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Aiyegbusi OL, Fenton A. The impact of rare kidney diseases on kidney failure. Lancet 2024; 403:1211-1213. [PMID: 38492577 DOI: 10.1016/s0140-6736(24)00198-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 01/31/2024] [Indexed: 03/18/2024]
Affiliation(s)
- Olalekan Lee Aiyegbusi
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK; NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; NIHR Applied Research Collaboration West Midlands, Birmingham, UK.
| | - Anthony Fenton
- Department of Renal Medicine, Royal Stoke University Hospital, Stoke-on-Trent, UK
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Koraishy FM, Mallipattu SK. Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics. FRONTIERS IN NEPHROLOGY 2023; 3:1266967. [PMID: 37965069 PMCID: PMC10641281 DOI: 10.3389/fneph.2023.1266967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/05/2023] [Indexed: 11/16/2023]
Abstract
The COVID-19 pandemic resulted in an unprecedented burden on intensive care units (ICUs). With increased demands and limited supply, critical care resources, including dialysis machines, became scarce, leading to the undertaking of value-based cost-effectiveness analyses and the rationing of resources to deliver patient care of the highest quality. A high proportion of COVID-19 patients admitted to the ICU required dialysis, resulting in a major burden on resources such as dialysis machines, nursing staff, technicians, and consumables such as dialysis filters and solutions and anticoagulation medications. Artificial intelligence (AI)-based big data analytics are now being utilized in multiple data-driven healthcare services, including the optimization of healthcare system utilization. Numerous factors can impact dialysis resource allocation to critically ill patients, especially during public health emergencies, but currently, resource allocation is determined using a small number of traditional factors. Smart analytics that take into account all the relevant healthcare information in the hospital system and patient outcomes can lead to improved resource allocation, cost-effectiveness, and quality of care. In this review, we discuss dialysis resource utilization in critical care, the impact of the COVID-19 pandemic, and how AI can improve resource utilization in future public health emergencies. Research in this area should be an important priority.
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Affiliation(s)
- Farrukh M. Koraishy
- Division of Nephrology, Department of Medicine, Stony Brook University Hospital, , Stony Brook, NY, United States
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Szili-Torok T, Kremer D, Bakker SJL, Tietge UJF, de Borst MH. Blockchain in nephrology. Nat Rev Nephrol 2023:10.1038/s41581-023-00707-y. [PMID: 36964226 DOI: 10.1038/s41581-023-00707-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Affiliation(s)
- Tamas Szili-Torok
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Daan Kremer
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stephan J L Bakker
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Uwe J F Tietge
- Division of Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- Clinical Chemistry, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden
| | - Martin H de Borst
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Saito R, Yamamoto H, Ichihara N, Kumamaru H, Nishimura S, Shimada K, Mori K, Miyachi Y, Miyata H. Persistence of tolvaptan medication for autosomal dominant polycystic kidney disease: A retrospective cohort study using Shizuoka Kokuho Database. Medicine (Baltimore) 2022; 101:e30923. [PMID: 36221375 PMCID: PMC9542978 DOI: 10.1097/md.0000000000030923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is a rare hereditary disease leading to end-stage renal failure in approximately half of patients by seventy years of age. It is important to continuously take tolvaptan to control disease progression. However, adherence to tolvaptan in a real-world setting, rather than randomized controlled trials (RCTs), has not been sufficiently reported. We aimed to investigate tolvaptan persistence among patients with ADPKD using a large claims database. Using the Shizuoka Kokuho Database, we identified patients diagnosed with ADPKD who were prescribed tolvaptan from March 2014-September 2018 in Japan. The persistence rate of tolvaptan medication was estimated by Kaplan-Meier analysis, and patient background factors associated with treatment discontinuation were exploratively evaluated with log-rank tests. We identified 1714 eligible patients with ADPKD, and among them, 25 patients used tolvaptan medication. We followed up these patients, whose median treatment duration was 21 months. The persistence rates at 12, 24, and 36 months were estimated to be 70.8% (95% confidence interval: 48.2-93.4), 46.5% (23.2-66.9), and 38.7% (16.4-60.8), respectively. In the exploratory analysis, there were no factors that were obviously associated with tolvaptan discontinuation. The persistence rate of tolvaptan in patients with ADPKD in a real-world setting may be lower than that in previous RCTs. Our innovative method, particularly in Japan, to analyze adherence using large claims data should change the way clinical epidemiological research and health policies of rare diseases are designed in the future.
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Affiliation(s)
- Ryuta Saito
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka City, Japan
- Department of Health Policy and Management, School of Medicine, Keio University, Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Hiroyuki Yamamoto
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka City, Japan
- Department of Health Policy and Management, School of Medicine, Keio University, Shinanomachi, Shinjuku-ku, Tokyo, Japan
- *Correspondence: Hiroyuki Yamamoto, Shizuoka Graduate University of Public Health, 4-27-2 Kita Ando Aoi-ku, Shizuoka City, 420-0881, Japan (e-mail: )
| | - Nao Ichihara
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka City, Japan
- Department of Healthcare Quality Assessment, The University of Tokyo Graduate School of Medicine, Hongo, Bunkyo-ku, Tokyo, Japan
| | - Hiraku Kumamaru
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka City, Japan
- Department of Healthcare Quality Assessment, The University of Tokyo Graduate School of Medicine, Hongo, Bunkyo-ku, Tokyo, Japan
| | - Shiori Nishimura
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka City, Japan
- Department of Healthcare Quality Assessment, The University of Tokyo Graduate School of Medicine, Hongo, Bunkyo-ku, Tokyo, Japan
| | - Koki Shimada
- Department of Health Policy and Management, School of Medicine, Keio University, Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Kiyoshi Mori
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka City, Japan
| | - Yoshiki Miyachi
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka City, Japan
| | - Hiroaki Miyata
- Shizuoka Graduate University of Public Health, Shizuoka, Shizuoka City, Japan
- Department of Health Policy and Management, School of Medicine, Keio University, Shinanomachi, Shinjuku-ku, Tokyo, Japan
- Department of Healthcare Quality Assessment, The University of Tokyo Graduate School of Medicine, Hongo, Bunkyo-ku, Tokyo, Japan
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