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Lanot A, Akesson A, Nakano FK, Vens C, Björk J, Nyman U, Grubb A, Sundin PO, Eriksen BO, Melsom T, Rule AD, Berg U, Littmann K, Åsling-Monemi K, Hansson M, Larsson A, Courbebaisse M, Dubourg L, Couzi L, Gaillard F, Garrouste C, Jacquemont L, Kamar N, Legendre C, Rostaing L, Ebert N, Schaeffner E, Bökenkamp A, Mariat C, Pottel H, Delanaye P. Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR. BMC Nephrol 2025; 26:47. [PMID: 39885391 PMCID: PMC11780799 DOI: 10.1186/s12882-025-03972-0] [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: 11/02/2024] [Accepted: 01/21/2025] [Indexed: 02/01/2025] Open
Abstract
BACKGROUND Creatinine-based estimated glomerular filtration rate (eGFR) equations are widely used in clinical practice but exhibit inherent limitations. On the other side, measuring GFR is time consuming and not available in routine clinical practice. We developed and validated machine learning models to assess the trustworthiness (i.e. the ability of equations to estimate measured GFR (mGFR) within 10%, 20% or 30%) of the European Kidney Function Consortium (EKFC) equation at the individual level. METHODS This observational study used data from European and US cohorts, comprising 22,343 participants of all ages with available mGFR results. Four machine learning and two traditional logistic regression models were trained on a cohort of 9,202 participants to predict the likelihood of the EKFC creatinine-derived eGFR falling within 30% (p30), 20% (p20) or 10% (p10) of the mGFR value. The algorithms were internally and then externally validated on cohorts of respectively 3,034 and 10,107 participants. The predictors included in the models were creatinine, age, sex, height, weight, and EKFC. RESULTS The random forest model was the most robust model. In the external validation cohort, the model achieved an area under the curve of 0.675 (95%CI 0.660;0.690) and an accuracy of 0.716 (95%CI 0.707;0.725) for the P30 criterion. Sensitivity was 0.756 (95%CI 0.747;0.765) and specificity was 0.485 (95%CI 0.460; 0.511) at the 80% probability level that EKFC falls within 30% of mGFR. At the population level, the PPV of this machine learning model was 89.5%, higher than the EKFC P30 of 85.2%. A free web-application was developed to allow the physician to assess the trustworthiness of EKFC at the individual level. CONCLUSIONS A strategy using machine learning model marginally improves the trustworthiness of GFR estimation at the population level. An additional value of this approach lies in its ability to provide assessments at the individual level.
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Affiliation(s)
- Antoine Lanot
- Normandie Univ, UNICAEN, CHU de Caen Normandie, Néphrologie, Caen, France.
- Normandie Université, Unicaen, UFR de Médecine, 2 Rue Des Rochambelles, Caen, France.
- ANTICIPE" U1086 INSERM-UCN, Centre François Baclesse, Caen, France.
