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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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Chang TH, Chen YD, Lu HHS, Wu JL, Mak K, Yu CS. Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning. Medicine (Baltimore) 2024; 103:e37112. [PMID: 38363886 PMCID: PMC10869094 DOI: 10.1097/md.0000000000037112] [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: 10/17/2023] [Accepted: 01/09/2024] [Indexed: 02/18/2024] Open
Abstract
Chronic kidney disease (CKD) is a major public health concern. But there are limited machine learning studies on non-cancer patients with advanced CKD, and the results of machine learning studies on cancer patients with CKD may not apply directly on non-cancer patients. We aimed to conduct a comprehensive investigation of risk factors for a 3-year risk of death among non-cancer advanced CKD patients with an estimated glomerular filtration rate < 60.0 mL/min/1.73m2 by several machine learning algorithms. In this retrospective cohort study, we collected data from in-hospital and emergency care patients from 2 hospitals in Taiwan from 2009 to 2019, including their international classification of disease at admission and laboratory data from the hospital's electronic medical records (EMRs). Several machine learning algorithms were used to analyze the potential impact and degree of influence of each factor on mortality and survival. Data from 2 hospitals in northern Taiwan were collected with 6565 enrolled patients. After data cleaning, 26 risk factors and approximately 3887 advanced CKD patients from Shuang Ho Hospital were used as the training set. The validation set contained 2299 patients from Taipei Medical University Hospital. Predictive variables, such as albumin, PT-INR, and age, were the top 3 significant risk factors with paramount influence on mortality prediction. In the receiver operating characteristic curve, the random forest had the highest values for accuracy above 0.80. MLP, and Adaboost had better performance on sensitivity and F1-score compared to other methods. Additionally, SVM with linear kernel function had the highest specificity of 0.9983, while its sensitivity and F1-score were poor. Logistic regression had the best performance, with an area under the curve of 0.8527. Evaluating Taiwanese advanced CKD patients' EMRs could provide physicians with a good approximation of the patients' 3-year risk of death by machine learning algorithms.
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Affiliation(s)
- Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yu-Da Chen
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan
- Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Jenny L. Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | | | - Cheng-Sheng Yu
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Fintech RD Center, Nan Shan Life Insurance Co., Ltd
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Ou SM, Tsai MT, Lee KH, Tseng WC, Yang CY, Chen TH, Bin PJ, Chen TJ, Lin YP, Sheu WHH, Chu YC, Tarng DC. Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms. BioData Min 2023; 16:8. [PMID: 36899426 PMCID: PMC10007785 DOI: 10.1186/s13040-023-00324-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 02/17/2023] [Indexed: 03/12/2023] Open
Abstract
OBJECTIVES Type 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings. METHODS We established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with 70% and 30% of patients randomly assigned to the training and testing sets, respectively. RESULTS The discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender. CONCLUSIONS Because our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage.
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Affiliation(s)
- Shuo-Ming Ou
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan.,School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ming-Tsun Tsai
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan.,School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Kuo-Hua Lee
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan.,School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Cheng Tseng
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan.,School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chih-Yu Yang
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan.,School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tz-Heng Chen
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan.,School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Pin-Jie Bin
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Tzeng-Ji Chen
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Family Medicine, Taipei Veterans General Hospital, Hsinchu Branch, Hsinchu, Taiwan.,Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yao-Ping Lin
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan.,School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wayne Huey-Herng Sheu
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Institute of Molecular and Genetic Medicine, National Health Research Institute, Miaoli, Taiwan
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan. .,Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan. .,Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan.
