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Khalid F, Alsadoun L, Khilji F, Mushtaq M, Eze-Odurukwe A, Mushtaq MM, Ali H, Farman RO, Ali SM, Fatima R, Bokhari SFH. Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches. Cureus 2024; 16:e60145. [PMID: 38864072 PMCID: PMC11166249 DOI: 10.7759/cureus.60145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2024] [Indexed: 06/13/2024] Open
Abstract
Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.
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Affiliation(s)
- Fizza Khalid
- Nephrology, Sharif Medical City Hospital, Lahore, PAK
| | - Lara Alsadoun
- Trauma and Orthopedics, Chelsea and Westminster Hospital, London, GBR
| | - Faria Khilji
- Internal Medicine, Tehsil Headquarter Hospital, Shakargarh, PAK
- Internal Medicine, Quaid-e-Azam Medical College, Bahawalpur, PAK
| | - Maham Mushtaq
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | | | | | - Husnain Ali
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | - Rana Omer Farman
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | - Syed Momin Ali
- Medicine and Surgery, King Edward Medical University, Lahore, PAK
| | - Rida Fatima
- Medicine and Surgery, Fatima Jinnah Medical University, Lahore, PAK
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Hoang AT, Nguyen PA, Phan TP, Do GT, Nguyen HD, Chiu IJ, Chou CL, Ko YC, Chang TH, Huang CW, Iqbal U, Hsu YH, Wu MS, Liao CT. Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach. BMJ Health Care Inform 2024; 31:e100893. [PMID: 38677774 PMCID: PMC11057266 DOI: 10.1136/bmjhci-2023-100893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 04/16/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.
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Affiliation(s)
- Anh Trung Hoang
- Nephro-Urology and Dialysis Center, Bach Mai Hospital, Hanoi, Vietnam
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Thanh Phuc Phan
- International PhD program of Biotech and Healthcare Management,College of Management, Taipei Medical University, Taipei, Taiwan
- University Medical Center, Ho Chi Minh City, Vietnam
| | - Gia Tuyen Do
- Nephro-Urology and Dialysis Center, Bach Mai Hospital, Hanoi, Vietnam
- Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam
| | - Huu Dung Nguyen
- Nephro-Urology and Dialysis Center, Bach Mai Hospital, Hanoi, Vietnam
| | - I-Jen Chiu
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- TMU-Research Center of Urology and Kidney (TMU-RCUK), Taipei Medical University, Taipei, Taiwan
| | - Chu-Lin Chou
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- TMU-Research Center of Urology and Kidney (TMU-RCUK), Taipei Medical University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Hsin Kuo Min Hospital, Taipei Medical University, Taoyuan City, Taiwan
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Yu-Chen Ko
- Division of Cardiovascular Surgery, Department of Surgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Chih-Wei Huang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Usman Iqbal
- School of Population Health, Faculty of Medicine and Health, University of New South Wales (UNSW), Sydney, New South Wales, Australia
- Global Health & Health Security Department, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Yung-Ho Hsu
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- TMU-Research Center of Urology and Kidney (TMU-RCUK), Taipei Medical University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Hsin Kuo Min Hospital, Taipei Medical University, Taoyuan City, Taiwan
| | - Mai-Szu Wu
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- TMU-Research Center of Urology and Kidney (TMU-RCUK), Taipei Medical University, Taipei, Taiwan
| | - Chia-Te Liao
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- TMU-Research Center of Urology and Kidney (TMU-RCUK), Taipei Medical University, Taipei, Taiwan
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Saito H, Yoshimura H, Tanaka K, Kimura H, Watanabe K, Tsubokura M, Ejiri H, Zhao T, Ozaki A, Kazama S, Shimabukuro M, Asahi K, Watanabe T, Kazama JJ. Predicting CKD progression using time-series clustering and light gradient boosting machines. Sci Rep 2024; 14:1723. [PMID: 38242985 PMCID: PMC10798962 DOI: 10.1038/s41598-024-52251-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 01/16/2024] [Indexed: 01/21/2024] Open
Abstract
Predicting the transition of kidney function in chronic kidney disease is difficult as specific symptoms are lacking and often overlooked, and progress occurs due to complicating factors. In this study, we applied time-series cluster analysis and a light gradient boosting machine to predict the trajectories of kidney function in non-dialysis dependent chronic kidney disease patients with baseline estimated glomerular filtration rate (GFR) ≥ 45 mL/min/1.73 m2. Based on 5-year changes in estimated GFR, participants were stratified into groups with similar trajectories by cluster analysis. Next, we applied the light gradient boosting machine algorithm and Shapley addictive explanation to develop a prediction model for clusters and identify important parameters for prediction. Data from 780 participants were available for analysis. Participants were classified into five classes (Class 1: n = 78, mean [± standard deviation] estimated GFR 100 ± 19.3 mL/min/1.73 m2; Class 2: n = 176, 76.0 ± 9.3 mL/min/1.73 m2; Class 3: n = 191, 59.8 ± 5.9 mL/min/1.73 m2; Class 4: n = 261, 52.7 ± 4.6 mL/min/1.73 m2; and Class 5: n = 74, 53.5 ± 12.0 mL/min/1.73 m2). Declines in estimated GFR were 8.9% in Class 1, 12.2% in Class 2, 4.9% in Class 3, 12.0% in Class 4, and 45.1% in Class 5 during the 5-year period. The accuracy of prediction was 0.675, and the top three most important Shapley addictive explanation values were 1.61 for baseline estimated GFR, 0.12 for hemoglobin, and 0.11 for body mass index. The estimated GFR transition of patients with preserved chronic kidney disease mostly depended on baseline estimated GFR, and the borderline for estimated GFR trajectory was nearly 50 mL/min/1.73 m2.
