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Wu HF, Yan JP, Wu Q, Yu Z, Xu HX, Song CH, Guo ZQ, Li W, Xiang YJ, Xu Z, Luo J, Cheng SQ, Zhang FM, Shi HP, Zhuang CL. Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm. Nutrition 2024; 119:112317. [PMID: 38154396 DOI: 10.1016/j.nut.2023.112317] [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: 08/01/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 12/30/2023]
Abstract
OBJECTIVES Cancer cachexia is a debilitating condition with widespread negative effects. The heterogeneity of clinical features within patients with cancer cachexia is unclear. The identification and prognostic analysis of diverse phenotypes of cancer cachexia may help develop individualized interventions to improve outcomes for vulnerable populations. The aim of this study was to show that the machine learning-based cancer cachexia classification model generalized well on the external validation cohort. METHODS This was a nationwide multicenter observational study conducted from October 2012 to April 2021 in China. Unsupervised consensus clustering analysis was applied based on demographic, anthropometric, nutritional, oncological, and quality-of-life data. Key characteristics of each cluster were identified using the standardized mean difference. We used logistic and Cox regression analysis to evaluate 1-, 3-, 5-y, and overall mortality. RESULTS A consensus clustering algorithm was performed for 4329 patients with cancer cachexia in the discovery cohort, and four clusters with distinct phenotypes were uncovered. From clusters 1 to 4, the clinical characteristics of patients showed a transition from almost unimpaired to mildly, moderately, and severely impaired. Consistently, an increase in mortality from clusters 1 to 4 was observed. The overall mortality rate was 32%, 40%, 54%, and 68%, and the median overall survival time was 21.9, 18, 16.7, and 13.6 mo for patients in clusters 1 to 4, respectively. Our machine learning-based model performed better in predicting mortality than the traditional model. External validation confirmed the above results. CONCLUSIONS Machine learning is valuable for phenotype classifications of patients with cancer cachexia. Detection of clinically distinct clusters among cachexic patients assists in scheduling personalized treatment strategies and in patient selection for clinical trials.
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Affiliation(s)
- Hao-Fan Wu
- Colorectal Cancer Center/Department of Gastrointestinal Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiang-Peng Yan
- Department of Automation, Tsinghua University, Beijing, China
| | - Qian Wu
- Colorectal Cancer Center/Department of Gastrointestinal Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhen Yu
- Colorectal Cancer Center/Department of Gastrointestinal Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Hong-Xia Xu
- Department of Clinical Nutrition, Daping Hospital & Research Institute of Surgery, Third Military Medical University, Chongqing, China
| | - Chun-Hua Song
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Zeng-Qing Guo
- Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China
| | - Wei Li
- Cancer Center of the First Hospital of Jilin University, Changchun, China
| | - Yan-Jun Xiang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Zhe Xu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Jie Luo
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Shu-Qun Cheng
- Tongji University Cancer Center, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Feng-Min Zhang
- Colorectal Cancer Center/Department of Gastrointestinal Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Han-Ping Shi
- Department of Gastrointestinal Surgery/Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Cheng-Le Zhuang
- Colorectal Cancer Center/Department of Gastrointestinal Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 175] [Impact Index Per Article: 175.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Thongprayoon C, Miao J, Jadlowiec C, Mao SA, Mao M, Leeaphorn N, Kaewput W, Pattharanitima P, Valencia OAG, Tangpanithandee S, Krisanapan P, Suppadungsuk S, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering. Ren Fail 2023; 45:2292163. [PMID: 38087474 PMCID: PMC11001364 DOI: 10.1080/0886022x.2023.2292163] [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: 09/19/2023] [Accepted: 12/03/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Educational attainment significantly influences post-transplant outcomes in kidney transplant patients. However, research on specific attributes of lower-educated subgroups remains underexplored. This study utilized unsupervised machine learning to segment kidney transplant recipients based on education, further analyzing the relationship between these segments and post-transplant results. METHODS Using the OPTN/UNOS 2017-2019 data, consensus clustering was applied to 20,474 kidney transplant recipients, all below a college/university educational threshold. The analysis concentrated on recipient, donor, and transplant features, aiming to discern pivotal attributes for each cluster and compare post-transplant results. RESULTS Four distinct clusters emerged. Cluster 1 comprised younger, non-diabetic, first-time recipients from non-hypertensive younger donors. Cluster 2 predominantly included white patients receiving their first-time kidney transplant either preemptively or within three years, mainly from living donors. Cluster 3 included younger re-transplant recipients, marked by elevated PRA, fewer HLA mismatches. In contrast, Cluster 4 captured older, diabetic patients transplanted after prolonged dialysis duration, primarily from lower-grade donors. Interestingly, Cluster 2 showcased the most favorable post-transplant outcomes. Conversely, Clusters 1, 3, and 4 revealed heightened risks for graft failure and mortality in comparison. CONCLUSIONS Through unsupervised machine learning, this study proficiently categorized kidney recipients with lesser education into four distinct clusters. Notably, the standout performance of Cluster 2 provides invaluable insights, underscoring the necessity for adept risk assessment and tailored transplant strategies, potentially elevating care standards for this patient cohort.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL, USA
| | - Michael Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Napat Leeaphorn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
| | | | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Chakri Naruebodindra Medical Institute, Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Internal Medicine, Thammasat University, Pathum Thani, Thailand
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Chakri Naruebodindra Medical Institute, Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Matthew Cooper
- Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
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Lai CF, Liu JH, Tseng LJ, Tsao CH, Chou NK, Lin SL, Chen YM, Wu VC. Unsupervised clustering identifies sub-phenotypes and reveals novel outcome predictors in patients with dialysis-requiring sepsis-associated acute kidney injury. Ann Med 2023; 55:2197290. [PMID: 37043222 PMCID: PMC10101673 DOI: 10.1080/07853890.2023.2197290] [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: 07/22/2022] [Accepted: 03/25/2023] [Indexed: 04/13/2023] Open
Abstract
INTRODUCTION Heterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This study aimed to perform unsupervised consensus clustering in critically ill patients with dialysis-requiring SA-AKI. PATIENTS AND METHODS This prospective observational cohort study included all septic patients, defined by the Sepsis-3 criteria, with dialysis-requiring SA-AKI in surgical intensive care units in Taiwan between 2009 and 2018. We employed unsupervised consensus clustering based on 23 clinical variables upon initializing renal replacement therapy. Multivariate-adjusted Cox regression models and Fine-Gray sub-distribution hazard models were built to test associations between cluster memberships with mortality and being free of dialysis at 90 days after hospital discharge, respectively. RESULTS Consensus clustering among 999 enrolled patients identified three sub-phenotypes characterized with distinct clinical manifestations upon renal replacement therapy initiation (n = 352, 396 and 251 in cluster 1, 2 and 3, respectively). They were followed for a median of 48 (interquartile range 9.5-128.5) days. Phenotypic cluster 1, featured by younger age, lower Charlson Comorbidity Index, higher baseline estimated glomerular filtration rate but with higher severity of acute illness was associated with an increased risk of death (adjusted hazard ratio of 3.05 [95% CI, 2.35-3.97]) and less probability to become free of dialysis (adjusted sub-distribution hazard ratio of 0.55 [95% CI, 0.38-0.8]) than cluster 3. By examining distinct features of the sub-phenotypes, we discovered that pre-dialysis hyperlactatemia ≥3.3 mmol/L was an independent outcome predictor. A clinical model developed to determine high-risk sub-phenotype 1 in this cohort (C-static 0.99) can identify a sub-phenotype with high in-hospital mortality risk (adjusted hazard ratio of 1.48 [95% CI, 1.25-1.74]) in another independent multi-centre SA-AKI cohort. CONCLUSIONS Our data-driven approach suggests sub-phenotypes with clinical relevance in dialysis-requiring SA-AKI and serves an outcome predictor. This strategy represents further development toward precision medicine in the definition of high-risk sub-phenotype in patients with SA-AKI.Key messagesUnsupervised consensus clustering can identify sub-phenotypes of patients with SA-AKI and provide a risk prediction.Examining the features of patient heterogeneity contributes to the discovery of serum lactate levels ≥ 3.3 mmol/L upon initializing RRT as an independent outcome predictor.This data-driven approach can be useful for prognostication and lead to a better understanding of therapeutic strategies in heterogeneous clinical syndromes.
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Affiliation(s)
- Chun-Fu Lai
- Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
| | - Jung-Hua Liu
- Department of Communication, National Chung Cheng University, Minhsiung, Taiwan
| | - Li-Jung Tseng
- Department of Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Chun-Hao Tsao
- Department of Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Nai-Kuan Chou
- Department of Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Shuei-Liong Lin
- Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
- Graduate Institute of Physiology, National Taiwan University College of Medicine, Taipei City, Taiwan
| | - Yung-Ming Chen
- Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
- National Taiwan University Hospital Bei-Hu Branch, Taipei City, Taiwan
| | - Vin-Cent Wu
- Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
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Bhatraju PK, Prince DK, Mansour S, Ikizler TA, Siew ED, Chinchilli VM, Garg AX, Go AS, Kaufman JS, Kimmel PL, Coca SG, Parikh CR, Wurfel MM, Himmelfarb J. Integrated Analysis of Blood and Urine Biomarkers to Identify Acute Kidney Injury Subphenotypes and Associations With Long-term Outcomes. Am J Kidney Dis 2023; 82:311-321.e1. [PMID: 37178093 PMCID: PMC10523857 DOI: 10.1053/j.ajkd.2023.01.449] [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: 08/29/2022] [Accepted: 01/15/2023] [Indexed: 05/15/2023]
Abstract
RATIONALE & OBJECTIVE Acute kidney injury (AKI) is a heterogeneous clinical syndrome with varying causes, pathophysiology, and outcomes. We incorporated plasma and urine biomarker measurements to identify AKI subgroups (subphenotypes) more tightly linked to underlying pathophysiology and long-term clinical outcomes. STUDY DESIGN Multicenter cohort study. SETTING & PARTICIPANTS 769 hospitalized adults with AKI matched with 769 without AKI, enrolled from December 2009 to February 2015 in the ASSESS-AKI Study. PREDICTORS 29 clinical, plasma, and urinary biomarker parameters used to identify AKI subphenotypes. OUTCOME Composite of major adverse kidney events (MAKE) with a median follow-up period of 4.7 years. ANALYTICAL APPROACH Latent class analysis (LCA) and k-means clustering were applied to 29 clinical, plasma, and urinary biomarker parameters. Associations between AKI subphenotypes and MAKE were analyzed using Kaplan-Meier curves and Cox proportional hazard models. RESULTS Among 769 AKI patients both LCA and k-means identified 2 distinct AKI subphenotypes (classes 1 and 2). The long-term risk for MAKE was higher with class 2 (adjusted HR, 1.41 [95% CI, 1.08-1.84]; P=0.01) compared with class 1, adjusting for demographics, hospital level factors, and KDIGO stage of AKI. The higher risk of MAKE among class 2 was explained by a higher risk of long-term chronic kidney disease progression and dialysis. The top variables that were different between classes 1 and 2 included plasma and urinary biomarkers of inflammation and epithelial cell injury; serum creatinine ranked 20th out of the 29 variables for differentiating classes. LIMITATIONS A replication cohort with simultaneously collected blood and urine sampling in hospitalized adults with AKI and long-term outcomes was unavailable. CONCLUSIONS We identify 2 molecularly distinct AKI subphenotypes with differing risk of long-term outcomes, independent of the current criteria to risk stratify AKI. Future identification of AKI subphenotypes may facilitate linking therapies to underlying pathophysiology to prevent long-term sequalae after AKI. PLAIN-LANGUAGE SUMMARY Acute kidney injury (AKI) occurs commonly in hospitalized patients and is associated with high morbidity and mortality. The AKI definition lumps many different types of AKI together, but subgroups of AKI may be more tightly linked to the underlying biology and clinical outcomes. We used 29 different clinical, blood, and urinary biomarkers and applied 2 different statistical algorithms to identify AKI subtypes and their association with long-term outcomes. Both clustering algorithms identified 2 AKI subtypes with different risk of chronic kidney disease, independent of the serum creatinine concentrations (the current gold standard to determine severity of AKI). Identification of AKI subtypes may facilitate linking therapies to underlying biology to prevent long-term consequences after AKI.