| | - Der-Cherng Tarng
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, 201, Section 2, Shih-Pai Road, Taipei, 11217, Taiwan. .,School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. .,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. .,Department and Institute of Physiology, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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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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Kanda E, Epureanu BI, Adachi T, Kashihara N. Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality. PLOS DIGITAL HEALTH 2023; 2:e0000188. [PMID: 36812636 PMCID: PMC9931312 DOI: 10.1371/journal.pdig.0000188] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/20/2022] [Indexed: 01/20/2023]
Abstract
Chronic kidney disease (CKD) patients have high risks of end-stage kidney disease (ESKD) and pre-ESKD death. Therefore, accurately predicting these outcomes is useful among CKD patients, especially in those who are at high risk. Thus, we evaluated whether a machine-learning system can predict accurately these risks in CKD patients and attempted its application by developing a Web-based risk-prediction system. We developed 16 risk-prediction machine-learning models using Random Forest (RF), Gradient Boosting Decision Tree, and eXtreme Gradient Boosting with 22 variables or selected variables for the prediction of the primary outcome (ESKD or death) on the basis of repeatedly measured data of CKD patients (n = 3,714; repeatedly measured data, n = 66,981) in their electronic-medical records. The performances of the models were evaluated using data from a cohort study of CKD patients carried out over 3 years (n = 26,906). One RF model with 22 variables and another RF model with 8 variables of time-series data showed high accuracies of the prediction of the outcomes and were selected for use in a risk-prediction system. In the validation, the 22- and 8-variable RF models showed high C-statistics for the prediction of the outcomes: 0.932 (95% CI 0.916, 0.948) and 0.93 (0.915, 0.945), respectively. Cox proportional hazards models using splines showed a highly significant relationship between the high probability and high risk of an outcome (p<0.0001). Moreover, the risks of patients with high probabilities were higher than those with low probabilities: 22-variable model, hazard ratio of 104.9 (95% CI 70.81, 155.3); 8-variable model, 90.9 (95% CI 62.29, 132.7). Then, a Web-based risk-prediction system was actually developed for the implementation of the models in clinical practice. This study showed that a machine-learning-based Web system is a useful tool for the risk prediction and treatment of CKD patients.
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Affiliation(s)
- Eiichiro Kanda
- Medical Science, Kawasaki Medical School, Kurashikishi, Okayamaken, Japan
- * E-mail:
| | - Bogdan Iuliu Epureanu
- College of Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Taiji Adachi
- Department of Biosystems Science, Institute for Life and Medical Sciences, Kyoto University, Kyotoshi, Kyotofu, Japan
| | - Naoki Kashihara
- Department of Nephrology and Hypertension, Kawasaki Medical School, Kurashikishi, Okayamaken, Japan
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Fuster-Parra P, Yañez AM, López-González A, Aguiló A, Bennasar-Veny M. Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network. Front Public Health 2023; 10:1035025. [PMID: 36711374 PMCID: PMC9878341 DOI: 10.3389/fpubh.2022.1035025] [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] [Received: 09/02/2022] [Accepted: 12/15/2022] [Indexed: 01/14/2023] Open
Abstract
Background It is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease. Methods This study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Markov blanket. A BN model for T2D was built from a dataset composed of 12 relevant features of the T2D domain, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure and parameters were learned with the bnlearn package in R language introducing prior knowledge. The Markov blanket was considered to find those features (variables) which increase the risk of T2D. Results The BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to obesity value, the glycosylated hemoglobin (HbA1c) to more than 6 value, the fatty liver index (FLI) to more than 60 value, physical activity (PA) to no state, and age to 48-62 state. The features increasing T2D in specific states (warning factors) were ranked. Conclusion The feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.