| | - Anna Akesson
- Skane University Hospital, Clinical Studies Sweden Forum South, Remissgatan 4, Lund, 22185, Sweden
- Lund University, Malmö, Sweden
| | - Felipe Kenji Nakano
- Department of Public Health and Primary Care, KU Leuven, Campus Kulak, Kortrijk, Belgium
- Itec, Imec Research Group, KU Leuven, Kortrijk, Belgium
| | - Celine Vens
- Department of Public Health and Primary Care, KU Leuven, Campus Kulak, Kortrijk, Belgium
- Itec, Imec Research Group, KU Leuven, Kortrijk, Belgium
| | - Jonas Björk
- Lund University, Box 117, 221 00, Lund, Sweden
| | - Ulf Nyman
- , Östra Vallgatan 41, 223 61, Lund, Sweden
| | - Anders Grubb
- Department of Clinical Chemistry and Pharmacology, Laboratory Lund University, Lund, 22185, Sweden
| | - Per-Ola Sundin
- Karla Healthcare Centre, Faculty of Medicine and Health, Örebro University, Örebro, 701 85, Sweden
| | - Björn O Eriksen
- University Hospital of North Norway (UNN), 9038, Breivika, Troms, Norway
| | - Toralf Melsom
- University Hospital of North Norway (UNN), 9038, Breivika, Troms, Norway
| | - Andrew D Rule
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Ulla Berg
- Department of Clinical Science, Intervention and Technology, Division of Pediatrics, Karolinska Institutet, Karolinska University. Hospital Huddinge, 14186, Stockholm, Sweden
| | - Karin Littmann
- Department of Medicine Huddinge, Karolinska Institutet, C2:91 Karolinska University Hospital, Huddinge, SE-141 52, Sweden
| | - Kajsa Åsling-Monemi
- Barnnjursektionen K 88, Astrid Lindgrens Barnsjukhus, Karolinska University Hospital, Stockholm, 141 86, Sweden
| | - Magnus Hansson
- Department of Clinical Chemistry, C1:74 Huddinge, Karolinska University Hospital, Stockholm, SE-141 86, Sweden
| | - Anders Larsson
- Clinical Chemistry and Pharmacology, Entrance 61, 2Nd Floor, Akademiska Hospital, 751 85, Uppsala, Sweden
| | - Marie Courbebaisse
- Service de Physiologie-Explorations, Fonctionnelles Renales Hopital Europeen Georges Pompidou, 20 Rue Leblanc, Paris, 75015, France
| | - Laurence Dubourg
- Exploration Fonctionnelle Renale Pavillon P, Hopital Edouard Herriot, 5 Place d'Arsonval, 69437, Lyon, Cedex 03, France
| | - Lionel Couzi
- CHU de Bordeaux, Nephrologie-Transplantation-Dialyse, Hopital Pellegrin, Universite de Bordeaux, Place Amelie Raba Leon, Bordeaux, 33076, France
| | - Francois Gaillard
- Renal Transplantation Department, Assistance Publique-Hopitaux de Paris (AP-HP), Hopital Bichat, 46 Rue Henri Huchard, Paris, 75018, France
| | - Cyril Garrouste
- Department of Nephrology, Clermont-Ferrand University Hospital, Clermont-Ferrand, France
| | - Lola Jacquemont
- Service de Nephrologie Et Immunologie Clinique, CHU de Nantes, 30 Boulevard Jean Monnet, 44093, Nantes, Cedex 1, France
| | - Nassim Kamar
- Department of Nephrology and Organ Transplantation, CHU Rangueil, 1 Avenue J.Poulhes, TSA 50032, 31059, Toulouse, Cedex 9, France
| | - Christophe Legendre
- Transplantation Renale, Hopital Necker, 145 Rue de Sevres, Paris, 75015, France
| | - Lionel Rostaing
- Service de Nephrologie, Hemodialyse, Aphereses Et Transplantation Renale, Hopital Michallon, Centre Hospitalier Universitaire Grenoble-Alpes, Boulevard de La Chantourne, La Tronche, 38700, France
| | - Natalie Ebert
- Institute of Public Health, Charité. Universitätsmedizin Berlin, Luisenstrasse 57, Berlin, 10117, Germany
| | - Elke Schaeffner
- Institute of Public Health, Charité. Universitätsmedizin Berlin, Luisenstrasse 57, Berlin, 10117, Germany
| | - Arend Bökenkamp
- Amsterdam UMC, Vrije Universiteit, De Boelelaan 1112, Amsterdam, 1081 HV, the Netherlands
| | - Christophe Mariat
- Service de Nephrologie, Dialyse Et Transplantation Renale, Hopital Nord, CHU de Saint-Etienne, 25 Boulevard Pasteur, 42055, Saint-Etienne, Cedex 2, France
| | - Hans Pottel
- Department of Public Health and Primary Care, KU Leuven, Campus Kulak, Kortrijk, Belgium
| | - Pierre Delanaye
- Department of Nephrology-Dialysis-Transplantation, University of Liège, CHU Sart Tilman, Liège, Belgium
- Department of Nephrology-Dialysis-Apheresis, Hôpital Universitaire Carémeau, Nîmes, France
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Liu X, Shi J, Jiao Y, An J, Tian J, Yang Y, Zhuo L. Integrated multi-omics with machine learning to uncover the intricacies of kidney disease. Brief Bioinform 2024; 25:bbae364. [PMID: 39082652 PMCID: PMC11289682 DOI: 10.1093/bib/bbae364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/20/2024] [Accepted: 07/17/2024] [Indexed: 08/03/2024] Open
Abstract
The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.