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Affiliation(s)
- Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington; Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington.
| | - David K Prince
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Sherry Mansour
- Division of Nephrology, Yale University, New Haven, Connecticut
| | - T Alp Ikizler
- Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Edward D Siew
- Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Vernon M Chinchilli
- Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania
| | - Amit X Garg
- Division of Nephrology, Department of Medicine, Western University, London, Ontario, Canada
| | - Alan S Go
- Division of Nephrology, Department of Medicine, University of California, San Francisco, California; Department of Epidemiology and Biostatistics, University of California, San Francisco, California; Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - James S Kaufman
- Division of Nephrology, School of Medicine, New York University, New York, New York; Division of Nephrology, VA New York Harbor Healthcare System, New York, New York
| | - Paul L Kimmel
- National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Steve G Coca
- Section of Nephrology, Department of Internal Medicine, Mount Sinai School of Medicine, New York, New York
| | - Chirag R Parikh
- Division of Nephrology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Mark M Wurfel
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington; Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Jonathan Himmelfarb
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
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Dai M, Zhang C, Li C, Wang Q, Gao C, Yue R, Yao M, Su Z, Zheng Z. Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model. Arthritis Res Ther 2023; 25:155. [PMID: 37612772 PMCID: PMC10463535 DOI: 10.1186/s13075-023-03139-y] [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: 04/11/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND Pulmonary arterial hypertension (PAH) is a severe complication of systemic lupus erythematosus (SLE). This study aims to explore the clinical characteristics and prognosis in SLE-PAH based on consensus clustering and risk prediction model. METHODS A total of 205 PAH (including 163 SLE-PAH and 42 idiopathic PAH) patients were enrolled retrospectively based on medical records at the First Affiliated Hospital of Zhengzhou University from July 2014 to June 2021. Unsupervised consensus clustering was used to identify SLE-PAH subtypes that best represent the data pattern. The Kaplan-Meier survival was analyzed in different subtypes. Besides, the least absolute shrinkage and selection operator combined with Cox proportional hazards regression model were performed to construct the SLE-PAH risk prediction model. RESULTS Clustering analysis defined two subtypes, cluster 1 (n = 134) and cluster 2 (n = 29). Compared with cluster 1, SLE-PAH patients in cluster 2 had less favorable levels of poor cardiac, kidney, and coagulation function markers, with higher SLE disease activity, less frequency of PAH medications, and lower survival rate within 2 years (86.2% vs. 92.8%) (P < 0.05). The risk prediction model was also constructed, including older age at diagnosis (≥ 38 years), anti-dsDNA antibody, neuropsychiatric lupus, and platelet distribution width (PDW). CONCLUSIONS Consensus clustering identified two distinct SLE-PAH subtypes which were associated with survival outcomes. Four prognostic factors for death were discovered to construct the SLE-PAH risk prediction model.
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Affiliation(s)
- Mengmeng Dai
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chunyi Zhang
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chaoying Li
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qianqian Wang
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Congcong Gao
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Runzhi Yue
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Menghui Yao
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhaohui Su
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhaohui Zheng
- Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Thongprayoon C, Miao J, Jadlowiec CC, Mao SA, Mao MA, Leeaphorn N, Kaewput W, Pattharanitima P, Tangpanithandee S, Krisanapan P, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Differences between Kidney Transplant Recipients from Deceased Donors with Diabetes Mellitus as Identified by Machine Learning Consensus Clustering. J Pers Med 2023; 13:1094. [PMID: 37511707 PMCID: PMC10381319 DOI: 10.3390/jpm13071094] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/21/2023] [Accepted: 07/01/2023] [Indexed: 07/30/2023] Open
Abstract
Clinical outcomes of deceased donor kidney transplants coming from diabetic donors currently remain inconsistent, possibly due to high heterogeneities in this population. Our study aimed to cluster recipients of diabetic deceased donor kidney transplants using an unsupervised machine learning approach in order to identify subgroups with high risk of inferior outcomes and potential variables associated with these outcomes. Consensus cluster analysis was performed based on recipient-, donor-, and transplant-related characteristics in 7876 recipients of diabetic deceased donor kidney transplants from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between the clusters. Consensus cluster analysis identified three clinically distinct clusters. Recipients in cluster 1 (n = 2903) were characterized by oldest age (64 ± 8 years), highest rate of comorbid diabetes mellitus (55%). They were more likely to receive kidney allografts from donors that were older (58 ± 6.3 years), had hypertension (89%), met expanded criteria donor (ECD) status (78%), had a high rate of cerebrovascular death (63%), and carried a high kidney donor profile index (KDPI). Recipients in cluster 2 (n = 687) were younger (49 ± 13 years) and all were re-transplant patients with higher panel reactive antibodies (PRA) (88 [IQR 46, 98]) who received kidneys from younger (44 ± 11 years), non-ECD deceased donors (88%) with low numbers of HLA mismatch (4 [IQR 2, 5]). The cluster 3 cohort was characterized by first-time kidney transplant recipients (100%) who received kidney allografts from younger (42 ± 11 years), non-ECD deceased donors (98%). Compared to cluster 3, cluster 1 had higher incidence of primary non-function, delayed graft function, patient death and death-censored graft failure, whereas cluster 2 had higher incidence of delayed graft function and death-censored graft failure but comparable primary non-function and patient death. An unsupervised machine learning approach characterized diabetic donor kidney transplant patients into three clinically distinct clusters with differing outcomes. Our data highlight opportunities to improve utilization of high KDPI kidneys coming from diabetic donors in recipients with survival-limiting comorbidities such as those observed in cluster 1.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Napat Leeaphorn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | | | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Tsao HM, Lai TS, Chou YH, Lin SL, Chen YM. Predialysis trajectories of estimated GFR and concurrent trends of Chronic Kidney Disease-relevant biomarkers. Ther Adv Chronic Dis 2023; 14:20406223231177291. [PMID: 37324405 PMCID: PMC10265358 DOI: 10.1177/20406223231177291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 05/04/2023] [Indexed: 06/17/2023] Open
Abstract
Background The glomerular filtration rate (GFR) decline varies in patients with advanced chronic kidney disease (CKD), and the concurrent changes in CKD-related biomarkers are unclear. Objectives This study aimed to examine the changes in CKD-related biomarkers along with the kidney function decline in various GFR trajectory groups. Design This study was a longitudinal cohort study originated from the pre-end-stage renal disease (pre-ESRD) care program in a single tertiary center between 2006 and 2019. Methods We adopted a group-based trajectory model to categorize CKD patients into three trajectories according to estimated glomerular filtration rate (eGFR) changes. A repeated-measures linear mixed model was used to estimate the concurrent biomarker trends in a 2-year period before dialysis and to examine the differences among trajectory groups. A total of 15 biomarkers were analyzed, including urine protein, serum uric acid, albumin, lipid, electrolytes, and hematologic markers. Results Using longitudinal data from 2 years before dialysis initiation, 1758 CKD patients were included. We identified three distinct eGFR trajectories: persistently low eGFR levels, progressive loss of eGFR, and accelerated loss of eGFR. Eight of the 15 biomarkers showed distinct patterns among the trajectory groups. Compared with the group with persistently low eGFR values, the other two groups were associated with a more rapid increase in the blood urea nitrogen (BUN) level and urine protein-creatinine ratio (UPCR), especially in the year before dialysis initiation, and a more rapid decline in hemoglobin and platelet counts. A rapid eGFR decline was associated with lower levels of albumin and potassium, and higher levels of mean corpuscular hemoglobin concentration (MCHC) and white blood cell (WBC). The albumin level in the group with an accelerated loss of eGFR was below the normal range. Conclusion Using longitudinal data, we delineated the changes in CKD biomarkers with disease progression. The results provide information to clinicians and clues to elucidate the mechanism of CKD progression.