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Affiliation(s)
- Pilar Fuster-Parra
- Department of Mathematics and Computer Sciences, Balearic Islands University, Palma, Spain,Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain
| | - Aina M. Yañez
- Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain,Department of Nursing and Physiotherapy, Balearic Islands University, Palma, Spain,Research Group on Global Health and Human Development, Balearic Islands University, Palma, Spain,*Correspondence: Aina M. Yañez ✉
| | - Arturo López-González
- Escuela Universitaria ADEMA, Palma, Spain,Prevention of Occupational Risk in Health Services, Balearic Islands Health Service, Palma, Spain
| | - A. Aguiló
- Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain,Department of Nursing and Physiotherapy, Balearic Islands University, Palma, Spain
| | - Miquel Bennasar-Veny
- Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain,Department of Nursing and Physiotherapy, Balearic Islands University, Palma, Spain,CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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Schena FP, Anelli VW, Abbrescia DI, Di Noia T. Prediction of chronic kidney disease and its progression by artificial intelligence algorithms. J Nephrol 2022; 35:1953-1971. [PMID: 35543912 DOI: 10.1007/s40620-022-01302-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/04/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND OBJECTIVE Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitioners and nephrologists are involved in the first and in the late stages of the disease, respectively. Early diagnosis of CKD is an important step in preventing the progression of kidney damage. Our aim was to review publications on machine learning algorithms (MLAs) that can predict early CKD and its progression. METHODS We conducted a systematic review and selected 55 articles on the application of MLAs in CKD. PubMed, Medline, Scopus, Web of Science and IEEE Xplore Digital Library of the Institute of Electrical and Electronics Engineers were searched. The search terms were chronic kidney disease, artificial intelligence, data mining and machine learning algorithms. RESULTS MLAs use enormous numbers of predictors combining them in non-linear and highly interactive ways. This ability increases when new data is added. We observed some limitations in the publications: (i) databases were not accurately reviewed by physicians; (ii) databases did not report the ethnicity of the patients; (iii) some databases collected variables that were not important for the diagnosis and progression of CKD; (iv) no information was presented on the native kidney disease causing CKD; (v) no validation of the results in external independent cohorts was provided; and (vi) no insights were given on the MLAs that were used. Overall, there was limited collaboration among experts in electronics, computer science and physicians. CONCLUSIONS The application of MLAs in kidney diseases may enhance the ability of clinicians to predict CKD and RF, thus improving diagnostic assistance and providing suitable therapeutic decisions. However, it is necessary to improve the development process of MLA tools.
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Affiliation(s)
- Francesco Paolo Schena
- Department of Emergency and Organ Transplants, University of Bari, Bari, Italy.
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy.
| | - Vito Walter Anelli
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy
| | | | - Tommaso Di Noia
- Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy
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Inaguma D, Hayashi H, Yanagiya R, Koseki A, Iwamori T, Kudo M, Fukuma S, Yuzawa Y. Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan. BMJ Open 2022; 12:e058833. [PMID: 35680264 PMCID: PMC9185577 DOI: 10.1136/bmjopen-2021-058833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach. DESIGN Retrospective single-centre cohort study. SETTINGS Tertiary referral university hospital in Toyoake city, Japan. PARTICIPANTS A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m2 and eGFR decline of ≥30% within 2 years. PRIMARY OUTCOME Our main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters. RESULTS Our clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics. CONCLUSIONS The random forest model could be useful in identifying patients with extremely rapid eGFR decline. TRIAL REGISTRATION UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.
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Affiliation(s)
- Daijo Inaguma
- Internal Medicine, Fujita Health University Bantane Hospital, Nagoya, Japan
| | | | - Ryosuke Yanagiya
- Medical Information Systems, Fujita Health University, Toyoake, Japan
| | | | | | | | - Shingo Fukuma
- Human Health Science, Kyoto University, Kyoto, Japan
| | - Yukio Yuzawa
- Nephrology, Fujita Health University, Toyoake, Japan
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Liu Y, Li W, Yang H, Zhang X, Wang W, Jia S, Xiang B, Wang Y, Miao L, Zhang H, Wang L, Wang Y, Song J, Sun Y, Chai L, Tian X. Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome. Front Cell Infect Microbiol 2021; 11:645951. [PMID: 34178718 PMCID: PMC8231010 DOI: 10.3389/fcimb.2021.645951] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 04/29/2021] [Indexed: 12/12/2022] Open
Abstract
Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, and Clostridium sensu stricto 1 in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2, and Eubacterium coprostanoligenes group. In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics.