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Affiliation(s)
| | | | | | | | | | | | - Li Zhuo
- Corresponding author. Department of Nephrology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Clinic Medical College, Beijing University of Chinese Medicine, 100029 Beijing, China. E-mail:
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García-Jaramillo M, Luque C, León-Vargas F. Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis. J Diabetes Sci Technol 2024; 18:287-301. [PMID: 38047451 PMCID: PMC10973853 DOI: 10.1177/19322968231215350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
BACKGROUND The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address this, a bibliometric analysis of scientific articles published from 2000 to 2022 was conducted to discover global research trends and networks and to emphasize the most prominent countries, institutions, journals, articles, and key topics in this domain. METHODS The Scopus database was used to identify and retrieve high-quality scientific documents. The results were classified into categories of detection (covering diagnosis, screening, identification, segmentation, among others), prediction (prognosis, forecasting, estimation), and management (treatment, control, monitoring, education, telemedicine integration). Biblioshiny and RStudio were used to analyze the data. RESULTS A total of 1773 articles were collected and analyzed. The number of publications and citations increased substantially since 2012, with a notable increase in the last 3 years. Of the 3 categories considered, detection was the most dominant, followed by prediction and management. Around 53.2% of the total journals started disseminating articles on this subject in 2020. China, India, and the United States were the most productive countries. Although no evidence of outstanding leadership by specific authors was found, the University of California emerged as the most influential institution for the development of scientific production. CONCLUSION This is an evolving field that has experienced a rapid increase in productivity, especially over the last years with exponential growth. This trend is expected to continue in the coming years.
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Affiliation(s)
| | - Carolina Luque
- Faculty of Engineering, Universidad
EAN, Bogotá, Colombia
| | - Fabian León-Vargas
- Faculty of Mechanical, Electronic and
Biomedical Engineering, Universidad Antonio Nariño, Bogotá, Colombia
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Özdede M, Guven AT. Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification. Cureus 2023; 15:e51109. [PMID: 38274913 PMCID: PMC10809736 DOI: 10.7759/cureus.51109] [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: 12/24/2023] [Indexed: 01/27/2024] Open
Abstract
Background Elevated serum uric acid, associated with cardiovascular conditions such as atherosclerotic heart disease, hypertension, and heart failure, can be elevated by thiazide or thiazide-like drugs (THZ), essential in hypertension management. Identifying clinical determinants affecting THZ-related uric acid elevation is critical. Methods In this retrospective cross-sectional study, we explored the clinical determinants influencing uric acid elevation related to THZ, focusing on patients where THZ was initiated or the dose escalated. A cohort of 143 patients was analyzed, collecting baseline and control uric acid levels, alongside basic biochemical studies and clinical data. Feature selection was conducted utilizing criteria based on mean squared error increase and enhancement in node purity. Four machine learning algorithms - Random Forest, Neural Network, Support Vector Machine, and Gradient Boosting regressions - were applied to pinpoint clinical influencers. Results Significant features include uncontrolled diabetes, index estimated Glomerular Filtration Rate (eGFR) level, absence of insulin, action of indapamide, and absence of statin treatment, with absence of Sodium-glucose cotransporter 2 inhibitors (SGLT2i), low dose aspirin exposure, and older age also being noteworthy. Among the applied models, the Gradient Boosting regression model outperformed the others, exhibiting the lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) values, and the highest R2 value (0.779). While Random Forest and Neural Network regression models were able to fit the data adequately, the Support Vector Machine demonstrated inferior metrics. Conclusions Machine learning algorithms are adept at accurately identifying the factors linked to uric acid fluctuations caused by THZ. This proficiency aids in customizing treatments more effectively, reducing the need to unnecessarily avoid THZ, and providing guidance on its use to prevent instances where uric acid levels could become problematic.