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Affiliation(s)
- Hsiao-Mei Tsao
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Tai-Shuan Lai
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, No.7, Chung-Shan S. Rd, Taipei 100225
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Yu-Hsiang Chou
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shuei-Liong Lin
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Yung-Ming Chen
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital Bei-Hu Branch, Taipei, Taiwan
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9
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Chu CD, Tuot DS, Tummalapalli SL. Kidney Function Trajectories and Health Care Costs: Identifying High-Need, High-Cost Patients. Kidney Med 2023; 5:100664. [PMID: 37250504 PMCID: PMC10209529 DOI: 10.1016/j.xkme.2023.100664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023] Open
Affiliation(s)
- Chi D. Chu
- Department of Medicine, University of California, San Francisco, California
- Department of Medicine, Priscilla Chan and Mark Zuckerberg San Francisco General Hospital, San Francisco, California
- Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, California and San Francisco VA Health Care System, San Francisco, California
- Division of Nephrology, Zuckerberg San Francisco General Hospital, San Francisco, California
| | - Delphine S. Tuot
- Department of Medicine, University of California, San Francisco, California
- Department of Medicine, Priscilla Chan and Mark Zuckerberg San Francisco General Hospital, San Francisco, California
- Division of Nephrology, Zuckerberg San Francisco General Hospital, San Francisco, California
| | - Sri Lekha Tummalapalli
- Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, California and San Francisco VA Health Care System, San Francisco, California
- Division of Healthcare Delivery Science & Innovation, Department of Population Health Sciences, and Division of Nephrology & Hypertension, Department of Medicine, Weill Cornell Medicine, New York, New York
- The Rogosin Institute, New York, New York
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10
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Diamantidis CJ, Storfer-Isser A, Fishman E, Wang V, Zepel L, Maciejewski ML. Costs Associated With Progression of Mildly Reduced Kidney Function Among Medicare Advantage Enrollees. Kidney Med 2023; 5:100636. [PMID: 37250500 PMCID: PMC10220400 DOI: 10.1016/j.xkme.2023.100636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023] Open
Abstract
Rationale & Objective The prevalence of early chronic kidney disease (CKD) in older adults has increased in the past 2 decades, yet CKD disease progression, overall, is variable. It is unclear whether health care costs differ by progression trajectory. The purpose of this study was to estimate the trajectories of CKD progression and examine Medicare Advantage (MA) health care costs of each trajectory over a 3-year period in a large cohort of MA enrollees with mildly reduced kidney function. Study Design Cohort study. Setting & Population 421,187 MA enrollees with stage G2 CKD in 2014-2017. Outcomes We identified 5 trajectories of kidney function over time. Model Perspective & Timeframe Mean total health care costs for each of the trajectories were described in each of the following 3 years from a payer perspective: 1 year before and 2 years after the index date establishing stage G2 CKD (study entry). Results The mean estimated glomerular filtration rate (eGFR) at study entry was 75.9 mL/min/1.73 m2 and the median (interquartile range) follow-up period was 2.6 (1.6, 3.7) years. The cohort had a mean age of 72.6 years and had predominantly female participants (57.2%), and White (71.2%). We identified the following 5 distinct trajectories of kidney function: a stable eGFR (22.3%); slow eGFR decline with a mean eGFR at study entry of 78.6 (30.2%); slow eGFR decline with an eGFR at study entry of 70.9 (28.4%); steep eGFR decline (16.3%); and accelerated eGFR decline (2.8%). Mean costs of enrollees with accelerated eGFR decline were double the MA enrollees' mean costs in each of the other 4 trajectories in every year ($27,738 vs $13,498 for a stable eGFR 1 year after study entry). Limitations Results may not generalized beyond MA and a lack of albumin values. Conclusions The small fraction of MA enrollees with accelerated eGFR decline has disproportionately higher costs than other enrollees with mildly reduced kidney function.
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Affiliation(s)
- Clarissa J. Diamantidis
- Division of General Internal Medicine, Duke University School of Medicine, Durham, North Carolina
- Division of Nephrology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | | | - Ezra Fishman
- National Committee for Quality Assurance, Washington DC
- Optum Labs, Minneapolis, Minnesota
| | - Virginia Wang
- Division of General Internal Medicine, Duke University School of Medicine, Durham, North Carolina
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina
- Duke-Margolis Center for Health Policy, Duke University, Durham, North Carolina
| | - Lindsay Zepel
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
- Optum Labs, Minneapolis, Minnesota
| | - Matthew L. Maciejewski
- Division of General Internal Medicine, Duke University School of Medicine, Durham, North Carolina
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina
- Duke-Margolis Center for Health Policy, Duke University, Durham, North Carolina
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11
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Thongprayoon C, Miao J, Jadlowiec CC, Mao SA, Mao MA, Vaitla P, Leeaphorn N, Kaewput W, Pattharanitima P, Tangpanithandee S, Krisanapan P, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59050977. [PMID: 37241209 DOI: 10.3390/medicina59050977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 05/10/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023]
Abstract
Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Napat Leeaphorn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | | | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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12
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Walther CP, Benoit JS, Bansal N, Nambi V, Navaneethan SD. Heart Failure-Type Symptom Score Trajectories in CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis 2023; 81:446-456. [PMID: 36403887 PMCID: PMC10038859 DOI: 10.1053/j.ajkd.2022.09.016] [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: 07/14/2022] [Accepted: 09/23/2022] [Indexed: 11/19/2022]
Abstract
RATIONALE & OBJECTIVE Quality of life in chronic kidney disease (CKD) is impaired by a large burden of symptoms including some that overlap with the symptoms of heart failure (HF). We studied a group of individuals with CKD to understand the patterns and trajectories of HF-type symptoms in this setting. STUDY DESIGN Prospective cohort study. SETTING & PARTICIPANTS 3,044 participants in the Chronic Renal Insufficiency Cohort (CRIC) without prior diagnosis of HF. PREDICTORS Sociodemographics, medical history, medications, vital signs, laboratory values, echocardiographic and electrocardiographic parameters. OUTCOME Trajectory over 5.5 years of a HF-type symptom score (modified Kansas City Cardiomyopathy Questionnaire [KCCQ] Overall Summary Score with a range of 0-100 where<75 reflects clinically significant symptoms). ANALYTICAL APPROACH Latent class mixed models were used to model trajectories. Multinomial logistic regression was used to model relationships of predictors with trajectory group membership. RESULTS Five trajectories of KCCQ score were identified in the cohort of 3,044 adults, 45% of whom were female, and whose median age was 61 years. Group 1 (41.7%) had a stable high score (minimal symptoms, average score of 96); groups 2 (35.6%) and 3 (15.6%) had stable but lower scores (mild symptoms [average of 81] and clinically significant symptoms [average of 52], respectively). Group 4 (4.9%) had a substantial worsening in symptoms over time (mean 31-point decline), and group 5 (2.2%) had a substantial improvement (mean 33-point increase) in KCCQ score. A majority of group 1 was male, without diabetes or obesity, and this group had higher baseline kidney function. A majority of groups 2 and 3 had diabetes and obesity. A majority of group 4 was male and had substantial proteinuria. Group 5 had the highest proportion of baseline cardiovascular disease (CVD). LIMITATIONS No validation cohort available, CKD management changes in recent years may alter trajectories, and latent class models depend on the missing at random assumption. CONCLUSIONS Distinct HF-type symptom burden trajectories were identified in the setting of CKD, corresponding to different baseline characteristics. These results highlight the diversity of HF-type symptom experiences in individuals with CKD.
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Affiliation(s)
- Carl P Walther
- Selzman Institute for Kidney Health, Section of Nephrology, Baylor College of Medicine, Houston, Texas.
| | - Julia S Benoit
- Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, Texas
| | - Nisha Bansal
- Kidney Research Institute and Division of Nephrology, University of Washington, Seattle, Washington
| | - Vijay Nambi
- Section of Cardiovascular Research, Baylor College of Medicine, Houston, Texas; Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas
| | - Sankar D Navaneethan
- Selzman Institute for Kidney Health, Section of Nephrology, Baylor College of Medicine, Houston, Texas; Institute of Clinical and Translational Research, Baylor College of Medicine, Houston, Texas; Section of Nephrology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas
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13
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Thongprayoon C, Vaitla P, Jadlowiec CC, Leeaphorn N, Mao SA, Mao MA, Qureshi F, Kaewput W, Qureshi F, Tangpanithandee S, Krisanapan P, Pattharanitima P, Acharya PC, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering. MEDICINES (BASEL, SWITZERLAND) 2023; 10:medicines10040025. [PMID: 37103780 PMCID: PMC10144541 DOI: 10.3390/medicines10040025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 03/24/2023] [Indexed: 04/28/2023]
Abstract
BACKGROUND Better understanding of the different phenotypes/subgroups of non-U.S. citizen kidney transplant recipients may help the transplant community to identify strategies that improve outcomes among non-U.S. citizen kidney transplant recipients. This study aimed to cluster non-U.S. citizen kidney transplant recipients using an unsupervised machine learning approach; Methods: We conducted a consensus cluster analysis based on recipient-, donor-, and transplant- related characteristics in non-U.S. citizen kidney transplant recipients in the United States from 2010 to 2019 in the OPTN/UNOS database using recipient, donor, and transplant-related characteristics. Each cluster's key characteristics were identified using the standardized mean difference. Post-transplant outcomes were compared among the clusters; Results: Consensus cluster analysis was performed in 11,300 non-U.S. citizen kidney transplant recipients and identified two distinct clusters best representing clinical characteristics. Cluster 1 patients were notable for young age, preemptive kidney transplant or dialysis duration of less than 1 year, working income, private insurance, non-hypertensive donors, and Hispanic living donors with a low number of HLA mismatch. In contrast, cluster 2 patients were characterized by non-ECD deceased donors with KDPI <85%. Consequently, cluster 1 patients had reduced cold ischemia time, lower proportion of machine-perfused kidneys, and lower incidence of delayed graft function after kidney transplant. Cluster 2 had higher 5-year death-censored graft failure (5.2% vs. 9.8%; p < 0.001), patient death (3.4% vs. 11.4%; p < 0.001), but similar one-year acute rejection (4.7% vs. 4.9%; p = 0.63), compared to cluster 1; Conclusions: Machine learning clustering approach successfully identified two clusters among non-U.S. citizen kidney transplant recipients with distinct phenotypes that were associated with different outcomes, including allograft loss and patient survival. These findings underscore the need for individualized care for non-U.S. citizen kidney transplant recipients.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | | | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke's Health System, Kansas City, MO 64108, USA
| | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Fahad Qureshi
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Pattharawin Pattharanitima
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand
| | - Prakrati C Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Matthew Cooper
- Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 21042, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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14
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Dashtban A, Mizani MA, Pasea L, Denaxas S, Corbett R, Mamza JB, Gao H, Morris T, Hemingway H, Banerjee A. Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals. EBioMedicine 2023; 89:104489. [PMID: 36857859 PMCID: PMC9989643 DOI: 10.1016/j.ebiom.2023.104489] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Although chronic kidney disease (CKD) is associated with high multimorbidity, polypharmacy, morbidity and mortality, existing classification systems (mild to severe, usually based on estimated glomerular filtration rate, proteinuria or urine albumin-creatinine ratio) and risk prediction models largely ignore the complexity of CKD, its risk factors and its outcomes. Improved subtype definition could improve prediction of outcomes and inform effective interventions. METHODS We analysed individuals ≥18 years with incident and prevalent CKD (n = 350,067 and 195,422 respectively) from a population-based electronic health record resource (2006-2020; Clinical Practice Research Datalink, CPRD). We included factors (n = 264 with 2670 derived variables), e.g. demography, history, examination, blood laboratory values and medications. Using a published framework, we identified subtypes through seven unsupervised machine learning (ML) methods (K-means, Diana, HC, Fanny, PAM, Clara, Model-based) with 66 (of 2670) variables in each dataset. We evaluated subtypes for: (i) internal validity (within dataset, across methods); (ii) prognostic validity (predictive accuracy for 5-year all-cause mortality and admissions); and (iii) medications (new and existing by British National Formulary chapter). FINDINGS After identifying five clusters across seven approaches, we labelled CKD subtypes: 1. Early-onset, 2. Late-onset, 3. Cancer, 4. Metabolic, and 5. Cardiometabolic. Internal validity: We trained a high performing model (using XGBoost) that could predict disease subtypes with 95% accuracy for incident and prevalent CKD (Sensitivity: 0.81-0.98, F1 score:0.84-0.97). Prognostic validity: 5-year all-cause mortality, hospital admissions, and incidence of new chronic diseases differed across CKD subtypes. The 5-year risk of mortality and admissions in the overall incident CKD population were highest in cardiometabolic subtype: 43.3% (42.3-42.8%) and 29.5% (29.1-30.0%), respectively, and lowest in the early-onset subtype: 5.7% (5.5-5.9%) and 18.7% (18.4-19.1%). MEDICATIONS Across CKD subtypes, the distribution of prescription medication classes at baseline varied, with highest medication burden in cardiometabolic and metabolic subtypes, and higher burden in prevalent than incident CKD. INTERPRETATION In the largest CKD study using ML, to-date, we identified five distinct subtypes in individuals with incident and prevalent CKD. These subtypes have relevance to study of aetiology, therapeutics and risk prediction. FUNDING AstraZeneca UK Ltd, Health Data Research UK.