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Affiliation(s)
- Hirotaka Saito
- Department of Nephrology and Hypertension, Fukushima Medical University, 1 Hikariga-Oka, Fukushima City, Fukushima, 960-1295, Japan
| | - Hiroki Yoshimura
- Department of Radiation Health Management, Fukushima Medical University, Fukushima, Japan
| | - Kenichi Tanaka
- Department of Nephrology and Hypertension, Fukushima Medical University, 1 Hikariga-Oka, Fukushima City, Fukushima, 960-1295, Japan.
- Division of Advanced Community Based Care for Lifestyle Related Diseases, Fukushima Medical University, Fukushima, Japan.
| | - Hiroshi Kimura
- Department of Nephrology and Hypertension, Fukushima Medical University, 1 Hikariga-Oka, Fukushima City, Fukushima, 960-1295, Japan
- Division of Advanced Community Based Care for Lifestyle Related Diseases, Fukushima Medical University, Fukushima, Japan
| | - Kimio Watanabe
- Department of Nephrology and Hypertension, Fukushima Medical University, 1 Hikariga-Oka, Fukushima City, Fukushima, 960-1295, Japan
| | - Masaharu Tsubokura
- Department of Radiation Health Management, Fukushima Medical University, Fukushima, Japan
| | - Hiroki Ejiri
- Department of Nephrology and Hypertension, Fukushima Medical University, 1 Hikariga-Oka, Fukushima City, Fukushima, 960-1295, Japan
| | - Tianchen Zhao
- Department of Radiation Health Management, Fukushima Medical University, Fukushima, Japan
| | - Akihiko Ozaki
- Department of Thyroid and Endocrinology, Fukushima Medical University, Fukushima, Japan
| | - Sakumi Kazama
- Division of Advanced Community Based Care for Lifestyle Related Diseases, Fukushima Medical University, Fukushima, Japan
| | - Michio Shimabukuro
- Division of Advanced Community Based Care for Lifestyle Related Diseases, Fukushima Medical University, Fukushima, Japan
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University, Fukushima, Japan
| | - Koichi Asahi
- Division of Advanced Community Based Care for Lifestyle Related Diseases, Fukushima Medical University, Fukushima, Japan
- Division of Nephrology and Hypertension, Iwate Medical University, Yahaba, Japan
| | - Tsuyoshi Watanabe
- Division of Advanced Community Based Care for Lifestyle Related Diseases, Fukushima Medical University, Fukushima, Japan
| | - Junichiro J Kazama
- Department of Nephrology and Hypertension, Fukushima Medical University, 1 Hikariga-Oka, Fukushima City, Fukushima, 960-1295, Japan
- Division of Advanced Community Based Care for Lifestyle Related Diseases, Fukushima Medical University, Fukushima, Japan
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Takkavatakarn K, Oh W, Cheng E, Nadkarni GN, Chan L. Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4. BMC Nephrol 2023; 24:376. [PMID: 38114923 PMCID: PMC10731874 DOI: 10.1186/s12882-023-03424-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
INTRODUCTION End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care planning and optimizing healthcare resource allocation. METHODS We obtained electronic health record data from patients with CKD4 in a large health system between January 1, 2006, and December 31, 2016. We developed and validated four models, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network (ANN), to predict ESKD at 3 years. We utilized area under the receiver operating characteristic curve (AUROC) to evaluate model performances and utilized Shapley additive explanation (SHAP) values and plots to define feature dependence of the best performance model. RESULTS We included 3,160 patients with CKD4. ESKD was observed in 538 patients (21%). All approaches had similar AUROCs; ANN yielded the highest AUROC (0.77; 95%CI 0.75 to 0.79) and LASSO regression (0.77; 95%CI 0.75 to 0.79), followed by random forest (0.76; 95% CI 0.74 to 0.79), and XGBoost (0.76; 95% CI 0.74 to 0.78). CONCLUSIONS We developed and validated several models for near-term prediction of kidney failure in CKD4. ANN, random forest, and XGBoost demonstrated similar predictive performances. Using this suite of models, interventions can be customized based on risk, and population health and resources appropriately allocated.