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Affiliation(s)
- Yuxia Liu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Wenhui Li
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Hongxia Yang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiaoying Zhang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Wenxiu Wang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Sitong Jia
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Beibei Xiang
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yi Wang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.,Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Lin Miao
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.,Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Han Zhang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.,Laboratory of Pharmacology of Traditional Chinese Medical Formulae Co-Constructed by the Province-Ministry, Tianjin University of TCM, Tianjin, China
| | - Lin Wang
- Tianjin Zhongxin Pharmaceutical Group Co., Ltd. Le Ren Tang Pharmaceutical Factory, Tianjin, China
| | - Yujing Wang
- Tianjin Zhongxin Pharmaceutical Group Co., Ltd. Le Ren Tang Pharmaceutical Factory, Tianjin, China
| | - Jixiang Song
- Tianjin Zhongxin Pharmaceutical Group Co., Ltd. Le Ren Tang Pharmaceutical Factory, Tianjin, China
| | - Yingjie Sun
- Tianjin Zhongxin Pharmaceutical Group Co., Ltd. Le Ren Tang Pharmaceutical Factory, Tianjin, China
| | - Lijuan Chai
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.,Laboratory of Pharmacology of Traditional Chinese Medical Formulae Co-Constructed by the Province-Ministry, Tianjin University of TCM, Tianjin, China
| | - Xiaoxuan Tian
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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10
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Ho CWL, Caals K. A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology. Semin Nephrol 2021; 41:282-293. [PMID: 34330368 DOI: 10.1016/j.semnephrol.2021.05.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Digitalization in nephrology has progressed in a manner that is disparate and siloed, even though learning (under a broader Learning Health System initiative) has been manifested in all the main areas of clinical application. Most applications based on artificial intelligence/machine learning (AI/ML) are still in the initial developmental stages and are yet to be adequately validated and shown to contribute to positive patient outcomes. There is also no consistent or comprehensive digitalization plan, and insufficient data are a limiting factor across all of these areas. In this article, we first consider how digitalization along nephrology care pathways relates to the Learning Health System initiative. We then consider the current state of AI/ML-based software and devices in nephrology and the ethical and regulatory challenges in scaling them up toward broader clinical application. We conclude with our proposal to establish a dedicated ethics and governance framework that is centered around health care providers in nephrology and the AI/ML-based software to which their work relates. This framework should help to integrate ethical and regulatory values and considerations, involve a wide range of stakeholders, and apply across normative domains that are conventionally demarcated as clinical, research, and public health.
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Affiliation(s)
- Calvin Wai-Loon Ho
- Centre for Medical Ethics and Law, Department of Law, The University of Hong Kong, Hong Kong SAR.
| | - Karel Caals
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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11
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Elderly Patients in a Large Nephrology Unit: Who Are Our Old, Old-Old and Oldest-Old Patients? J Clin Med 2021; 10:jcm10061168. [PMID: 33799519 PMCID: PMC8000250 DOI: 10.3390/jcm10061168] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/04/2021] [Accepted: 03/07/2021] [Indexed: 12/14/2022] Open
Abstract
The world population is aging, and the prevalence of chronic kidney disease (CKD) is increasing. Whether this increase is also due to the methods currently being used to assess kidney function in the elderly is still a matter of discussion. We aimed to describe the actual referral pattern of CKD patients in a large nephrology unit and test whether the use of different formulae to estimate kidney function could affect the staging and the need for specialist care in the older subset of our population. In 2019, 1992 patients were referred to our center. Almost 28% of the patients were aged ≥80 and about 6% were ≥90 years old. Among the causes of kidney disease, glomerulonephritis displayed a higher prevalence in younger patients whereas hypertensive or diabetic kidney disease were more prevalent in older patients. The prevalence of referred patients in advanced CKD stages increased with age; estimated glomerular filtration rate (eGFR) decreased with age regardless of which equation was used (chronic kidney disease epidemiology collaboration (CKD-EPI), Lund–Malmö Revised (LMR), modification of diet in renal disease (MDRD), Full Age Spectrum (FAS), or Berlin Initiative Study 1 (BIS)). With CKD-EPI as a reference, MDRD and FAS underestimated the CKD stage while LMR overestimated it. The BIS showed the highest heterogeneity. Considering an eGFR threshold limit of 45 mL/min for defining “significant” CKD in patients over 65 years of age, the variability in CKD staging was 10% no matter which equation was used. Our study quantified the weight of “old” and “old-old” patients on follow-up in a large nephrology outpatient unit and suggested that with the current referral pattern, the type of formula used does not affect the need for CKD care within the context of a relatively late referral, particularly in elderly patients.