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Affiliation(s)
- Ashkan Dashtban
- Institute of Health Informatics, University College London, London, UK
| | - Mehrdad A Mizani
- Institute of Health Informatics, University College London, London, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Laura Pasea
- Institute of Health Informatics, University College London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | | | - Jil B Mamza
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, London, UK
| | - He Gao
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, London, UK
| | - Tamsin Morris
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK; Barts Health NHS Trust, London, UK; University College London Hospitals NHS Trust, London, UK.
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15
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Mariani LH, Eddy S, AlAkwaa FM, McCown PJ, Harder JL, Nair V, Eichinger F, Martini S, Ademola AD, Boima V, Reich HN, El Saghir J, Godfrey B, Ju W, Tanner EC, Vega-Warner V, Wys NL, Adler SG, Appel GB, Athavale A, Atkinson MA, Bagnasco SM, Barisoni L, Brown E, Cattran DC, Coppock GM, Dell KM, Derebail VK, Fervenza FC, Fornoni A, Gadegbeku CA, Gibson KL, Greenbaum LA, Hingorani SR, Hladunewich MA, Hodgin JB, Hogan MC, Holzman LB, Jefferson JA, Kaskel FJ, Kopp JB, Lafayette RA, Lemley KV, Lieske JC, Lin JJ, Menon R, Meyers KE, Nachman PH, Nast CC, O'Shaughnessy MM, Otto EA, Reidy KJ, Sambandam KK, Sedor JR, Sethna CB, Singer P, Srivastava T, Tran CL, Tuttle KR, Vento SM, Wang CS, Ojo AO, Adu D, Gipson DS, Trachtman H, Kretzler M. Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis. Kidney Int 2023; 103:565-579. [PMID: 36442540 DOI: 10.1016/j.kint.2022.10.023] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 10/25/2022] [Accepted: 10/28/2022] [Indexed: 11/27/2022]
Abstract
The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.
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Affiliation(s)
- Laura H Mariani
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
| | - Sean Eddy
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Fadhl M AlAkwaa
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Phillip J McCown
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Jennifer L Harder
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Viji Nair
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Felix Eichinger
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Sebastian Martini
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Adebowale D Ademola
- Department of Paediatrics, Faculty of Clinical Sciences, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
| | - Vincent Boima
- Department of Medicine and Therapeutics, University of Ghana Medical School, College of Health Sciences, University of Ghana, Accra, Ghana
| | - Heather N Reich
- Division of Nephrology, Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Jamal El Saghir
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Bradley Godfrey
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Wenjun Ju
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Emily C Tanner
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Virginia Vega-Warner
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Noel L Wys
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Sharon G Adler
- Division of Nephrology and Hypertension at Harbor-UCLA Medical Center and The Lundquist Institute for Biomedical Innovation, Torrance, California, USA
| | - Gerald B Appel
- Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Ambarish Athavale
- Division of Nephrology-Hypertension, University of San Diego, California, San Diego, California, USA
| | - Meredith A Atkinson
- Division of Pediatric Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Serena M Bagnasco
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Laura Barisoni
- Department of Pathology and Medicine, Division of Nephrology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Elizabeth Brown
- Division of Nephrology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Daniel C Cattran
- Division of Nephrology, Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Gaia M Coppock
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Katherine M Dell
- Center for Pediatric Nephrology, Cleveland Clinic, Case Western Reserve University, Cleveland, Ohio, USA
| | - Vimal K Derebail
- University of North Carolina Kidney Center, Division of Nephrology and Hypertension, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Fernando C Fervenza
- Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Alessia Fornoni
- Katz Family Division of Nephrology and Hypertension, Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Crystal A Gadegbeku
- Department of Kidney Medicine, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Keisha L Gibson
- Pediatric Nephrology Division, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Laurence A Greenbaum
- Division of Nephrology, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Sangeeta R Hingorani
- Division of Nephrology, Department of Pediatrics, University of Washington, Seattle, Washington, USA
| | - Michelle A Hladunewich
- Division of Nephrology, Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Jeffrey B Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Marie C Hogan
- Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Lawrence B Holzman
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - J Ashley Jefferson
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Frederick J Kaskel
- Division of Pediatric Nephrology, Montefiore Medical Center, Bronx, New York, USA
| | - Jeffrey B Kopp
- National Institute of Diabetes and Digestive Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Richard A Lafayette
- Department of Medicine, Division of Nephrology, Stanford University, Stanford, California, USA
| | - Kevin V Lemley
- Department of Pediatrics, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - John C Lieske
- Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jen-Jar Lin
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Rajarasee Menon
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kevin E Meyers
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Patrick H Nachman
- Division of Nephrology and Hypertension, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Cynthia C Nast
- Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | | | - Edgar A Otto
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Kimberly J Reidy
- Division of Pediatric Nephrology, Montefiore Medical Center, Bronx, New York, USA
| | - Kamalanathan K Sambandam
- Division of Nephrology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, USA; Division of Nephrology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - John R Sedor
- Lerner Research Institutes, Cleveland Clinic, Cleveland, Ohio, USA; Department of Molecular Medicine, Case Western Reserve University, Cleveland, Ohio, USA; Department of Physiology, Case Western Reserve University, Cleveland, Ohio, USA; Department of Biophysics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Christine B Sethna
- Division of Pediatric Nephrology, Cohen Children's Medical Center, New Hyde Park, New York, USA
| | - Pamela Singer
- Division of Pediatric Nephrology, Cohen Children's Medical Center, New Hyde Park, New York, USA
| | - Tarak Srivastava
- Section of Nephrology, Children's Mercy Hospital, Kansas City, Missouri, USA
| | - Cheryl L Tran
- Pediatric Nephrology, Mayo Clinic, Rochester, Minnesota, USA
| | - Katherine R Tuttle
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington, USA; Providence Medical Research Center, Providence Health Care, University of Washington, Spokane, Washington, USA
| | - Suzanne M Vento
- Division of Nephrology, Department of Pediatrics, New York University School of Medicine, New York, New York, USA
| | - Chia-Shi Wang
- Division of Nephrology, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Akinlolu O Ojo
- Department of Population Health, School of Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Dwomoa Adu
- Department of Medicine and Therapeutics, University of Ghana Medical School, College of Health Sciences, University of Ghana, Accra, Ghana
| | - Debbie S Gipson
- Division of Nephrology, Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, USA
| | - Howard Trachtman
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthias Kretzler
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1399] [Impact Index Per Article: 1399.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Thongprayoon C, Jadlowiec CC, Mao SA, Mao MA, Leeaphorn N, Kaewput W, Pattharanitima P, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering. BMJ SURGERY, INTERVENTIONS, & HEALTH TECHNOLOGIES 2023; 5:e000137. [PMID: 36843871 PMCID: PMC9944353 DOI: 10.1136/bmjsit-2022-000137] [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] [Received: 02/26/2022] [Accepted: 02/05/2023] [Indexed: 02/22/2023] Open
Abstract
Objectives This study aimed to identify distinct clusters of very elderly kidney transplant recipients aged ≥80 and assess clinical outcomes among these unique clusters. Design Cohort study with machine learning (ML) consensus clustering approach. Setting and participants All very elderly (age ≥80 at time of transplant) kidney transplant recipients in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database database from 2010 to 2019. Main outcome measures Distinct clusters of very elderly kidney transplant recipients and their post-transplant outcomes including death-censored graft failure, overall mortality and acute allograft rejection among the assigned clusters. Results Consensus cluster analysis was performed in 419 very elderly kidney transplant and identified three distinct clusters that best represented the clinical characteristics of very elderly kidney transplant recipients. Recipients in cluster 1 received standard Kidney Donor Profile Index (KDPI) non-extended criteria donor (ECD) kidneys from deceased donors. Recipients in cluster 2 received kidneys from older, hypertensive ECD deceased donors with a KDPI score ≥85%. Kidneys for cluster 2 patients had longer cold ischaemia time and the highest use of machine perfusion. Recipients in clusters 1 and 2 were more likely to be on dialysis at the time of transplant (88.3%, 89.4%). Recipients in cluster 3 were more likely to be preemptive (39%) or had a dialysis duration less than 1 year (24%). These recipients received living donor kidney transplants. Cluster 3 had the most favourable post-transplant outcomes. Compared with cluster 3, cluster 1 had comparable survival but higher death-censored graft failure, while cluster 2 had lower patient survival, higher death-censored graft failure and more acute rejection. Conclusions Our study used an unsupervised ML approach to cluster very elderly kidney transplant recipients into three clinically unique clusters with distinct post-transplant outcomes. These findings from an ML clustering approach provide additional understanding towards individualised medicine and opportunities to improve care for very elderly kidney transplant recipients.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, Florida, USA
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Napat Leeaphorn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA,Renal Transplant Program, Saint Luke's Health System, Kansas City, Missouri, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
| | | | | | - Matthew Cooper
- Division of Transplant, Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
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18
