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Zoccali C, Mallamaci F, Lightstone L, Jha V, Pollock C, Tuttle K, Kotanko P, Wiecek A, Anders HJ, Remuzzi G, Kalantar-Zadeh K, Levin A, Vanholder R. A new era in the science and care of kidney diseases. Nat Rev Nephrol 2024:10.1038/s41581-024-00828-y. [PMID: 38575770 DOI: 10.1038/s41581-024-00828-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2024] [Indexed: 04/06/2024]
Abstract
Notable progress in basic, translational and clinical nephrology research has been made over the past five decades. Nonetheless, many challenges remain, including obstacles to the early detection of kidney disease, disparities in access to care and variability in responses to existing and emerging therapies. Innovations in drug development, research technologies, tissue engineering and regenerative medicine have the potential to improve patient outcomes. Exciting prospects include the availability of new drugs to slow or halt the progression of chronic kidney disease, the development of bioartificial kidneys that mimic healthy kidney functions, and tissue engineering techniques that could enable transplantable kidneys to be created from the cells of the recipient, removing the risk of rejection. Cell and gene therapies have the potential to be applied for kidney tissue regeneration and repair. In addition, about 30% of kidney disease cases are monogenic and could potentially be treated using these genetic medicine approaches. Systemic diseases that involve the kidney, such as diabetes mellitus and hypertension, might also be amenable to these treatments. Continued investment, communication, collaboration and translation of innovations are crucial to realize their full potential. In addition, increasing sophistication in exploring large datasets, implementation science, and qualitative methodologies will improve the ability to deliver transformational kidney health strategies.
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Affiliation(s)
- Carmine Zoccali
- Kidney Research Institute, New York City, NY, USA.
- Institute of Molecular Biology and Genetics (Biogem), Ariano Irpino, Italy.
- Associazione Ipertensione Nefrologia Trapianto Kidney (IPNET), c/o Nefrologia, Grande Ospedale Metropolitano, Reggio Calabria, Italy.
| | - Francesca Mallamaci
- Nephrology, Dialysis and Transplantation Unit Azienda Ospedaliera "Bianchi-Melacrino-Morelli", Reggio Calabria, Italy
- CNR-IFC, Institute of Clinical Physiology, Research Unit of Clinical Epidemiology and Physiopathology of Kidney Diseases and Hypertension of Reggio Calabria, Reggio Calabria, Italy
| | - Liz Lightstone
- Department of Immunology and Inflammation, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, UK
| | - Vivek Jha
- George Institute for Global Health, UNSW, New Delhi, India
- School of Public Health, Imperial College, London, UK
- Prasanna School of Public Health, Manipal Academy of Medical Education, Manipal, India
| | - Carol Pollock
- Kolling Institute, Royal North Shore Hospital University of Sydney, Sydney, NSW, Australia
| | - Katherine Tuttle
- Providence Medical Research Center, Providence Inland Northwest, Spokane, Washington, USA
- Department of Medicine, University of Washington, Seattle, Spokane, Washington, USA
- Kidney Research Institute, Institute of Translational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Peter Kotanko
- Kidney Research Institute, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrzej Wiecek
- Department of Nephrology, Transplantation and Internal Medicine, Medical University of Silesia, 40-027, Katowice, Poland
| | - Hans Joachim Anders
- Division of Nephrology, Department of Medicine IV, Hospital of the Ludwig Maximilians University Munich, Munich, Germany
| | - Giuseppe Remuzzi
- Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Bergamo, Italy
| | - Kamyar Kalantar-Zadeh
- Harold Simmons Center for Kidney Disease Research and Epidemiology, California, USA
- Division of Nephrology and Hypertension, University of California Irvine, School of Medicine, Orange, Irvine, USA
- Veterans Affairs Healthcare System, Division of Nephrology, Long Beach, California, USA
| | - Adeera Levin
- University of British Columbia, Vancouver General Hospital, Division of Nephrology, Vancouver, British Columbia, Canada
- British Columbia, Provincial Kidney Agency, Vancouver, British Columbia, Canada
| | - Raymond Vanholder
- European Kidney Health Alliance, Brussels, Belgium
- Nephrology Section, Department of Internal Medicine and Paediatrics, University Hospital Ghent, Ghent, Belgium
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Ma L, Zhang C, Gao J, Jiao X, Yu Z, Zhu Y, Wang T, Ma X, Wang Y, Tang W, Zhao X, Ruan W, Wang T. Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients. PATTERNS (NEW YORK, N.Y.) 2023; 4:100892. [PMID: 38106617 PMCID: PMC10724364 DOI: 10.1016/j.patter.2023.100892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/18/2023] [Accepted: 11/10/2023] [Indexed: 12/19/2023]
Abstract
The study aims to develop AICare, an interpretable mortality prediction model, using electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) patients. AICare includes a multichannel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AICare outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships and variations in feature importance and provides reference values. An AI-doctor interaction system is developed for visualizing patients' health trajectories and risk indicators.
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Affiliation(s)
| | | | - Junyi Gao
- Centre for Medical Informatics, University of Edinburgh, Edinburgh, UK
- Health Data Research UK, London, UK
| | | | | | | | | | - Xinyu Ma
- Peking University, Beijing, China
| | | | - Wen Tang
- Department of Nephrology, Peking University Third Hospital, Beijing, China
| | - Xinju Zhao
- Department of Nephrology, Peking University People’s Hospital, Beijing, China
| | - Wenjie Ruan
- Department of Computer Science, University of Exeter, Exeter, UK
| | - Tao Wang
- Department of Nephrology, Peking University Third Hospital, Beijing, China
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Kaufman HW, Wang C, Wang Y, Han H, Chaudhuri S, Usvyat L, Hahn Contino C, Kossmann R, Kraus MA. Machine Learning Case Study: Patterns of Kidney Function Decline and Their Association With Clinical Outcomes Within 90 Days After the Initiation of Renal Dialysis. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:33-39. [PMID: 36723279 DOI: 10.1053/j.akdh.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/28/2022] [Accepted: 11/16/2022] [Indexed: 01/20/2023]
Abstract
A case study explores patterns of kidney function decline using unsupervised learning methods first and then associating patterns with clinical outcomes using supervised learning methods. Predicting short-term risk of hospitalization and death prior to renal dialysis initiation may help target high-risk patients for more aggressive management. This study combined clinical data from patients presenting for renal dialysis at Fresenius Medical Care with laboratory data from Quest Diagnostics to identify disease trajectory patterns associated with the 90-day risk of hospitalization and death after beginning renal dialysis. Patients were clustered into 4 groups with varying rates of estimated glomerular filtration rate (eGFR) decline during the 2-year period prior to dialysis. Overall rates of hospitalization and death were 24.9% (582/2341) and 4.6% (108/2341), respectively. Groups with the steepest declines had the highest rates of hospitalization and death within 90 days of dialysis initiation. The rate of eGFR decline is a valuable and readily available tool to stratify short-term (90 days) risk of hospitalization and death after the initiation of renal dialysis. More intense approaches are needed that apply models that identify high risks to potentially avert or reduce short-term hospitalization and death of patients with a severe and rapidly progressive chronic kidney disease.
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Affiliation(s)
| | - Catherine Wang
- Statistics and Data Science, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, PA
| | - Yuedong Wang
- Department of Statistics and Applied Probability, College of Letters and Science, University of California - Santa Barbara, Santa Barbara, CA
| | - Hao Han
- Fresenius Medical Care, Waltham, MA
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