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Affiliation(s)
- Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand
| | - Wonsuk Oh
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ella Cheng
- The Cooper Union for the Advancement of Science and Art, New York, NY, USA
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Zhu Y, Bi D, Saunders M, Ji Y. Prediction of chronic kidney disease progression using recurrent neural network and electronic health records. Sci Rep 2023; 13:22091. [PMID: 38086905 PMCID: PMC10716428 DOI: 10.1038/s41598-023-49271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 12/06/2023] [Indexed: 12/18/2023] Open
Abstract
Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.
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Affiliation(s)
- Yitan Zhu
- Computing, Environment and Life Sciences, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA.
| | - Dehua Bi
- Department of Public Health Sciences, The University of Chicago, 5841 South Maryland Ave, MC 2000, Chicago, IL, 60637, USA
| | - Milda Saunders
- Department of Medicine, The University of Chicago, 5841 South Maryland Ave, MC 2007, Chicago, IL, 60637, USA
| | - Yuan Ji
- Department of Public Health Sciences, The University of Chicago, 5841 South Maryland Ave, MC 2000, Chicago, IL, 60637, USA.
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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 2023; 36:1101-1117. [PMID: 36786976 PMCID: PMC10227138 DOI: 10.1007/s40620-023-01573-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. METHODS We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. RESULTS From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. CONCLUSIONS Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
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Yaghoubi M, Cressman S, Edwards L, Shechter S, Doyle-Waters MM, Keown P, Sapir-Pichhadze R, Bryan S. A Systematic Review of Kidney Transplantation Decision Modelling Studies. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2023; 21:39-51. [PMID: 35945483 DOI: 10.1007/s40258-022-00744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/19/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Genome-based precision medicine strategies promise to minimize premature graft loss after renal transplantation, through precision approaches to immune compatibility matching between kidney donors and recipients. The potential adoption of this technology calls for important changes to clinical management processes and allocation policy. Such potential policy change decisions may be supported by decision models from health economics, comparative effectiveness research and operations management. OBJECTIVE We used a systematic approach to identify and extract information about models published in the kidney transplantation literature and provide an overview of the status of our collective model-based knowledge about the kidney transplant process. METHODS Database searches were conducted in MEDLINE, Embase, Web of Science and other sources, for reviews and primary studies. We reviewed all English-language papers that presented a model that could be a tool to support decision making in kidney transplantation. Data were extracted on the clinical context and modelling methods used. RESULTS A total of 144 studies were included, most of which focused on a single component of the transplantation process, such as immunosuppressive therapy or donor-recipient matching and organ allocation policies. Pre- and post-transplant processes have rarely been modelled together. CONCLUSION A whole-disease modelling approach is preferred to inform precision medicine policy, given its potential upstream implementation in the treatment pathway. This requires consideration of pre- and post-transplant natural history, risk factors for allograft dysfunction and failure, and other post-transplant outcomes. Our call is for greater collaboration across disciplines and whole-disease modelling approaches to more accurately simulate complex policy decisions about the integration of precision medicine tools in kidney transplantation.
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Affiliation(s)
- Mohsen Yaghoubi
- Department of Pharmacy Practice, Mercer University College of Pharmacy, Atlanta, USA
| | - Sonya Cressman
- Faculty of Health Sciences, Simon Fraser University, School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Louisa Edwards
- School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada
| | - Steven Shechter
- Sauder School of Business, University of British Columbia, Vancouver, Canada
| | - Mary M Doyle-Waters
- Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
| | - Paul Keown
- Department of Medicine, Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | | | - Stirling Bryan
- School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, Canada.