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Thongprayoon C, Vaitla P, Jadlowiec CC, Mao SA, Mao MA, Acharya PC, Leeaphorn N, Kaewput W, Pattharanitima P, Tangpanithandee S, Krisanapan P, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Differences between kidney retransplant recipients as identified by machine learning consensus clustering. Clin Transplant 2023; 37:e14943. [PMID: 36799718 DOI: 10.1111/ctr.14943] [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: 05/28/2022] [Revised: 08/13/2022] [Accepted: 02/11/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND Our study aimed to characterize kidney retransplant recipients using an unsupervised machine-learning approach. METHODS We performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 17 443 kidney retransplant recipients in the OPTN/UNOS database from 2010 to 2019. We identified each cluster's key characteristics using the standardized mean difference of >.3. We compared the posttransplant outcomes, including death-censored graft failure and patient death among the assigned clusters RESULTS: Consensus cluster analysis identified three distinct clusters of kidney retransplant recipients. Cluster 1 recipients were predominantly white and were less sensitized. They were most likely to receive a living donor kidney transplant and more likely to be preemptive (30%) or need ≤1 year of dialysis (32%). In contrast, cluster 2 recipients were the most sensitized (median PRA 95%). They were more likely to have been on dialysis >1 year, and receive a nationally allocated, low HLA mismatch, standard KDPI deceased donor kidney. Recipients in cluster 3 were more likely to be minorities (37% Black; 15% Hispanic). They were moderately sensitized with a median PRA of 87% and were also most likely to have been on dialysis >1 year. They received locally allocated high HLA mismatch kidneys from standard KDPI deceased donors. Thymoglobulin was the most commonly used induction agent for all three clusters. Cluster 1 had the most favorable patient and graft survival, while cluster 3 had the worst patient and graft survival. CONCLUSION The use of an unsupervised machine learning approach characterized kidney retransplant recipients into three clinically distinct clusters with differing posttransplant outcomes. Recipients with moderate allosensitization, such as those represented in cluster 3, are perhaps more disadvantaged in the kidney retransplantation process. Potential opportunities for improvement specific to these re-transplant recipients include working to improve opportunities to improve access to living donor kidney transplantation, living donor paired exchange and identifying strategies for better HLA matching.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | | | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, Florida, USA
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Prakrati C Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, Texas, USA
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke's Health System, Kansas City, Missouri, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
| | | | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.,Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Matthew Cooper
- Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Yan P, Ke B, Song J, Fang X. Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis. Front Genet 2023; 14:1111976. [PMID: 36814902 PMCID: PMC9939663 DOI: 10.3389/fgene.2023.1111976] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Background: Chronic kidney disease (CKD) is a heterogeneous disease with multiple etiologies, risk factors, clinical manifestations, and prognosis. The aim of this study was to identify different immune-related molecular clusters in CKD, their functional immunological properties, and to screen for promising diagnostic markers. Methods: Datasets of 440 CKD patients were obtained from the comprehensive gene expression database. The core immune-related genes (IRGs) were identified by weighted gene co-expression network analysis. We used unsupervised clustering to divide CKD samples into two immune-related subclusters. Then, functional enrichment analysis was performed for differentially expressed genes (DEGs) between clusters. Three machine learning methods (LASSO, RF, and SVM-RFE) and Venn diagrams were applied to filter out 5 significant IRGs with distinguished subtypes. A nomogram diagnostic model was developed, and the prediction effect was verified using calibration curve, decision curve analysis. CIBERSORT was applied to assess the variation in immune cell infiltration among clusters. The expression levels, immune characteristics and immune cell correlation of core diagnostic markers were investigated. Finally, the Nephroseq V5 was used to assess the correlation among core diagnostic markers and renal function. Results: The 15 core IRGs screened were differentially expressed in normal and CKD samples. CKD was classified into two immune-related molecular clusters. Cluster 2 is significantly enriched in biological functions such as leukocyte adhesion and regulation as well as immune activation, and has a severe immune prognosis compared to cluster 1. A nomogram diagnostic model with reliable prediction of immune-related clusters was developed based on five signature genes. The core diagnostic markers LYZ, CTSS, and ISG20 were identified as playing an important role in the immune microenvironment and were shown to correlate meaningfully with immune cell infiltration and renal function. Conclusion: Our study identifies two subtypes of CKD with distinct immune gene expression patterns and provides promising predictive models. Along with the exploration of the role of three promising diagnostic markers in the immune microenvironment of CKD, it is anticipated to provide novel breakthroughs in potential targets for disease treatment.
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Díez-Sanmartín C, Cabezuelo AS, Belmonte AA. A new approach to predicting mortality in dialysis patients using sociodemographic features based on artificial intelligence. Artif Intell Med 2023; 136:102478. [PMID: 36710068 DOI: 10.1016/j.artmed.2022.102478] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/30/2022]
Abstract
One of the main problems that affect patients in dialysis therapy who are on the waiting list to receive a kidney transplant is predicting their survival time if they do not receive a transplant. This paper proposes a new approach to survival prediction based on artificial intelligence techniques combined with statistical methods to study the association between sociodemographic factors and patient survival on the waiting list if they do not receive a kidney transplant. This new approach consists of a first stage that uses the clustering techniques that are best suited to the data structure (K-Means, Mini Batch K-Means, Agglomerative Clustering and K-Modes) used to identify the risk profile of dialysis patients. Later, a new method called False Clustering Discovery Reduction is performed to determine the minimum number of populations to be studied, and whose mortality risk is statistically differentiable. This approach was applied to the OPTN medical dataset (n = 44,663). The procedure started from 11 initial clusters obtained with the Agglomerative technique, and was reduced to eight final risk populations, for which their Kaplan-Meier survival curves were provided. With this result, it is possible to make predictions regarding the survival time of a new patient who enters the waiting list if the sociodemographic profile of the patient is known. To do so, the predictive algorithm XGBoost is used, which allows the cluster to which it belongs to be predicted and the corresponding Kaplan-Meier curve to be associated with it. This prediction process is achieved with an overall Multi-class AUC of 99.08 %.
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Affiliation(s)
- Covadonga Díez-Sanmartín
- Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain.
| | - Antonio Sarasa Cabezuelo
- Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain
| | - Amado Andrés Belmonte
- Nephrology Department, 12 de Octubre Hospital, Complutense University of Madrid, 28041 Madrid, Spain.
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Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering. Diseases 2023; 11:diseases11010018. [PMID: 36810532 PMCID: PMC9944494 DOI: 10.3390/diseases11010018] [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: 12/04/2022] [Revised: 01/15/2023] [Accepted: 01/20/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND The utilization of multi-dimensional patient data to subtype hepatorenal syndrome (HRS) can individualize patient care. Machine learning (ML) consensus clustering may identify HRS subgroups with unique clinical profiles. In this study, we aim to identify clinically meaningful clusters of hospitalized patients for HRS using an unsupervised ML clustering approach. METHODS Consensus clustering analysis was performed based on patient characteristics in 5564 patients primarily admitted for HRS in the National Inpatient Sample from 2003-2014 to identify clinically distinct HRS subgroups. We applied standardized mean difference to evaluate key subgroup features, and compared in-hospital mortality between assigned clusters. RESULTS The algorithm revealed four best distinct HRS subgroups based on patient characteristics. Cluster 1 patients (n = 1617) were older, and more likely to have non-alcoholic fatty liver disease, cardiovascular comorbidities, hypertension, and diabetes. Cluster 2 patients (n = 1577) were younger and more likely to have hepatitis C, and less likely to have acute liver failure. Cluster 3 patients (n = 642) were younger, and more likely to have non-elective admission, acetaminophen overdose, acute liver failure, to develop in-hospital medical complications and organ system failure, and to require supporting therapies, including renal replacement therapy, and mechanical ventilation. Cluster 4 patients (n = 1728) were younger, and more likely to have alcoholic cirrhosis and to smoke. Thirty-three percent of patients died in hospital. In-hospital mortality was higher in cluster 1 (OR 1.53; 95% CI 1.31-1.79) and cluster 3 (OR 7.03; 95% CI 5.73-8.62), compared to cluster 2, while cluster 4 had comparable in-hospital mortality (OR 1.13; 95% CI 0.97-1.32). CONCLUSIONS Consensus clustering analysis provides the pattern of clinical characteristics and clinically distinct HRS phenotypes with different outcomes.
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Schmidt IM, Myrick S, Liu J, Verma A, Srivastava A, Palsson R, Onul IF, Stillman IE, Avillach C, Patil P, Waikar SS. The use of plasma biomarker-derived clusters for clinicopathologic phenotyping: results from the Boston Kidney Biopsy Cohort. Clin Kidney J 2023; 16:90-99. [PMID: 36726432 PMCID: PMC9871860 DOI: 10.1093/ckj/sfac202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Indexed: 02/04/2023] Open
Abstract
Background Protein biomarkers may provide insight into kidney disease pathology but their use for the identification of phenotypically distinct kidney diseases has not been evaluated. Methods We used unsupervised hierarchical clustering on 225 plasma biomarkers in 541 individuals enrolled into the Boston Kidney Biopsy Cohort, a prospective cohort study of individuals undergoing kidney biopsy with adjudicated histopathology. Using principal component analysis, we studied biomarker levels by cluster and examined differences in clinicopathologic diagnoses and histopathologic lesions across clusters. Cox proportional hazards models tested associations of clusters with kidney failure and death. Results We identified three biomarker-derived clusters. The mean estimated glomerular filtration rate was 72.9 ± 28.7, 72.9 ± 33.4 and 39.9 ± 30.4 mL/min/1.73 m2 in Clusters 1, 2 and 3, respectively. The top-contributing biomarker in Cluster 1 was AXIN, a negative regulator of the Wnt signaling pathway. The top-contributing biomarker in Clusters 2 and 3 was Placental Growth Factor, a member of the vascular endothelial growth factor family. Compared with Cluster 1, individuals in Cluster 3 were more likely to have tubulointerstitial disease (P < .001) and diabetic kidney disease (P < .001) and had more severe mesangial expansion [odds ratio (OR) 2.44, 95% confidence interval (CI) 1.29, 4.64] and inflammation in the fibrosed interstitium (OR 2.49 95% CI 1.02, 6.10). After multivariable adjustment, Cluster 3 was associated with higher risks of kidney failure (hazard ratio 3.29, 95% CI 1.37, 7.90) compared with Cluster 1. Conclusion Plasma biomarkers may identify clusters of individuals with kidney disease that associate with different clinicopathologic diagnoses, histopathologic lesions and adverse outcomes, and may uncover biomarker candidates and relevant pathways for further study.