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Affiliation(s)
- Murat Özdede
- Internal Medicine, Hacettepe University Faculty of Medicine, Ankara, TUR
| | - Alper T Guven
- Internal Medicine, Baskent University Faculty of Medicine, Ankara, TUR
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Layton AT. "Hi, how can i help you?": embracing artificial intelligence in kidney research. Am J Physiol Renal Physiol 2023; 325:F395-F406. [PMID: 37589052 DOI: 10.1152/ajprenal.00177.2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/18/2023] Open
Abstract
In recent years, biology and precision medicine have benefited from major advancements in generating large-scale molecular and biomedical datasets and in analyzing those data using advanced machine learning algorithms. Machine learning applications in kidney physiology and pathophysiology include segmenting kidney structures from imaging data and predicting conditions like acute kidney injury or chronic kidney disease using electronic health records. Despite the potential of machine learning to revolutionize nephrology by providing innovative diagnostic and therapeutic tools, its adoption in kidney research has been slower than in other organ systems. Several factors contribute to this underutilization. The complexity of the kidney as an organ, with intricate physiology and specialized cell populations, makes it challenging to extrapolate bulk omics data to specific processes. In addition, kidney diseases often present with overlapping manifestations and morphological changes, making diagnosis and treatment complex. Moreover, kidney diseases receive less funding compared with other pathologies, leading to lower awareness and limited public-private partnerships. To promote the use of machine learning in kidney research, this review provides an introduction to machine learning and reviews its notable applications in renal research, such as morphological analysis, omics data examination, and disease diagnosis and prognosis. Challenges and limitations associated with data-driven predictive techniques are also discussed. The goal of this review is to raise awareness and encourage the kidney research community to embrace machine learning as a powerful tool that can drive advancements in understanding kidney diseases and improving patient care.
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Affiliation(s)
- Anita T Layton
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
- Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
- Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
- School of Pharmacology, University of Waterloo, Waterloo, Ontario, Canada
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Güven AT, Özdede M, Şener YZ, Yıldırım AO, Altıntop SE, Yeşilyurt B, Uyaroğlu OA, Tanrıöver MD. Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction. Eur J Intern Med 2023; 114:74-83. [PMID: 37217407 DOI: 10.1016/j.ejim.2023.05.021] [Citation(s) in RCA: 2] [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: 03/31/2023] [Revised: 05/01/2023] [Accepted: 05/15/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND Renin-angiotensin-aldosterone system inhibitors (RAASi) are commonly used medications. Renal adverse events associated with RAASi are hyperkalemia and acute kidney injury. We aimed to evaluate the performance of machine learning (ML) algorithms in order to define event associated features and predict RAASi associated renal adverse events. MATERIALS AND METHODS Data of patients recruited from five internal medicine and cardiology outpatient clinics were evaluated retrospectively. Clinical, laboratory, and medication data were acquired via electronic medical records. Dataset balancing and feature selection for machine learning algorithms were performed. Random forest (RF), k-nearest neighbor (kNN), naïve Bayes (NB), extreme gradient boosting (xGB), support vector machine (SVM), neural network (NN), and logistic regression (LR) were used to create a prediction model. RESULTS 409 patients were included, and 50 renal adverse events occurred. The most important features predicting the renal adverse events were the index K and glucose levels, as well as having uncontrolled diabetes mellitus. Thiazides reduced RAASi associated hyperkalemia. kNN, RF, xGB and NN algorithms have the highest and similar AUC (≥ 98%), recall (≥ 94%), specifity (≥ 97%), precision (≥ 92%), accuracy (≥ 96%) and F1 statistics (≥ 94%) performance metrics for prediction. CONCLUSION RAASi associated renal adverse events can be predicted prior to medication initiation by machine learning algorithms. Further prospective studies with large patient numbers are needed to create scoring systems as well as for their validation.