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Kamio T, Ikegami M, Machida Y, Uemura T, Chino N, Iwagami M. Machine learning-based prognostic modeling of patients with acute heart failure receiving furosemide in intensive care units. Digit Health 2023; 9:20552076231194933. [PMID: 37576718 PMCID: PMC10422900 DOI: 10.1177/20552076231194933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose This study developed machine learning models to predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation in patients with acute heart failure receiving furosemide in intensive care units. Method An extensive database comprising static and dynamic features obtained from a Japanese hospital chain was used to construct and train the machine learning models. Results The results revealed that the proposed machine learning models predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation with good accuracy. However, the optimal models vary depending on the predicted outcomes. The linear support vector machine classification models exhibited the highest in-hospital mortality and mechanical ventilation prediction accuracy, with the area under the receiver operating characteristic curve of 0.73 and 0.73, respectively, whereas the multi-layer neural network exhibited the highest accuracy for acute renal replacement therapy initiation prediction with an area under the receiver operating characteristic curve of 0.70. Conclusions In conclusion, this study demonstrated that machine learning models could help predict the clinical outcomes of patients with acute heart failure receiving furosemide. However, the optimal models may differ depending on the outcome of interest.
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Affiliation(s)
- Tadashi Kamio
- Division of Critical Care, Shonan Kamakura General Hospital, Kanagawa, Japan
| | - Masaru Ikegami
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Yoshihito Machida
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Tomoko Uemura
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Naotaka Chino
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Masao Iwagami
- Department of Health Services Research, University of Tsukuba, Ibaraki, Japan
- Health Services Research and Development Center, University of Tsukuba, Ibaraki, Japan
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK
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Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:2053-2061. [PMID: 35989154 DOI: 10.1016/j.jval.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/10/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
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Affiliation(s)
- Woojung Lee
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
| | - Naomi Schwartz
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Sara Khor
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Noah Hammarlund
- Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Beth Devine
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
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Su CT, Chang YP, Ku YT, Lin CM. Machine Learning Models for the Prediction of Renal Failure in Chronic Kidney Disease: A Retrospective Cohort Study. Diagnostics (Basel) 2022; 12:diagnostics12102454. [PMID: 36292142 PMCID: PMC9600783 DOI: 10.3390/diagnostics12102454] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/08/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022] Open
Abstract
This study assessed the feasibility of five separate machine learning (ML) classifiers for predicting disease progression in patients with pre-dialysis chronic kidney disease (CKD). The study enrolled 858 patients with CKD treated at a veteran’s hospital in Taiwan. After classification into early and advanced stages, patient demographics and laboratory data were processed and used to predict progression to renal failure and important features for optimal prediction were identified. The random forest (RF) classifier with synthetic minority over-sampling technique (SMOTE) had the best predictive performances among patients with early-stage CKD who progressed within 3 and 5 years and among patients with advanced-stage CKD who progressed within 1 and 3 years. Important features identified for predicting progression from early- and advanced-stage CKD were urine creatinine and serum creatinine levels, respectively. The RF classifier demonstrated the optimal performance, with an area under the receiver operating characteristic curve values of 0.96 for predicting progression within 5 years in patients with early-stage CKD and 0.97 for predicting progression within 1 year in patients with advanced-stage CKD. The proposed method resulted in the optimal prediction of CKD progression, especially within 1 year of advanced-stage CKD. These results will be useful for predicting prognosis among patients with CKD.
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Affiliation(s)
- Chuan-Tsung Su
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan 333, Taiwan
| | - Yi-Ping Chang
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan 333, Taiwan
- Department of Nephrology, Taoyuan Branch of Taipei Veterans General Hospital, Taoyuan 330, Taiwan
| | - Yuh-Ting Ku
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan 333, Taiwan
| | - Chih-Ming Lin
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan 333, Taiwan
- Correspondence: ; Tel.: +886-3-350-7001 (ext. 3530); Fax: +886-3-359-3880
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11
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Gottlieb ER, Samuel M, Bonventre JV, Celi LA, Mattie H. Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit. Adv Chronic Kidney Dis 2022; 29:431-438. [PMID: 36253026 PMCID: PMC9586459 DOI: 10.1053/j.ackd.2022.06.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/01/2022] [Accepted: 06/22/2022] [Indexed: 01/25/2023]
Abstract
Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.
