduri
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85721, USA;
| | - Api Chewcharat
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Napat Leeaphorn
- Department of Nephrology, Department of Medicine, Saint Luke’s Health System, Kansas City, MO 64111, USA;
| | - Maria L. Gonzalez-Suarez
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
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Bello AK, Ronksley PE, Tangri N, Kurzawa J, Osman MA, Singer A, Grill AK, Nitsch D, Queenan JA, Wick J, Lindeman C, Soos B, Tuot DS, Shojai S, Brimble KS, Mangin D, Drummond N. Quality of Chronic Kidney Disease Management in Canadian Primary Care. JAMA Netw Open 2019; 2:e1910704. [PMID: 31483474 PMCID: PMC6727682 DOI: 10.1001/jamanetworkopen.2019.10704] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
IMPORTANCE Although patients with chronic kidney disease (CKD) are routinely managed in primary care settings, no nationally representative study has assessed the quality of care received by these patients in Canada. OBJECTIVE To evaluate the current state of CKD management in Canadian primary care practices to identify care gaps to guide development and implementation of national quality improvement initiatives. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study leveraged Canadian Primary Care Sentinel Surveillance Network data from January 1, 2010, to December 31, 2015, to develop a cohort of 46 162 patients with CKD managed in primary care practices. Data analysis was performed from August 8, 2018, to July 31, 2019. MAIN OUTCOMES AND MEASURES The study examined the proportion of patients with CKD who met a set of 12 quality indicators in 6 domains: (1) detection and recognition of CKD, (2) testing and monitoring of kidney function, (3) use of recommended medications, (4) monitoring after initiation of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs), (5) management of blood pressure, and (6) monitoring for glycemic control in those with diabetes and CKD. The study also analyzed associations of divergence from these quality indicators. RESULTS The cohort comprised 46 162 patients (mean [SD] age, 69.2 [14.0] years; 25 855 [56.0%] female) with stage 3 to 5 CKD. Only 4 of 12 quality indicators were met by 75% or more of the study cohort. These indicators were receipt of an outpatient serum creatinine test within 18 months after confirmation of CKD, receipt of blood pressure measurement at any time during follow-up, achieving a target blood pressure of 140/90 mm Hg or lower, and receiving a hemoglobin A1c test for monitoring diabetes during follow-up. Indicators in the domains of detection and recognition of CKD, testing and monitoring of kidney function (specifically, urine albumin to creatinine ratio testing), use of recommended medications, and appropriate monitoring after initiation of treatment with ACEIs or ARBs were not met. Only 6529 patients (18.4%) with CKD received a urine albumin test within 6 months of CKD diagnosis, and 3954 (39.4%) had a second measurement within 6 months of an abnormal baseline urine albumin level. Older age (≥85 years) and CKD stage 5 were significantly associated with not satisfying the criteria for the quality indicators across all domains. Across age categories, younger patients (aged 18-49 years) and older patients (≥75 years) were less likely to be tested for albuminuria (314 of 1689 patients aged 18-49 years [18.5%], 1983 of 11 919 patients aged 75-84 years [61.6%], and 614 of 5237 patients aged ≥85 years [11.7%] received the urine albumin to creatinine ratio test within 6 months of initial estimated glomerular filtration rate <60 mL/min per 1.73 m2; P < .001). Patients aged 18 to 49 years were less commonly prescribed recommended medications (222 of 2881 [7.7%]), whereas patients aged 75 to 84 years were prescribed ACEIs or ARBs most frequently (2328 of 5262 [44.2%]; P < .001). CONCLUSIONS AND RELEVANCE The findings suggest that management of CKD across primary care practices in Canada varies according to quality indicator. This study revealed potential priority areas for quality improvement initiatives in Canadian primary care practices.