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Affiliation(s)
- Alper Tuna Güven
- Başkent University Faculty of Medicine, Department of Internal Medicine, Division of General Internal Medicine.
| | - Murat Özdede
- Hacettepe University Faculty of Medicine, Department of Internal Medicine, Division of General Internal Medicine
| | | | | | | | - Berkay Yeşilyurt
- Hacettepe University Faculty of Medicine, Department of Internal Medicine
| | - Oğuz Abdullah Uyaroğlu
- Hacettepe University Faculty of Medicine, Department of Internal Medicine, Division of General Internal Medicine
| | - Mine Durusu Tanrıöver
- Hacettepe University Faculty of Medicine, Department of Internal Medicine, Division of General Internal Medicine
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Kuang X, Zhong Z, Liang W, Huang S, Luo R, Luo H, Li Y. Bibliometric analysis of 100 top cited articles of heart failure-associated diseases in combination with machine learning. Front Cardiovasc Med 2023; 10:1158509. [PMID: 37304963 PMCID: PMC10248156 DOI: 10.3389/fcvm.2023.1158509] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/03/2023] [Indexed: 06/13/2023] Open
Abstract
Objective The aim of this paper is to analyze the application of machine learning in heart failure-associated diseases using bibliometric methods and to provide a dynamic and longitudinal bibliometric analysis of heart failure-related machine learning publications. Materials and methods Web of Science was screened to gather the articles for the study. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility. Intuitive data analysis was employed to analyze the top-100 cited articles and VOSViewer was used to analyze the relevance and impact of all articles. The two analysis methods were then compared to get conclusions. Results The search identified 3,312 articles. In the end, 2,392 papers were included in the study, which were published between 1985 and 2023. All articles were analyzed using VOSViewer. Key points of the analysis included the co-authorship map of authors, countries and organizations, the citation map of journal and documents and a visualization of keyword co-occurrence analysis. Among these 100 top-cited papers, with a mean of 122.9 citations, the most-cited article had 1,189, and the least cited article had 47. Harvard University and the University of California topped the list among all institutes with 10 papers each. More than one-ninth of the authors of these 100 top-cited papers wrote three or more articles. The 100 articles came from 49 journals. The articles were divided into seven areas according to the type of machine learning approach employed: Support Vector Machines, Convolutional Neural Networks, Logistic Regression, Recurrent Neural Networks, Random Forest, Naive Bayes, and Decision Tree. Support Vector Machines were the most popular method. Conclusions This analysis provides a comprehensive overview of the artificial intelligence (AI)-related research conducted in the field of heart failure, which helps healthcare institutions and researchers better understand the prospects of AI in heart failure and formulate more scientific and effective research plans. In addition, our bibliometric evaluation can assist healthcare institutions and researchers in determining the advantages, sustainability, risks, and potential impacts of AI technology in heart failure.