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Affiliation(s)
- Eric R Gottlieb
- Renal Section, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA.
| | | | - Joseph V Bonventre
- Renal Section, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Leo A Celi
- Harvard Medical School, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; MIT Critical Data, Cambridge, MA; Harvard T.H. Chan School of Public Health, Boston, MA; Beth Israel Deaconess Medical Center, Boston, MA
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12
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Lei N, Zhang X, Wei M, Lao B, Xu X, Zhang M, Chen H, Xu Y, Xia B, Zhang D, Dong C, Fu L, Tang F, Wu Y. Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2022; 22:205. [PMID: 35915457 PMCID: PMC9341041 DOI: 10.1186/s12911-022-01951-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 07/18/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression. METHODS We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, the Chinese Biomedicine Literature Database, Chinese National Knowledge Infrastructure, Wanfang Database, and the VIP Database for diagnostic studies on ML algorithms' accuracy in predicting kidney disease prognosis, from the establishment of these databases until October 2020. Two investigators independently evaluate study quality by QUADAS-2 tool and extracted data from single ML algorithm for data synthesis using the bivariate model and the hierarchical summary receiver operating characteristic (HSROC) model. RESULTS Fifteen studies were left after screening, only 6 studies were eligible for data synthesis. The sample size of these 6 studies was 12,534, and the kidney disease types could be divided into chronic kidney disease (CKD) and Immunoglobulin A Nephropathy, with 5 articles using end-stage renal diseases occurrence as the primary outcome. The main results indicated that the area under curve (AUC) of the HSROC was 0.87 (0.84-0.90) and ML algorithm exhibited a strong specificity, 95% confidence interval and heterogeneity (I2) of (0.87, 0.84-0.90, [I2 99.0%]) and a weak sensitivity of (0.68, 0.58-0.77, [I2 99.7%]) in predicting kidney disease deterioration. And the the results of subgroup analysis indicated that ML algorithm's AUC for predicting CKD prognosis was 0.82 (0.79-0.85), with the pool sensitivity of (0.64, 0.49-0.77, [I2 99.20%]) and pool specificity of (0.84, 0.74-0.91, [I2 99.84%]). The ML algorithm's AUC for predicting IgA nephropathy prognosis was 0.78 (0.74-0.81), with the pool sensitivity of (0.74, 0.71-0.77, [I2 7.10%]) and pool specificity of (0.93, 0.91-0.95, [I2 83.92%]). CONCLUSION Taking advantage of big data, ML algorithm-based prediction models have high accuracy in predicting kidney disease progression, we recommend ML algorithms as an auxiliary tool for clinicians to determine proper treatment and disease management strategies.
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Affiliation(s)
- Nuo Lei
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xianlong Zhang
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Mengting Wei
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Beini Lao
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xueyi Xu
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Min Zhang
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huifen Chen
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yanmin Xu
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bingqing Xia
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Dingjun Zhang
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chendi Dong
- The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lizhe Fu
- Chronic Disease Management Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fang Tang
- Chronic Disease Management Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yifan Wu
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
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13
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Lim DKE, Boyd JH, Thomas E, Chakera A, Tippaya S, Irish A, Manuel J, Betts K, Robinson S. Prediction models used in the progression of chronic kidney disease: A scoping review. PLoS One 2022; 17:e0271619. [PMID: 35881639 PMCID: PMC9321365 DOI: 10.1371/journal.pone.0271619] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 07/04/2022] [Indexed: 11/19/2022] Open
Abstract
Objective
To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).
Design
Scoping review.
Data sources
Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.
Study selection
All English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression.
Data extraction
Eligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications.
Results
From 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models.
Conclusions
Researchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.
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Affiliation(s)
- David K. E. Lim
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- * E-mail:
| | - James H. Boyd
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- La Trobe University, Melbourne, Bundoora, VIC, Australia
| | - Elizabeth Thomas
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- Medical School, The University of Western Australia, Perth, WA, Australia
| | - Aron Chakera
- Medical School, The University of Western Australia, Perth, WA, Australia
- Renal Unit, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Sawitchaya Tippaya
- Curtin Institute for Computation, Curtin University, Perth, WA, Australia
| | | | | | - Kim Betts
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
| | - Suzanne Robinson
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- Deakin Health Economics, Deakin University, Burwood, VIC, Australia
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Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7378307. [PMID: 35399848 PMCID: PMC8993553 DOI: 10.1155/2022/7378307] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/10/2022] [Accepted: 03/21/2022] [Indexed: 12/17/2022]
Abstract
Background Diabetic kidney disease (DKD), one of the complications of diabetes in patients, leads to progressive loss of kidney function. Timely intervention is known to improve outcomes. Therefore, screening patients to identify high-risk populations is important. Machine learning classification techniques can be applied to patient datasets to identify high-risk patients by building a predictive model. Objective This study aims to identify a suitable classification technique for predicting DKD by applying different classification techniques to a DKD dataset and comparing their performance using WEKA machine learning software. Methods The performance of nine different classification techniques was analyzed on a DKD dataset with 410 instances and 18 attributes. Data preprocessing was carried out using the PartitionMembershipFilter. A 10-fold cross validation was performed on the dataset. The performance was assessed on the basis of the execution time, accuracy, correctly and incorrectly classified instances, kappa statistics (K), mean absolute error, root mean squared error, and true values of the confusion matrix. Results With an accuracy of 93.6585% and a higher K value (0.8731), IBK and random tree classification techniques were found to be the best performing techniques. Moreover, they also exhibited the lowest root mean squared error rate (0.2496). There were 15 false-positive instances and 11 false-negative instances with these prediction models. Conclusions This study identified IBK and random tree classification techniques as the best performing classifiers and accurate prediction methods for DKD.