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Affiliation(s)
- Aminu K. Bello
- Division of Nephrology and Immunology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Paul E. Ronksley
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Navdeep Tangri
- Department of Medicine, Max Rady College of Medicine, Winnipeg, Manitoba, Canada
| | - Julia Kurzawa
- Division of Nephrology and Immunology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Mohamed A. Osman
- Division of Nephrology and Immunology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Alexander Singer
- Department of Family Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Allan K. Grill
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - John A. Queenan
- Canadian Primary Care Sentinel Surveillance Network, Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada
| | - James Wick
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Cliff Lindeman
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Boglarka Soos
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
- Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Delphine S. Tuot
- Division of Nephrology, University of California, San Francisco
- Kidney Health Research Institute, University of California, San Francisco
| | - Soroush Shojai
- Division of Nephrology and Immunology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - K. Scott Brimble
- Division of Nephrology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Dee Mangin
- Department of Family Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Neil Drummond
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
- Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada
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Wang J, Bao B, Shen P, Kong G, Yang Y, Sun X, Ding G, Gao B, Yang C, Zhao M, Lin H, Zhang L. Using electronic health record data to establish a chronic kidney disease surveillance system in China: protocol for the China Kidney Disease Network (CK-NET)-Yinzhou Study. BMJ Open 2019; 9:e030102. [PMID: 31467053 PMCID: PMC6719833 DOI: 10.1136/bmjopen-2019-030102] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 07/02/2019] [Accepted: 07/25/2019] [Indexed: 01/04/2023] Open
Abstract
INTRODUCTION Chronic kidney disease (CKD) is an important public health problem worldwide. However, there are few active disease surveillance systems for it. The China Kidney Disease Network (CK-NET) was established as a comprehensive surveillance system for CKD using various data sources. As part of this, the proposed CK-NET-Yinzhou study aims to build a regional surveillance system in a developed coastal area in China to obtain detailed dynamic information about kidney disease and to improve the ability to manage the disease effectively. METHODS AND ANALYSIS Yinzhou is a district of Ningbo city, Zhejiang province. The district has a population of more than 1 million. By 2016, 98% were registered in a regional health information system that started in 2009. This system includes administrative databases containing general demographic characteristics, health check information, inpatient and outpatient electronic medical records, health insurance information, disease surveillance and management information, and death certificates. We will use longitudinal individual electronic health record data to identify people with CKD by repeated laboratory measurements and diagnostic codes. We will also evaluate the associated risk factors, prognosis and disease management. An intelligent clinical decision support system (CDSS) will be developed based on clinical guidelines, domain expert knowledge and real-world data, and will be integrated into the hospital information system. ETHICS AND DISSEMINATION The CK-NET-Yinzhou study has been reviewed and approved by the Peking University First Hospital Ethics Committee. Privacy of local residents registered with the health information system will be tightly protected through the study process. The findings of the study will be disseminated through peer-reviewed journal articles, posters and presentations in national and international scientific conferences, as well as among local practitioners through the CDSS.
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Affiliation(s)
- Jinwei Wang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
| | - Beiyan Bao
- Renal Division, Ningbo Yinzhou No. 2 Hospital, Ningbo, Zhejiang, China
| | - Peng Shen
- Yinzhou District Center for Disease Control and Prevention, Ningbo, China
| | - Guilan Kong
- Center for Data Science in Health and Medicine, Peking University, Beijing, Beijing, China
| | - Yu Yang
- Center for Data Science in Health and Medicine, Peking University, Beijing, Beijing, China
| | - Xiaoyu Sun
- Center for Data Science in Health and Medicine, Peking University, Beijing, Beijing, China
| | - Guohui Ding
- College of Computer Science, Shenyang Aerospace University, Shenyang, China
| | - Bixia Gao
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
| | - Chao Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
| | - Minghui Zhao
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Beijing, China
| | - Hongbo Lin
- Yinzhou District Center for Disease Control and Prevention, Ningbo, China
| | - Luxia Zhang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China
- Center for Data Science in Health and Medicine, Peking University, Beijing, Beijing, China
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Yang C, Kong G, Wang L, Zhang L, Zhao MH. Big data in nephrology: Are we ready for the change? Nephrology (Carlton) 2019; 24:1097-1102. [PMID: 31314170 DOI: 10.1111/nep.13636] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/09/2019] [Indexed: 01/25/2023]
Abstract
Chronic kidney disease (CKD) is a major public health issue worldwide. However, the status of kidney health care needs to be strengthened globally and research evidence in nephrology is relatively limited. The unmet needs in nephrology leave ample space for imagination regarding leveraging big data and artificial intelligence (AI). Big data has potential to drive medical innovation, reduce medical costs and improve health care quality. Compared with other specialties such as cardiology, the scopes of utilizing big data in nephrology need to be enhanced. We reviewed the studies on the application of big data in nephrology, such as disease surveillance, risk prediction and clinical decision support systems (CDSS), and proposed several potential directions of utilizing big data and AI. The efforts including building a CKD surveillance system and collaborative network, implementing a real-world cohort in a cost-effective manner, strengthening the application and transformation of AI and CDSS, and stimulating the activeness of medical imaging in nephrology, could be considered. In the era of big data, a nephrologist would be stronger and smarter if he or she could get intelligent assistance from knowledge or big data-driven CDSS.