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Affiliation(s)
- Xuyuan Kuang
- Department of Hyperbaric Oxygen, Xiangya Hospital, Changsha, China
- National Research Center of Geriatic Diseases (Xiangya Hospital), Changsha, China
| | - Zihao Zhong
- Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China
| | - Wei Liang
- Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China
| | - Suzhen Huang
- The Big Data Institute, Central South University, Changsha, China
| | - Renji Luo
- Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China
| | - Hui Luo
- National Research Center of Geriatic Diseases (Xiangya Hospital), Changsha, China
- Department of Anesthesiology, Xiangya Hospital, Changsha, China
| | - Yongheng Li
- Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China
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Hundemer GL, Sood MM, Canney M. Recent updates in kidney risk prediction modeling: novel approaches and earlier outcomes. Curr Opin Nephrol Hypertens 2023; 32:257-262. [PMID: 36811630 DOI: 10.1097/mnh.0000000000000879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
PURPOSE OF REVIEW Recent years have witnessed the development of kidney risk prediction models which diverge from traditional model designs to incorporate novel approaches along with a focus on earlier outcomes. This review summarizes these recent advances, evaluates their pros and cons, and discusses their potential implications. RECENT FINDINGS Several kidney risk prediction models have recently been developed utilizing machine learning rather than traditional Cox regression. These models have demonstrated accurate prediction of kidney disease progression, often beyond that of traditional models, in both internal and external validation. On the opposite end of the spectrum, a simplified kidney risk prediction model was recently developed which minimized the need for laboratory data and instead relies primarily on self-reported data. While internal testing showed good overall predictive performance, the generalizability of this model remains uncertain. Finally, there is a growing trend toward prediction of earlier kidney outcomes (e.g., incident chronic kidney disease [CKD]) and away from a sole focus on kidney failure. SUMMARY Newer approaches and outcomes now being incorporated into kidney risk prediction modeling may enhance prediction and benefit a broader patient population. However, future work should address how best to implement these models into practice and assess their long-term clinical effectiveness.
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Affiliation(s)
- Gregory L Hundemer
- Division of Nephrology, Department of Medicine
- Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Manish M Sood
- Division of Nephrology, Department of Medicine
- Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Mark Canney
- Division of Nephrology, Department of Medicine
- Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
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Dalal S, Onyema EM, Malik A. Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy. World J Gastroenterol 2022; 28:6551-6563. [PMID: 36569269 PMCID: PMC9782838 DOI: 10.3748/wjg.v28.i46.6551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/27/2022] [Accepted: 11/21/2022] [Indexed: 12/08/2022] Open
Abstract
BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection. To curb the growing threats from liver disease, early detection is critical to help reduce the risks and improve treatment outcome. Emerging technologies such as machine learning, as shown in this study, could be deployed to assist in enhancing its prediction and treatment.
AIM To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection, diagnosis, and reduction of risks and mortality associated with the disease.
METHODS The dataset used in this study consisted of 416 people with liver problems and 167 with no such history. The data were collected from the state of Andhra Pradesh, India, through https://www.kaggle.com/datasets/uciml/indian-liver-patient-records. The population was divided into two sets depending on the disease state of the patient. This binary information was recorded in the attribute "is_patient".
RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36% and 73.24%, respectively, which was much better than the conventional method. The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis (scarring) and to enhance the survival of patients. The study showed the potential of machine learning in health care, especially as it concerns disease prediction and monitoring.
CONCLUSION This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease. However, relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.
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Affiliation(s)
- Surjeet Dalal
- Department of CSE, Amity University, Gurugram 122413, Haryana, India
| | - Edeh Michael Onyema
- Department of Mathematics and Computer Science, Coal City University, Enugu 400102, Nigeria
| | - Amit Malik
- Department of CSE, SRM University, Delhi-NCR, Sonipat 131001, Haryana, India
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Jiang J, Chan L, Nadkarni GN. The promise of artificial intelligence for kidney pathophysiology. Curr Opin Nephrol Hypertens 2022; 31:380-386. [PMID: 35703218 PMCID: PMC10309072 DOI: 10.1097/mnh.0000000000000808] [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] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions. RECENT FINDINGS We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions. SUMMARY The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.