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15
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Lee J, Warner E, Shaikhouni S, Bitzer M, Kretzler M, Gipson D, Pennathur S, Bellovich K, Bhat Z, Gadegbeku C, Massengill S, Perumal K, Saha J, Yang Y, Luo J, Zhang X, Mariani L, Hodgin JB, Rao A. Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease. Sci Rep 2022; 12:4832. [PMID: 35318420 PMCID: PMC8941143 DOI: 10.1038/s41598-022-08974-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/14/2022] [Indexed: 12/22/2022] Open
Abstract
Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95% confidence interval. We show that this method is reliable and reproducible and can achieve 0.93 AUC at predicting glomerular filtration rate at the time of biopsy as well as predicting a loss of function at one year. Additionally, with this method, we ranked the identified morphological features according to their importance as diagnostic markers for chronic kidney disease. In this study, we have demonstrated the feasibility of using an unsupervised machine learning method without human input in order to predict the level of kidney function in CKD. The results from our study indicate that the visual dictionary, or visual image pattern, obtained from unsupervised machine learning can predict outcomes using machine-derived values that correspond to both known and unknown clinically relevant features.
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Affiliation(s)
- Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Elisa Warner
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Salma Shaikhouni
- Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Markus Bitzer
- Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Matthias Kretzler
- Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Debbie Gipson
- Department of Pediatrics, Pediatric Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Subramaniam Pennathur
- Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Keith Bellovich
- Department of Internal Medicine, Nephrology, St. Clair Nephrology Research, Detroit, MI, USA
| | - Zeenat Bhat
- Department of Internal Medicine, Nephrology, Wayne State University, Detroit, MI, USA
| | - Crystal Gadegbeku
- Department of Internal Medicine, Nephrology, Cleveland Clinic, Cleveland, OH, USA
| | - Susan Massengill
- Department of Pediatrics, Pediatric Nephrology, Levine Children's Hospital, Charlotte, NC, USA
| | - Kalyani Perumal
- Department of Internal Medicine, Nephrology, Department of JH Stroger Hospital, Chicago, IL, USA
| | - Jharna Saha
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Yingbao Yang
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Jinghui Luo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Xin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Laura Mariani
- Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Jeffrey B Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
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16
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Feng X, Hong T, Liu W, Xu C, Li W, Yang B, Song Y, Li T, Li W, Zhou H, Yin C. Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma. Front Endocrinol (Lausanne) 2022; 13:1054358. [PMID: 36465636 PMCID: PMC9716136 DOI: 10.3389/fendo.2022.1054358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/28/2022] [Indexed: 11/21/2022] Open
Abstract
SIMPLE SUMMARY Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects. BACKGROUND Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer. METHODS Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds. RESULTS The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients. CONCLUSIONS The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.
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Affiliation(s)
- Xiaowei Feng
- Department of Neuro Rehabilitation, Shaanxi Provincial Rehabilitation Hospital, Xi ‘an, China
| | - Tao Hong
- Department of Cardiac Surgery, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Shenzhen, China
| | - Wencai Liu
- Department of Orthopaedic Surgery, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chan Xu
- Department of Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Wanying Li
- Department of Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Bing Yang
- Life Science Department, Tianjin Prosel Biological Technology Co., Ltd, Tianjin, China
| | - Yang Song
- Department of Gastroenterology and Hepatology, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Ting Li
- Department of Cell Biology, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Wenle Li
- Department of Neuro Rehabilitation, Shaanxi Provincial Rehabilitation Hospital, Xi ‘an, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Fujian, China
- *Correspondence: Chengliang Yin, ; Hui Zhou, ; Wenle Li,
| | - Hui Zhou
- School of Pharmacy, Tianjin Medical University, Tianjin, China
- *Correspondence: Chengliang Yin, ; Hui Zhou, ; Wenle Li,
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR China
- *Correspondence: Chengliang Yin, ; Hui Zhou, ; Wenle Li,
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Samal L, Fu HN, Camara DS, Wang J, Bierman AS, Dorr DA. Health information technology to improve care for people with multiple chronic conditions. Health Serv Res 2021; 56 Suppl 1:1006-1036. [PMID: 34363220 PMCID: PMC8515226 DOI: 10.1111/1475-6773.13860] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps. DATA SOURCES We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020. STUDY DESIGN We identified studies of health IT interventions for PLWMCC across three domains as follows: self-management support, care coordination, and algorithms to support clinical decision making. DATA COLLECTION/EXTRACTION METHODS Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers. PRINCIPAL FINDINGS The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi-experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient-centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality. CONCLUSIONS Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.