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Affiliation(s)
- Insa M Schmidt
- Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA
| | - Steele Myrick
- Boston University School of Public Health, Department of Biostatistics, Boston, MA, USA
| | - Jing Liu
- Division of Nephrology and National Clinical Research Center for Geriatrics, Kidney Research Institute, West China Hospital of Sichuan University, Chengdu, China
| | - Ashish Verma
- Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA
| | - Anand Srivastava
- Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ragnar Palsson
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ingrid F Onul
- Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA
| | - Isaac E Stillman
- Beth Israel Deaconess Medical Center, Harvard Medical School, Department of Pathology, Boston, MA, USA
| | - Claire Avillach
- Boston Medical Center, Department of Pathology, Boston, MA, USA
| | - Prasad Patil
- Boston University School of Public Health, Department of Biostatistics, Boston, MA, USA
| | - Sushrut S Waikar
- Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA
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23
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Jadlowiec CC, Thongprayoon C, Leeaphorn N, Kaewput W, Pattharanitima P, Cooper M, Cheungpasitporn W. Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes. Transpl Int 2022; 35:10810. [PMID: 36568137 PMCID: PMC9773391 DOI: 10.3389/ti.2022.10810] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 11/14/2022] [Indexed: 12/14/2022]
Abstract
Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT) recipients with DGF using OPTN/UNOS data. DGF was observed in 20.9% (n = 17,073) of KT and most kidneys had a KDPI score <85%. Four distinct clusters were identified. Cluster 1 recipients were young, high PRA re-transplants. Cluster 2 recipients were older diabetics and more likely to receive higher KDPI kidneys. Cluster 3 recipients were young, black, and non-diabetic; they received lower KDPI kidneys. Cluster 4 recipients were middle-aged, had diabetes or hypertension and received well-matched standard KDPI kidneys. By cluster, one-year patient survival was 95.7%, 92.5%, 97.2% and 94.3% (p < 0.001); one-year graft survival was 89.7%, 87.1%, 91.6%, and 88.7% (p < 0.001). There were no differences between clusters after accounting for death-censored graft loss (p = 0.08). Clinically meaningful differences in recipient characteristics were noted between clusters, however, after accounting for death and return to dialysis, there were no differences in death-censored graft loss. Greater emphasis on recipient comorbidities as contributors to DGF and outcomes may help improve utilization of DGF at-risk kidneys.
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Affiliation(s)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, United States
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine, Saint Luke’s Health System, Kansas City, MO, United States
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
| | | | - Matthew Cooper
- Medstar Georgetown Transplant Institute, Georgetown University, Washington, DC, United States
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, United States
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Sullivan MK, Carrero JJ, Jani BD, Anderson C, McConnachie A, Hanlon P, Nitsch D, McAllister DA, Mair FS, Mark PB, Gasparini A. The presence and impact of multimorbidity clusters on adverse outcomes across the spectrum of kidney function. BMC Med 2022; 20:420. [PMID: 36320059 PMCID: PMC9623942 DOI: 10.1186/s12916-022-02628-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 10/24/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Multimorbidity (the presence of two or more chronic conditions) is common amongst people with chronic kidney disease, but it is unclear which conditions cluster together and if this changes as kidney function declines. We explored which clusters of conditions are associated with different estimated glomerular filtration rates (eGFRs) and studied associations between these clusters and adverse outcomes. METHODS Two population-based cohort studies were used: the Stockholm Creatinine Measurements project (SCREAM, Sweden, 2006-2018) and the Secure Anonymised Information Linkage Databank (SAIL, Wales, 2006-2021). We studied participants in SCREAM (404,681 adults) and SAIL (533,362) whose eGFR declined lower than thresholds (90, 75, 60, 45, 30 and 15 mL/min/1.73m2). Clusters based on 27 chronic conditions were identified. We described the most common chronic condition(s) in each cluster and studied their association with adverse outcomes using Cox proportional hazards models (all-cause mortality (ACM) and major adverse cardiovascular events (MACE)). RESULTS Chronic conditions became more common and clustered differently across lower eGFR categories. At eGFR 90, 75, and 60 mL/min/1.73m2, most participants were in large clusters with no prominent conditions. At eGFR 15 and 30 mL/min/1.73m2, clusters involving cardiovascular conditions were larger and were at the highest risk of adverse outcomes. At eGFR 30 mL/min/1.73m2, in the heart failure, peripheral vascular disease and diabetes cluster in SCREAM, ACM hazard ratio (HR) is 2.66 (95% confidence interval (CI) 2.31-3.07) and MACE HR is 4.18 (CI 3.65-4.78); in the heart failure and atrial fibrillation cluster in SAIL, ACM HR is 2.23 (CI 2.04 to 2.44) and MACE HR is 3.43 (CI 3.22-3.64). Chronic pain and depression were common and associated with adverse outcomes when combined with physical conditions. At eGFR 30 mL/min/1.73m2, in the chronic pain, heart failure and myocardial infarction cluster in SCREAM, ACM HR is 2.00 (CI 1.62-2.46) and MACE HR is 4.09 (CI 3.39-4.93); in the depression, chronic pain and stroke cluster in SAIL, ACM HR is 1.38 (CI 1.18-1.61) and MACE HR is 1.58 (CI 1.42-1.76). CONCLUSIONS Patterns of multimorbidity and corresponding risk of adverse outcomes varied with declining eGFR. While diabetes and cardiovascular disease are known high-risk conditions, chronic pain and depression emerged as important conditions and associated with adverse outcomes when combined with physical conditions.
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Affiliation(s)
- Michael K Sullivan
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.
| | - Juan-Jesus Carrero
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Bhautesh Dinesh Jani
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Craig Anderson
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Alex McConnachie
- Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Peter Hanlon
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Dorothea Nitsch
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - David A McAllister
- Public Health, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Frances S Mair
- General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Patrick B Mark
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | - Alessandro Gasparini
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Thongprayoon C, Kattah AG, Mao MA, Keddis MT, Pattharanitima P, Vallabhajosyula S, Nissaisorakarn V, Erickson SB, Dillon JJ, Garovic VD, Cheungpasitporn W. Distinct phenotypes of hospitalized patients with hyperkalemia by machine learning consensus clustering and associated mortality risks. QJM 2022; 115:442-449. [PMID: 34270780 DOI: 10.1093/qjmed/hcab194] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/03/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Hospitalized patients with hyperkalemia are heterogeneous, and cluster approaches may identify specific homogenous groups. This study aimed to cluster patients with hyperkalemia on admission using unsupervised machine learning (ML) consensus clustering approach, and to compare characteristics and outcomes among these distinct clusters. METHODS Consensus cluster analysis was performed in 5133 hospitalized adult patients with admission hyperkalemia, based on available clinical and laboratory data. The standardized mean difference was used to identify each cluster's key clinical features. The association of hyperkalemia clusters with hospital and 1-year mortality was assessed using logistic and Cox proportional hazard regression. RESULTS Three distinct clusters of hyperkalemia patients were identified using consensus cluster analysis: 1661 (32%) in cluster 1, 2455 (48%) in cluster 2 and 1017 (20%) in cluster 3. Cluster 1 was mainly characterized by older age, higher serum chloride and acute kidney injury (AKI), but lower estimated glomerular filtration rate (eGFR), serum bicarbonate and hemoglobin. Cluster 2 was mainly characterized by higher eGFR, serum bicarbonate and hemoglobin, but lower comorbidity burden, serum potassium and AKI. Cluster 3 was mainly characterized by higher comorbidity burden, particularly diabetes and end-stage kidney disease, AKI, serum potassium, anion gap, but lower eGFR, serum sodium, chloride and bicarbonate. Hospital and 1-year mortality risk was significantly different among the three identified clusters, with highest mortality in cluster 3, followed by cluster 1 and then cluster 2. CONCLUSION In a heterogeneous cohort of hyperkalemia patients, three distinct clusters were identified using unsupervised ML. These three clusters had different clinical characteristics and outcomes.
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Affiliation(s)
- C Thongprayoon
- From the Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - A G Kattah
- From the Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - M A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - M T Keddis
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - P Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, 10120, Thailand
| | - S Vallabhajosyula
- Section of Interventional Cardiology, Division of Cardiovascular Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - V Nissaisorakarn
- Department of Internal Medicine, MetroWest Medical Center, Framingham, MA 01702, USA
| | - S B Erickson
- From the Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - J J Dillon
- From the Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - V D Garovic
- From the Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - W Cheungpasitporn
- From the Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Pan HC, Sun CY, Huang TTM, Huang CT, Tsao CH, Lai CH, Chen YM, Wu VC. Distinct Subtyping of Successful Weaning from Acute Kidney Injury Requiring Renal Replacement Therapy by Consensus Clustering in Critically Ill Patients. Biomedicines 2022; 10:biomedicines10071628. [PMID: 35884933 PMCID: PMC9313082 DOI: 10.3390/biomedicines10071628] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/26/2022] [Accepted: 07/04/2022] [Indexed: 02/07/2023] Open
Abstract
Background: Clinical decisions regarding the appropriate timing of weaning off renal replacement therapy (RRT) in critically ill patients are complex and multifactorial. The aim of the current study was to identify which critical patients with acute kidney injury (AKI) may be more likely to be successfully weaned off RRT using consensus cluster analysis. Methods: In this study, critically ill patients who received RRT at three multicenter referral hospitals at several timepoints from August 2016 to July 2018 were enrolled. An unsupervised consensus clustering algorithm was used to identify distinct phenotypes. The outcomes of interest were the ability to wean off RTT and 90-day mortality. Results: A total of 124 patients with AKI requiring RRT (AKI-RRT) were enrolled. The 90-day mortality rate was 30.7% (38/124), and 49.2% (61/124) of the patients were successfully weaned off RRT for over 90 days. The consensus clustering algorithm identified three clusters from a total of 45 features. The three clusters had distinct features and could be separated according to the combination of urinary neutrophil gelatinase-associated lipocalin to creatinine ratio (uNGAL/Cr), Sequential Organ Failure Assessment (SOFA) score, and estimated glomerular filtration rate at the time of weaning off RRT. uNGAL/Cr (hazard ratio [HR] 2.43, 95% confidence interval [CI]: 1.36–4.33) and clustering phenotype (cluster 1 vs. 3, HR 2.7, 95% CI: 1.11–6.57; cluster 2 vs. 3, HR 44.5, 95% CI: 11.92–166.39) could predict 90-day mortality or re-dialysis. Conclusions: Almost half of the critical patients with AKI-RRT could wean off dialysis for over 90 days. Urinary NGAL/Cr and distinct clustering phenotypes could predict 90-day mortality or re-dialysis.