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Affiliation(s)
- Joy Jiang
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lili Chan
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N. Nadkarni
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Li PKT, Chow KM, Cho Y, Fan S, Figueiredo AE, Harris T, Kanjanabuch T, Kim YL, Madero M, Malyszko J, Mehrotra R, Okpechi IG, Perl J, Piraino B, Runnegar N, Teitelbaum I, Wong JKW, Yu X, Johnson DW. ISPD peritonitis guideline recommendations: 2022 update on prevention and treatment. Perit Dial Int 2022; 42:110-153. [PMID: 35264029 DOI: 10.1177/08968608221080586] [Citation(s) in RCA: 302] [Impact Index Per Article: 100.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Peritoneal dialysis (PD)-associated peritonitis is a serious complication of PD and prevention and treatment of such is important in reducing patient morbidity and mortality. The ISPD 2022 updated recommendations have revised and clarified definitions for refractory peritonitis, relapsing peritonitis, peritonitis-associated catheter removal, PD-associated haemodialysis transfer, peritonitis-associated death and peritonitis-associated hospitalisation. New peritonitis categories and outcomes including pre-PD peritonitis, enteric peritonitis, catheter-related peritonitis and medical cure are defined. The new targets recommended for overall peritonitis rate should be no more than 0.40 episodes per year at risk and the percentage of patients free of peritonitis per unit time should be targeted at >80% per year. Revised recommendations regarding management of contamination of PD systems, antibiotic prophylaxis for invasive procedures and PD training and reassessment are included. New recommendations regarding management of modifiable peritonitis risk factors like domestic pets, hypokalaemia and histamine-2 receptor antagonists are highlighted. Updated recommendations regarding empirical antibiotic selection and dosage of antibiotics and also treatment of peritonitis due to specific microorganisms are made with new recommendation regarding adjunctive oral N-acetylcysteine therapy for mitigating aminoglycoside ototoxicity. Areas for future research in prevention and treatment of PD-related peritonitis are suggested.
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Affiliation(s)
- Philip Kam-Tao Li
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
- Carol and Richard Yu Peritoneal Dialysis Research Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Kai Ming Chow
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
- Carol and Richard Yu Peritoneal Dialysis Research Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Yeoungjee Cho
- Australasian Kidney Trials Network, University of Queensland, Brisbane, Australia
- Department of Nephrology, Princess Alexandra Hospital, Brisbane, Australia
| | - Stanley Fan
- Translational Medicine and Therapeutic, William Harvey Research Institute, Queen Mary University, London, UK
| | - Ana E Figueiredo
- Nursing School Escola de Ciências da Saúde e da Vida Pontificia Universidade Catolica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Tess Harris
- Polycystic Kidney Disease Charity, London, UK
| | - Talerngsak Kanjanabuch
- Division of Nephrology, Department of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Kidney Metabolic Disorders, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yong-Lim Kim
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Magdalena Madero
- Division of Nephrology, Department of Medicine, National Heart Institute, Mexico City, Mexico
| | - Jolanta Malyszko
- Department of Nephrology, Dialysis and Internal Diseases, The Medical University of Warsaw, Poland
| | - Rajnish Mehrotra
- Division of Nephrology, Department of Medicine, Harborview Medical Center, University of Washington, Seattle, Washington, DC, USA
| | - Ikechi G Okpechi
- Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, South Africa
| | - Jeff Perl
- St Michael's Hospital, University of Toronto, ON, Canada
| | - Beth Piraino
- Department of Medicine, Renal Electrolyte Division, University of Pittsburgh, PA, USA
| | - Naomi Runnegar
- Infectious Management Services, Princess Alexandra Hospital, University of Queensland, Brisbane, Australia
| | - Isaac Teitelbaum
- Division of Nephrology, Department of Medicine, University of Colorado, Aurora, CO, USA
| | | | - Xueqing Yu
- Department of Nephrology, Guangdong Provincial People's Hospital, Guangzhou, China
- Guangdong Academy of Medical Sciences, Guangzhou, China
| | - David W Johnson
- Australasian Kidney Trials Network, University of Queensland, Brisbane, Australia
- Department of Nephrology, Princess Alexandra Hospital, Brisbane, Australia
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