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Affiliation(s)
- Lipika Samal
- Brigham and Women's HospitalBostonMAUSA
- Harvard Medical SchoolBostonMAUSA
| | - Helen N. Fu
- Indiana University Richard M. Fairbanks School of Public HealthIndianapolisINUSA
- Regenstrief InstituteCenter for Biomedical InformaticsIndianapolisINUSA
| | - Djibril S. Camara
- Center for Disease Control and Prevention, Center for Surveillance, Epidemiology, and Laboratory Services (CSELS) Division of Scientific Education and Professional Development, Public Health Informatics Fellowship ProgramAtlantaGeorgiaUSA
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
| | - Jing Wang
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
- Florida State University College of NursingTallahasseeFloridaUSA
- Health and Aging Policy Fellows Program at Columbia UniversityNew YorkNYUSA
| | - Arlene S. Bierman
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
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18
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Maniruzzaman M, Islam MM, Rahman MJ, Hasan MAM, Shin J. Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data. Diabetes Metab Syndr 2021; 15:102263. [PMID: 34482122 DOI: 10.1016/j.dsx.2021.102263] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 11/27/2022]
Abstract
AIMS This research work presented a comparative study of machine learning (ML), including two objectives: (i) determination of the risk factors of diabetic nephropathy (DN) based on principal component analysis (PCA) via different cutoffs; (ii) prediction of DN patients using ML-based techniques. METHODS The combination of PCA and ML-based techniques has been implemented to select the best features at different PCA cutoff values and choose the optimal PCA cutoff in which ML-based techniques give the highest accuracy. These optimum features are fed into six ML-based techniques: linear discriminant analysis, support vector machine (SVM), logistic regression, K-nearest neighborhood, naïve Bayes, and artificial neural network. The leave-one-out cross-validation protocol is executed and compared ML-based techniques performance using accuracy and area under the curve (AUC). RESULTS The data utilized in this work consists of 133 respondents having 73 DN patients with an average age of 69.6±10.2 years and 54.2% of DN patients are female. Our findings illustrate that PCA combined with SVM-RBF classifier yields 88.7% accuracy and 0.91 AUC at 0.96 PCA cutoff. CONCLUSIONS This study also suggests that PCA combined with SVM-RBF classifier may correctly classify DN patients with the highest accuracy when compared to the models published in the existing research. Prospective studies are warranted to further validate the applicability of our model in clinical settings.
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Affiliation(s)
- Md Maniruzzaman
- Statistics Discipline, Khulna University, Khulna, Bangladesh.
| | - Md Merajul Islam
- Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
| | - Md Jahanur Rahman
- Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
| | - Md Al Mehedi Hasan
- Department of Computer Science & Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh; School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Fukushima, Japan.
| | - Jungpil Shin
- School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Fukushima, Japan.
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19
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Huang YC, Li SJ, Chen M, Lee TS. The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients. Healthcare (Basel) 2021; 9:710. [PMID: 34200785 PMCID: PMC8230367 DOI: 10.3390/healthcare9060710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 11/17/2022] Open
Abstract
Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals' medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.