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Affiliation(s)
- Chao Yang
- Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China
| | - Guilan Kong
- National Institute of Health Data Science at Peking University, Beijing, China
| | - Liwei Wang
- Key Laboratory of Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | - Luxia Zhang
- Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China.,National Institute of Health Data Science at Peking University, Beijing, China
| | - Ming-Hui Zhao
- Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Beijing, China
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Bello AK, Ronksley PE, Tangri N, Kurzawa J, Osman MA, Singer A, Grill A, Nitsch D, Queenan JA, Wick J, Lindeman C, Soos B, Tuot DS, Shojai S, Brimble S, Mangin D, Drummond N. Prevalence and Demographics of CKD in Canadian Primary Care Practices: A Cross-sectional Study. Kidney Int Rep 2019; 4:561-570. [PMID: 30993231 PMCID: PMC6451150 DOI: 10.1016/j.ekir.2019.01.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 12/16/2018] [Accepted: 01/07/2019] [Indexed: 11/24/2022] Open
Abstract
Introduction Surveillance systems enable optimal care delivery and appropriate resource allocation, yet Canada lacks a dedicated surveillance system for chronic kidney disease (CKD). Using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a national chronic disease surveillance system, this study describes the geographic, sociodemographic, and clinical variations in CKD prevalence in the Canadian primary care context. Methods This cross-sectional study included 559,745 adults in primary care in 5 provinces across Canada from 2010 through 2015. Data were analyzed by geographic (urban or rural residence), sociodemographic (age, sex, deprivation index), and clinical (medications prescribed, comorbid conditions) factors, using data from CPCSSN and the Canadian Deprivation Index. CKD stage 3 or higher was defined as 2 estimated glomerular filtration rate (eGFR) values of <60 ml/min per 1.73 m2 more than 90 days apart as of January 1, 2015. Results Prevalence of CKD was 71.9 per 1000 individuals and varied by geography, with the highest prevalence in rural settings compared with urban settings (86.2 vs. 68.4 per 1000). CKD was highly prevalent among individuals with 3 or more other chronic diseases (281.7 per 1000). Period prevalence of CKD indicated a slight decline over the study duration, from 53.4 per 1000 in 2010 to 46.5 per 1000 in 2014. Conclusion This is the first study to estimate the prevalence of CKD in primary care in Canada at a national level. Results may facilitate further research, prioritization of care, and quality improvement activities to identify gaps and improvement in CKD care.
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Affiliation(s)
- Aminu K Bello
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Paul E Ronksley
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
| | - Navdeep Tangri
- Department of Medicine, Max Rady College of Medicine, Winnipeg, MB, Canada
| | - Julia Kurzawa
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Mohamed A Osman
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Alexander Singer
- Department of Family Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Allan Grill
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - John A Queenan
- Canadian Primary Care Sentinel Surveillance Network, Department of Family Medicine, Queen's University, Kingston, ON, Canada
| | - James Wick
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Cliff Lindeman
- Department of Family Medicine, University of Alberta, Edmonton, AB, Canada
| | - Boglarka Soos
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.,Department of Family Medicine, University of Calgary, Calgary, AB, Canada
| | - Delphine S Tuot
- Division of Nephrology, University of California, San Francisco, California, USA.,Kidney Health Research Institute, University of California, San Francisco, California, USA
| | - Soroush Shojai
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Scott Brimble
- Division of Nephrology, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Dee Mangin
- Department of Family Medicine, McMaster University, Hamilton, ON, Canada
| | - Neil Drummond
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.,Department of Family Medicine, University of Alberta, Edmonton, AB, Canada.,Department of Family Medicine, University of Calgary, Calgary, AB, Canada
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Enabling informed policymaking for chronic kidney disease with a registry: Initiatory steps in Iran and the path forward. HEALTH POLICY AND TECHNOLOGY 2018. [DOI: 10.1016/j.hlpt.2018.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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