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12
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Inaguma D, Kitagawa A, Yanagiya R, Koseki A, Iwamori T, Kudo M, Yuzawa Y. Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database. PLoS One 2020; 15:e0239262. [PMID: 32941535 PMCID: PMC7497987 DOI: 10.1371/journal.pone.0239262] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 09/02/2020] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence is increasingly being adopted in medical fields to predict various outcomes. In particular, chronic kidney disease (CKD) is problematic because it often progresses to end-stage kidney disease. However, the trajectories of kidney function depend on individual patients. In this study, we propose a machine learning-based model to predict the rapid decline in kidney function among CKD patients by using a big hospital database constructed from the information of 118,584 patients derived from the electronic medical records system. The database included the estimated glomerular filtration rate (eGFR) of each patient, recorded at least twice over a period of 90 days. The data of 19,894 patients (16.8%) were observed to satisfy the CKD criteria. We characterized the rapid decline of kidney function by a decline of 30% or more in the eGFR within a period of two years and classified the available patients into two groups—those exhibiting rapid eGFR decline and those exhibiting non-rapid eGFR decline. Following this, we constructed predictive models based on two machine learning algorithms. Longitudinal laboratory data including urine protein, blood pressure, and hemoglobin were used as covariates. We used longitudinal statistics with a baseline corresponding to 90-, 180-, and 360-day windows prior to the baseline point. The longitudinal statistics included the exponentially smoothed average (ESA), where the weight was defined to be 0.9*(t/b), where t denotes the number of days prior to the baseline point and b denotes the decay parameter. In this study, b was taken to be 7 (7-day ESA). We used logistic regression (LR) and random forest (RF) algorithms based on Python code with scikit-learn library (https://scikit-learn.org/) for model creation. The areas under the curve for LR and RF were 0.71 and 0.73, respectively. The 7-day ESA of urine protein ranked within the first two places in terms of importance according to both models. Further, other features related to urine protein were likely to rank higher than the rest. The LR and RF models revealed that the degree of urine protein, especially if it exhibited an increasing tendency, served as a prominent risk factor associated with rapid eGFR decline.
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Affiliation(s)
- Daijo Inaguma
- Department of Internal Medicine, Fujita Health University Bantane Hospital–Nagoya, Japan
- * E-mail:
| | - Akimitsu Kitagawa
- Department of Internal Medicine, Fujita Health University Bantane Hospital–Nagoya, Japan
| | - Ryosuke Yanagiya
- Division of Medical Information Systems, Fujita Health University School of Medicine–Toyoake, Japan
| | | | | | | | - Yukio Yuzawa
- Department of Nephrology, Fujita Health University School of Medicine–Toyoake, Japan
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13
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Yuan Q, Zhang H, Deng T, Tang S, Yuan X, Tang W, Xie Y, Ge H, Wang X, Zhou Q, Xiao X. Role of Artificial Intelligence in Kidney Disease. Int J Med Sci 2020; 17:970-984. [PMID: 32308551 PMCID: PMC7163364 DOI: 10.7150/ijms.42078] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 03/17/2020] [Indexed: 12/17/2022] Open
Abstract
Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.
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Affiliation(s)
- Qiongjing Yuan
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Haixia Zhang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China.,Department of Nephrology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou, Jiangsu 215000, China
| | - Tianci Deng
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Shumei Tang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiangning Yuan
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Wenbin Tang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Yanyun Xie
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Huipeng Ge
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiufen Wang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Qiaoling Zhou
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiangcheng Xiao
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
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14
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Burlacu A, Iftene A, Busoiu E, Cogean D, Covic A. Challenging the supremacy of evidence-based medicine through artificial intelligence: the time has come for a change of paradigms. Nephrol Dial Transplant 2019; 35:191-194. [PMID: 31697377 DOI: 10.1093/ndt/gfz203] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 09/02/2019] [Indexed: 12/14/2022] Open
Affiliation(s)
- Alexandru Burlacu
- Department of Interventional Cardiology, Cardiovascular Diseases Institute, 'Grigore T. Popa' University of Medicine, Iasi, Romania
| | - Adrian Iftene
- Faculty of Computer Science, 'Alexandru Ioan Cuza' University of Iasi, Iasi, Romania
| | - Eugen Busoiu
- Artificial Intelligence Community, Iasi, Romania
| | - Dragos Cogean
- Software Development Gemini CAD Systems, Iasi, Romania
| | - Adrian Covic
- Nephrology Clinic, Dialysis and Renal Transplant Center, 'C.I. Parhon' University Hospital, 'Grigore T. Popa' University of Medicine, Iasi, Romania
- The Academy of Romanian Scientists (AOSR)
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