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Affiliation(s)
- Heng-Chih Pan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei 100, Taiwan;
- Division of Nephrology, Department of Internal Medicine, Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan;
- School of Medicine, Chang Gung University College of Medicine, Taoyuan 33302, Taiwan
| | - Chiao-Yin Sun
- Division of Nephrology, Department of Internal Medicine, Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan;
| | - Thomas Tao-Min Huang
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei 100, Taiwan; (T.T.-M.H.); (Y.-M.C.)
| | - Chun-Te Huang
- Nephrology and Critical Care Medicine, Department of Internal Medicine and Critical Care Medicine, Taichung Veterans General Hospital, Taichung 407, Taiwan;
| | - Chun-Hao Tsao
- Department of Surgery, National Taiwan University Hospital, Taipei 100, Taiwan; (C.-H.T.); (C.-H.L.)
| | - Chien-Heng Lai
- Department of Surgery, National Taiwan University Hospital, Taipei 100, Taiwan; (C.-H.T.); (C.-H.L.)
| | - Yung-Ming Chen
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei 100, Taiwan; (T.T.-M.H.); (Y.-M.C.)
| | - Vin-Cent Wu
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei 100, Taiwan; (T.T.-M.H.); (Y.-M.C.)
- Correspondence: ; Tel.: +886-2-23562082
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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 N, Hong R, Zhou W, Zhong J, Kan M, Zheng Y, Zhou E, Sun W, Zhang L. The Association between Leisure-Time Physical Activity Intensity and Duration with the Risk of Mortality in Patients with Chronic Kidney Disease with or without Cardiovascular Diseases. Rev Cardiovasc Med 2022; 23:244. [PMID: 39076900 PMCID: PMC11266840 DOI: 10.31083/j.rcm2307244] [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: 03/29/2022] [Revised: 05/22/2022] [Accepted: 06/09/2022] [Indexed: 07/31/2024] Open
Abstract
Introduction For chronic kidney disease (CKD) patients with or without cardiovascular diseases, the associations between leisure-time physical activity intensity (LTPA) and daily exercise time with mortality risk remain unclear. Method This study enrolled 3279 CKD patients from National Health and Nutrition Examination Survey (NHANES) 2007-2014 survey. Patients were grouped into different groups according to LTPA intensity (none, moderate, vigorous) and duration (0 min, 0-30 min, 30-60 min, > 60 min). We selected the confounders based on their connections with the outcomes of interest or a change in effect estimate of more than 10%. Multivariable-adjusted Cox proportional hazards models were used to examine the associations between LTPA and mortality. The three-knot cubic spline (10, 50, and 90%) was employed to investigate the relationship between the dose of LTPA duration and all-cause death. Patients were divided into different groups according to cardiovascular diseases (CVD). Results A total of 564 all-cause death were recorded in this study. Multivariable Cox regression showed that moderate LTPA was associated with a reduced risk of mortality by 38% (hazard ratio (HR): 0.62, 95% confidence interval (CI): 0.44-0.88) in CKD patients, while vigorous LTPA did not have evident survival benefits (HR: 0.91, 95% CI: 0.46-2.64). Subgroups analysis demonstrated that those who engaged in moderate LTPA have a significantly lower risk of mortality (HR: 0.67, 95% CI: 0.47-0.95) in patients without CVD, while patients complicated with CVD did not benefit from the practice (HR: 0.61, 95% CI: 0.37-1.02). Physical exercise for more than 30 minutes was associated with a lower risk of mortality in general CKD patients (30-60 min: HR: 0.23, 95% CI: 0.09-0.58, > 60 min: HR: 0.23, 95% CI: 0.08-0.63) and those without CVD (30-60 min/d: HR: 0.32, 95% CI: 0.12-0.83, > 60 min/d: HR: 0.20, 95% CI: 0.06-0.71); however, this positive outcome was not seen in patients complicated with CVD (30-60 min/d: HR: 0.67, 95% CI: 0.11-4.04, > 60 min/d: HR: 1.14, 95% CI: 0.14-9.11). Conclusions Moderate LTPA for more than 30 minutes is associated with a reduced risk of mortality in general CKD patients and those without CVD. However, LTPA did not reduce the risk of mortality in CKD patients complicated with CVD.
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Affiliation(s)
- Ning Li
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
| | - Ruoyang Hong
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
| | - Weiguo Zhou
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
| | - Jingchen Zhong
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
| | - Mingyun Kan
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
| | - Yawei Zheng
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
| | - Enchao Zhou
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
| | - Wei Sun
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
| | - Lu Zhang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangsu, China
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Thongprayoon C, Mao SA, Jadlowiec CC, Mao MA, Leeaphorn N, Kaewput W, Vaitla P, Pattharanitima P, Tangpanithandee S, Krisanapan P, Qureshi F, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States. J Clin Med 2022; 11:jcm11123288. [PMID: 35743357 PMCID: PMC9224965 DOI: 10.3390/jcm11123288] [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] [Received: 04/28/2022] [Revised: 05/28/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
| | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA;
| | | | - Michael A. Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke’s Health System, Kansas City, MO 64108, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
- Correspondence: (W.K.); (P.P.); (W.C.)
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA;
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
- Correspondence: (W.K.); (P.P.); (W.C.)
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
| | - Pitchaphon Nissaisorakarn
- Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Matthew Cooper
- Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 20007, USA;
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA; (C.T.); (S.T.); (P.K.); (F.Q.)
- Correspondence: (W.K.); (P.P.); (W.C.)
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Distinct Phenotypes of Kidney Transplant Recipients in the United States with Limited Functional Status as Identified through Machine Learning Consensus Clustering. J Pers Med 2022; 12:jpm12060859. [PMID: 35743647 PMCID: PMC9225038 DOI: 10.3390/jpm12060859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 05/22/2022] [Accepted: 05/23/2022] [Indexed: 01/27/2023] Open
Abstract
Background: There have been concerns regarding increased perioperative mortality, length of hospital stay, and rates of graft loss in kidney transplant recipients with functional limitations. The application of machine learning consensus clustering approach may provide a novel understanding of unique phenotypes of functionally limited kidney transplant recipients with distinct outcomes in order to identify strategies to improve outcomes. Methods: Consensus cluster analysis was performed based on recipient-, donor-, and transplant-related characteristics in 3205 functionally limited kidney transplant recipients (Karnofsky Performance Scale (KPS) < 40% at transplant) in the OPTN/UNOS database from 2010 to 2019. Each cluster’s key characteristics were identified using the standardized mean difference. Posttransplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection were compared among the clusters Results: Consensus cluster analysis identified two distinct clusters that best represented the clinical characteristics of kidney transplant recipients with limited functional status prior to transplant. Cluster 1 patients were older in age and were more likely to receive deceased donor kidney transplant with a higher number of HLA mismatches. In contrast, cluster 2 patients were younger, had shorter dialysis duration, were more likely to be retransplants, and were more likely to receive living donor kidney transplants from HLA mismatched donors. As such, cluster 2 recipients had a higher PRA, less cold ischemia time, and lower proportion of machine-perfused kidneys. Despite having a low KPS, 5-year patient survival was 79.1 and 83.9% for clusters 1 and 2; 5-year death-censored graft survival was 86.9 and 91.9%. Cluster 1 had lower death-censored graft survival and patient survival but higher acute rejection, compared to cluster 2. Conclusion: Our study used an unsupervised machine learning approach to characterize kidney transplant recipients with limited functional status into two clinically distinct clusters with differing posttransplant outcomes.