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Affiliation(s)
- Yen-Chun Huang
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan;
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Shao-Jung Li
- Cardiovascular Research Center, Wan Fang Hospital, Taipei Medical University, Taipei City 116, Taiwan;
- Taipei Heart Institute, Taipei Medical University, New Taipei City 231, Taiwan
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 116, Taiwan
- Division of Cardiovascular Surgery, Department of Surgery, Wan Fang Hospital, Taipei Medical University, Taipei City 116, Taiwan
| | - Mingchih Chen
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan;
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Tian-Shyug Lee
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan;
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan. Healthcare (Basel) 2021; 9:healthcare9050546. [PMID: 34067129 PMCID: PMC8151834 DOI: 10.3390/healthcare9050546] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/29/2021] [Accepted: 04/29/2021] [Indexed: 01/26/2023] Open
Abstract
Chronic kidney disease (CKD) represents a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data obtained from Taiwan's National Health Insurance Research Database to forecast the occurrence of CKD within the next 6 or 12 months before its onset, and hence its prevalence in the population. A total of 18,000 people with CKD and 72,000 people without CKD diagnosis were selected using propensity score matching. Their demographic, medication and comorbidity data from their respective two-year observation period were used to build a predictive model. Among the approaches investigated, the Convolutional Neural Networks (CNN) model performed best with a test set AUROC of 0.957 and 0.954 for the 6-month and 12-month predictions, respectively. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The models can allow close monitoring of people at risk, early detection of CKD, better allocation of resources, and patient-centric management.
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21
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JAMTHIKAR AD, PUVVULA A, GUPTA D, JOHRI AM, NAMBI V, KHANNA NN, SABA L, MAVROGENI S, LAIRD JR, PAREEK G, MINER M, SFIKAKIS PP, PROTOGEROU A, KITAS GD, NICOLAIDES A, SHARMA AM, VISWANATHAN V, RATHORE VS, KOLLURI R, BHATT DL, SURI JS. Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review. INT ANGIOL 2021; 40:150-164. [DOI: 10.23736/s0392-9590.20.04538-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Owens E, Tan KS, Ellis R, Del Vecchio S, Humphries T, Lennan E, Vesey D, Healy H, Hoy W, Gobe G. Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease. Biomedicines 2020; 8:E606. [PMID: 33327377 PMCID: PMC7764886 DOI: 10.3390/biomedicines8120606] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/09/2020] [Accepted: 12/10/2020] [Indexed: 12/12/2022] Open
Abstract
Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%).
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Affiliation(s)
- Evan Owens
- NHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, Australia; (E.O.); (K.-S.T.); (H.H.)
- Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia; (R.E.); (S.D.V.); (T.H.); (D.V.)
- Kidney Disease Research Collaborative, Translational Research Institute, Princess Alexandra Hospital, The University of Queensland, Brisbane 4102, Australia
| | - Ken-Soon Tan
- NHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, Australia; (E.O.); (K.-S.T.); (H.H.)
- Renal Medicine, Metro South Hospital and Health Service, Logan Hospital, Meadowbrook 4131, Australia;
| | - Robert Ellis
- Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia; (R.E.); (S.D.V.); (T.H.); (D.V.)
- Kidney Disease Research Collaborative, Translational Research Institute, Princess Alexandra Hospital, The University of Queensland, Brisbane 4102, Australia
| | - Sharon Del Vecchio
- Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia; (R.E.); (S.D.V.); (T.H.); (D.V.)
- Kidney Disease Research Collaborative, Translational Research Institute, Princess Alexandra Hospital, The University of Queensland, Brisbane 4102, Australia
| | - Tyrone Humphries
- Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia; (R.E.); (S.D.V.); (T.H.); (D.V.)
- Kidney Disease Research Collaborative, Translational Research Institute, Princess Alexandra Hospital, The University of Queensland, Brisbane 4102, Australia
| | - Erica Lennan
- Renal Medicine, Metro South Hospital and Health Service, Logan Hospital, Meadowbrook 4131, Australia;
| | - David Vesey
- Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia; (R.E.); (S.D.V.); (T.H.); (D.V.)
- Kidney Disease Research Collaborative, Translational Research Institute, Princess Alexandra Hospital, The University of Queensland, Brisbane 4102, Australia
| | - Helen Healy
- NHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, Australia; (E.O.); (K.-S.T.); (H.H.)
- Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia; (R.E.); (S.D.V.); (T.H.); (D.V.)
- Kidney Health Service, Royal Brisbane and Women’s Hospital, Brisbane 4029, Australia
| | - Wendy Hoy
- NHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, Australia; (E.O.); (K.-S.T.); (H.H.)
- Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia; (R.E.); (S.D.V.); (T.H.); (D.V.)
- Centre for Chronic Disease, Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia
| | - Glenda Gobe
- NHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, Australia; (E.O.); (K.-S.T.); (H.H.)
- Faculty of Medicine, The University of Queensland, Brisbane 4067, Australia; (R.E.); (S.D.V.); (T.H.); (D.V.)
- Kidney Disease Research Collaborative, Translational Research Institute, Princess Alexandra Hospital, The University of Queensland, Brisbane 4102, Australia
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