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Thongprayoon C, Vaitla P, Jadlowiec CC, Leeaphorn N, Mao SA, Mao MA, Pattharanitima P, Bruminhent J, Khoury NJ, Garovic VD, Cooper M, Cheungpasitporn W. Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Black Kidney Transplant Recipients and Associated Outcomes. JAMA Surg 2022; 157:e221286. [PMID: 35507356 PMCID: PMC9069346 DOI: 10.1001/jamasurg.2022.1286] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Importance Among kidney transplant recipients, Black patients continue to have worse graft function and reduced patient and graft survival. Better understanding of different phenotypes and subgroups of Black kidney transplant recipients may help the transplant community to identify individualized strategies to improve outcomes among these vulnerable groups. Objective To cluster Black kidney transplant recipients in the US using an unsupervised machine learning approach. Design, Setting, and Participants This cohort study performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in Black kidney transplant recipients in the US from January 1, 2015, to December 31, 2019, in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database. Each cluster's key characteristics were identified using the standardized mean difference, and subsequently the posttransplant outcomes were compared among the clusters. Data were analyzed from June 9 to July 17, 2021. Exposure Machine learning consensus clustering approach. Main Outcomes and Measures Death-censored graft failure, patient death within 3 years after kidney transplant, and allograft rejection within 1 year after kidney transplant. Results Consensus cluster analysis was performed for 22 687 Black kidney transplant recipients (mean [SD] age, 51.4 [12.6] years; 13 635 men [60%]), and 4 distinct clusters that best represented their clinical characteristics were identified. Cluster 1 was characterized by highly sensitized recipients of deceased donor kidney retransplants; cluster 2, by recipients of living donor kidney transplants with no or short prior dialysis; cluster 3, by young recipients with hypertension and without diabetes who received young deceased donor transplants with low kidney donor profile index scores; and cluster 4, by older recipients with diabetes who received kidneys from older donors with high kidney donor profile index scores and extended criteria donors. Cluster 2 had the most favorable outcomes in terms of death-censored graft failure, patient death, and allograft rejection. Compared with cluster 2, all other clusters had a higher risk of death-censored graft failure and death. Higher risk for rejection was found in clusters 1 and 3, but not cluster 4. Conclusions and Relevance In this cohort study using an unsupervised machine learning approach, the identification of clinically distinct clusters among Black kidney transplant recipients underscores the need for individualized care strategies to improve outcomes among vulnerable patient groups.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Pradeep Vaitla
- Division of Nephrology, University of Mississippi Medical Center, Jackson
| | | | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine, Saint Luke's Health System
| | - Shennen A Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, Florida
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida
| | | | - Jackrapong Bruminhent
- Ramathibodi Excellence Center for Organ Transplantation, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Division of Infectious Diseases, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Nadeen J Khoury
- Department of Nephrology, Department of Medicine, Henry Ford Hospital, Detroit, Michigan
| | - Vesna D Garovic
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota
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Thongprayoon C, Mao MA, Kattah AG, Keddis MT, Pattharanitima P, Erickson SB, Dillon JJ, Garovic VD, Cheungpasitporn W. Subtyping hospitalized patients with hypokalemia by machine learning consensus clustering and associated mortality risks. Clin Kidney J 2022; 15:253-261. [PMID: 35145640 PMCID: PMC8825225 DOI: 10.1093/ckj/sfab190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Indexed: 12/18/2022] Open
Abstract
Background Hospitalized patients with hypokalemia are heterogeneous and cluster analysis, an unsupervised machine learning methodology, may discover more precise and specific homogeneous groups within this population of interest. Our study aimed to cluster patients with hypokalemia at hospital admission using an unsupervised machine learning approach and assess the mortality risk among these distinct clusters. Methods We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities and laboratory data among 4763 hospitalized adult patients with admission serum potassium ≤3.5 mEq/L. We calculated the standardized mean difference of each variable and used the cutoff of ±0.3 to identify each cluster's key features. We assessed the association of the hypokalemia cluster with hospital and 1-year mortality. Results Consensus cluster analysis identified three distinct clusters that best represented patients’ baseline characteristics. Cluster 1 had 1150 (32%) patients, cluster 2 had 1344 (28%) patients and cluster 3 had 1909 (40%) patients. Based on the standardized difference, patients in cluster 1 were younger, had less comorbidity burden but higher estimated glomerular filtration rate (eGFR) and higher hemoglobin; patients in cluster 2 were older, more likely to be admitted for cardiovascular disease and had higher serum sodium and chloride levels but lower eGFR, serum bicarbonate, strong ion difference (SID) and hemoglobin, while patients in cluster 3 were older, had a greater comorbidity burden, higher serum bicarbonate and SID but lower serum sodium, chloride and eGFR. Compared with cluster 1, cluster 2 had both higher hospital and 1-year mortality, whereas cluster 3 had higher 1-year mortality but comparable hospital mortality. Conclusion Our study demonstrated the use of consensus clustering analysis in the heterogeneous cohort of hospitalized hypokalemic patients to characterize their patterns of baseline clinical and laboratory data into three clinically distinct clusters with different mortality risks.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Andrea G Kattah
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mira T Keddis
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Phoenix, AZ, USA
| | | | - Stephen B Erickson
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - John J Dillon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Vesna D Garovic
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
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Jiang Y, Xia J, Che C, Wei Y. Data-driven classification of prediabetes using cardiometabolic biomarkers: Data from National Health and Nutrition Examination Survey 2007-2016. Front Endocrinol (Lausanne) 2022; 13:937942. [PMID: 36072936 PMCID: PMC9441552 DOI: 10.3389/fendo.2022.937942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Cluster analyses have proposed different prediabetes phenotypes using glycemic parameters, body fat distribution, liver fat content, and insulin sensitivity. We aimed at classifying the subjects with prediabetes using cluster analysis and exploring the associations between prediabetes clusters with hypertension and kidney function. METHODS Patients with prediabetes in the National Health and Nutrition Examination Survey (NHANES) underwent comprehensive phenotyping and physical and laboratory variable assessment. We identified six clusters using consensus clustering analysis based on the measurements representing the body fat, glycemic status, pancreatic islet function, blood lipids, and liver function. Differences in the characteristics and prevalence of hypertension, decreased estimated glomerular filtration rate (eGFR), and increased albumin-to-creatinine ratio (ACR) were compared between clusters. RESULTS A total of 4,385 subjects with prediabetes were classified into six clusters of distinctive patterns by manifesting higher or lower levels of certain metabolic parameters in each cluster. Subjects with prediabetes in cluster 1 had the lowest prevalence of hypertension, decreased eGFR, and increased ACR, whereas these were much higher in cluster 5 and cluster 6. Except for cluster 3, all the other clusters had significantly increased odds ratio (OR) of hypertension as compared with cluster 1. Compared with cluster 1, all the other clusters presented significantly increased ORs of decreased eGFR. There were also significantly higher ORs of increased ACR for cluster 5 (OR 1.95, 95% confidence interval [CI] 1.09-3.51) and cluster 6 (OR 2.02, 95%CI = 1.15-3.52) compared with cluster 1. CONCLUSION We stratified subjects with prediabetes into six subgroups with different characteristics. With further development and validation, such approaches might guide early intervention on the risk factors for the subjects with prediabetes who would benefit most.
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Affiliation(s)
- Yan Jiang
- Medical Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Jinying Xia
- Department of Endocrinology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Caiyan Che
- Medical Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Yongning Wei
- Department of Hepatic Neoplasms, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
- *Correspondence: Yongning Wei,
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Pattharanitima P, Thongprayoon C, Petnak T, Srivali N, Gembillo G, Kaewput W, Chesdachai S, Vallabhajosyula S, O’Corragain OA, Mao MA, Garovic VD, Qureshi F, Dillon JJ, Cheungpasitporn W. Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units. J Pers Med 2021; 11:jpm11111132. [PMID: 34834484 PMCID: PMC8623582 DOI: 10.3390/jpm11111132] [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: 09/22/2021] [Revised: 10/28/2021] [Accepted: 10/30/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. METHODS We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. RESULTS We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. CONCLUSIONS Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.
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Affiliation(s)
- Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12121, Thailand
- Correspondence: (P.P.); (C.T.); (W.C.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (V.D.G.); (F.Q.); (J.J.D.)
- Correspondence: (P.P.); (C.T.); (W.C.)
| | - Tananchai Petnak
- Division of Pulmonary and Pulmonary Critical Care Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand;
| | - Narat Srivali
- Division of Pulmonary Medicine, St. Agnes Hosipital, Baltimore, MD 21229, USA;
| | - Guido Gembillo
- Unit of Nephrology and Dialysis, Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Supavit Chesdachai
- Division of Infectious Disease, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Saraschandra Vallabhajosyula
- Section of Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA;
| | - Oisin A. O’Corragain
- Department of Thoracic Medicine and Surgery, Temple University Hospital, Philadelphia, PA 19140, USA;
| | - Michael A. Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Vesna D. Garovic
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (V.D.G.); (F.Q.); (J.J.D.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (V.D.G.); (F.Q.); (J.J.D.)
| | - John J. Dillon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (V.D.G.); (F.Q.); (J.J.D.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (V.D.G.); (F.Q.); (J.J.D.)
- Correspondence: (P.P.); (C.T.); (W.C.)
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Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival. J Nephrol 2021; 35:921-929. [PMID: 34623631 DOI: 10.1007/s40620-021-01163-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 09/17/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND The objective of this study was to characterize hypernatremia patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters. METHODS We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 922 hospitalized adult patients with admission serum sodium of > 145 mEq/L. We calculated the standardized difference of each variable to identify each cluster's key features. We assessed the association of each hypernatremia cluster with hospital and 1-year mortality. RESULTS There were three distinct clusters of patients with hypernatremia on admission: 318 (34%) patients in cluster 1, 339 (37%) patients in cluster 2, and 265 (29%) patients in cluster 3. Cluster 1 consisted of more critically ill patients with more severe hypernatremia and hypokalemic hyperchloremic metabolic acidosis. Cluster 2 consisted of older patients with more comorbidity burden, body mass index, and metabolic alkalosis. Cluster 3 consisted of younger patients with less comorbidity burden, higher baseline eGFR, hemoglobin, and serum albumin. Compared to cluster 3, odds ratios for hospital mortality were 15.74 (95% CI 3.75-66.18) for cluster 1, and 6.51 (95% CI 1.48-28.59) for cluster 2, whereas hazard ratios for 1-year mortality were 6.25 (95% CI 3.69-11.46) for cluster 1 and 4.66 (95% CI 2.73-8.59) for cluster 2. CONCLUSION Our cluster analysis identified three clinically distinct phenotypes with differing mortality risk in patients hospitalized with hypernatremia.
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Thongprayoon C, Dumancas CY, Nissaisorakarn V, Keddis MT, Kattah AG, Pattharanitima P, Petnak T, Vallabhajosyula S, Garovic VD, Mao MA, Dillon JJ, Erickson SB, Cheungpasitporn W. Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements. J Clin Med 2021; 10:4441. [PMID: 34640457 PMCID: PMC8509302 DOI: 10.3390/jcm10194441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/18/2021] [Accepted: 09/25/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. METHODS We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster's key features. We assessed the association of the clusters with mortality. RESULTS In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. CONCLUSION Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Carissa Y. Dumancas
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Voravech Nissaisorakarn
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA;
| | - Mira T. Keddis
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Phoenix, AZ 85054, USA;
| | - Andrea G. Kattah
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Tananchai Petnak
- Division of Pulmonary and Pulmonary Critical Care Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand;
| | - Saraschandra Vallabhajosyula
- Section of Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA;
| | - Vesna D. Garovic
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Michael A. Mao
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - John J. Dillon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Stephen B. Erickson
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
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Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering. Med Sci (Basel) 2021; 9:medsci9040060. [PMID: 34698185 PMCID: PMC8544570 DOI: 10.3390/medsci9040060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 09/19/2021] [Accepted: 09/21/2021] [Indexed: 12/22/2022] Open
Abstract
Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster’s key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Results: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Conclusion: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.
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Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia. Diseases 2021; 9:diseases9030054. [PMID: 34449583 PMCID: PMC8395840 DOI: 10.3390/diseases9030054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 01/12/2023] Open
Abstract
Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster’s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.
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Is It Time for Machine Learning Algorithms to Predict the Risk of Kidney Failure in Patients with Chronic Kidney Disease? J Clin Med 2021; 10:jcm10051121. [PMID: 33800205 PMCID: PMC7962455 DOI: 10.3390/jcm10051121] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 03/05/2021] [Indexed: 12/21/2022] Open
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