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Ramírez Medina CR, Ali I, Baricevic-Jones I, Saleem MA, Whetton AD, Kalra PA, Geifman N. Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort. Clin Proteomics 2024; 21:34. [PMID: 38762513 PMCID: PMC11102163 DOI: 10.1186/s12014-024-09486-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/25/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction. METHODS Using the Salford Kidney Study biobank, a UK-based prospective cohort of over 3000 non-dialysis CKD patients, 433 patients met our inclusion criteria: a minimum of four eGFR measurements over a two-year period and a linear eGFR trajectory. Plasma samples were obtained and analysed for novel proteomic signals using SWATH-Mass-Spectrometry. The 4-variable UK-calibrated KFRE was calculated for each patient based on their baseline clinical characteristics. Boruta machine learning algorithm was used for the selection of proteins most contributing to differentiation between patient groups. Logistic regression was employed for estimation of ESRD prediction by (1) proteomic features; (2) KFRE; and (3) proteomic features alongside KFRE. RESULTS SWATH maps with 943 quantified proteins were generated and investigated in tandem with available clinical data to identify potential progression biomarkers. We identified a set of proteins (SPTA1, MYL6 and C6) that, when used alongside the 4-variable UK-KFRE, improved the prediction of 5-year risk of ESRD (AUC = 0.75 vs AUC = 0.70). Functional enrichment analysis revealed Rho GTPases and regulation of the actin cytoskeleton pathways to be statistically significant, inferring their role in kidney function and the pathogenesis of renal disease. CONCLUSIONS Proteins SPTA1, MYL6 and C6, when used alongside the 4-variable UK-KFRE achieve an improved performance when predicting a 5-year risk of ESRD. Specific pathways implicated in the pathogenesis of podocyte dysfunction were also identified, which could serve as potential therapeutic targets. The findings of our study carry implications for comprehending the involvement of the Rho family GTPases in the pathophysiology of kidney disease, advancing our understanding of the proteomic factors influencing susceptibility to renal damage.
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Affiliation(s)
- Carlos Raúl Ramírez Medina
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
| | - Ibrahim Ali
- Salford Royal Hospital, Northern Care Alliance Foundation NHS Trust, Salford, UK
- Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK
| | - Ivona Baricevic-Jones
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Salford Royal Hospital, Northern Care Alliance Foundation NHS Trust, Salford, UK
| | - Moin A Saleem
- Bristol Renal and Children's Renal Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anthony D Whetton
- Veterinary Health Innovation Engine (vHive), Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Philip A Kalra
- Salford Royal Hospital, Northern Care Alliance Foundation NHS Trust, Salford, UK
| | - Nophar Geifman
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
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Ooi YG, Sarvanandan T, Hee NKY, Lim QH, Paramasivam SS, Ratnasingam J, Vethakkan SR, Lim SK, Lim LL. Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus. Diabetes Metab J 2024; 48:196-207. [PMID: 38273788 PMCID: PMC10995482 DOI: 10.4093/dmj.2023.0244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/25/2023] [Indexed: 01/27/2024] Open
Abstract
People with type 2 diabetes mellitus have increased risk of chronic kidney disease and atherosclerotic cardiovascular disease. Improved care delivery and implementation of guideline-directed medical therapy have contributed to the declining incidence of atherosclerotic cardiovascular disease in high-income countries. By contrast, the global incidence of chronic kidney disease and associated mortality is either plateaued or increased, leading to escalating direct and indirect medical costs. Given limited resources, better risk stratification approaches to identify people at risk of rapid progression to end-stage kidney disease can reduce therapeutic inertia, facilitate timely interventions and identify the need for early nephrologist referral. Among people with chronic kidney disease G3a and beyond, the kidney failure risk equations (KFRE) have been externally validated and outperformed other risk prediction models. The KFRE can also guide the timing of preparation for kidney replacement therapy with improved healthcare resources planning and may prevent multiple complications and premature mortality among people with chronic kidney disease with and without type 2 diabetes mellitus. The present review summarizes the evidence of KFRE to date and call for future research to validate and evaluate its impact on cardiovascular and mortality outcomes, as well as healthcare resource utilization in multiethnic populations and different healthcare settings.
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Affiliation(s)
- Ying-Guat Ooi
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Tharsini Sarvanandan
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Nicholas Ken Yoong Hee
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Quan-Hziung Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Jeyakantha Ratnasingam
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Shireene R. Vethakkan
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Soo-Kun Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Asia Diabetes Foundation, Hong Kong SAR, China
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Patel DM, Churilla BM, Thiessen-Philbrook H, Sang Y, Grams ME, Parikh CR, Crews DC. Implementation of the Kidney Failure Risk Equation in a United States Nephrology Clinic. Kidney Int Rep 2023; 8:2665-2676. [PMID: 38106577 PMCID: PMC10719573 DOI: 10.1016/j.ekir.2023.09.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 12/19/2023] Open
Abstract
Introduction The kidney failure risk equation (KFRE) estimates a person's risk of kidney failure and has great potential utility in clinical care. Methods We used mixed methods to explore implementation of the KFRE in nephrology clinics. Results KFRE scores were integrated into the electronic health record at Johns Hopkins Medicine and were displayed to nephrology providers. Documentation of KFRE scores increased over time, reaching 25% of eligible outpatient nephrology clinic notes at month 11. Three providers documented KFRE scores in >75% of notes, whereas 25 documented scores in <10% of notes. Surveys and focus groups of nephrology providers were conducted to probe provider views on the KFRE. Survey respondents (n = 25) reported variability in use of KFRE for decisions such as maintaining nephrology care, referring for transplant evaluation, or providing dialysis modality education. Provider perspectives on the use of KFRE, assessed in 2 focus groups of 4 providers each, included 3 common themes as follows: (i) KFRE scores may be most impactful in the care of specific subsets of people with chronic kidney disease (CKD); (ii) there is uncertainty about KFRE risk-based thresholds to guide clinical care; and (iii) education of patients, nephrology providers, and non-nephrology providers on appropriate interpretations of KFRE scores may help maximize their utility. Conclusion Implementation of the KFRE was limited by non-uniform provider adoption of its use, and limited knowledge about utilization of the KFRE in clinical decisions.
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Affiliation(s)
- Dipal M. Patel
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Bryce M. Churilla
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Heather Thiessen-Philbrook
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Morgan E. Grams
- Division of Precision Medicine, Department of Medicine, New York University, New York, New York, USA
| | - Chirag R. Parikh
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Deidra C. Crews
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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Wang SH, Wu K, Chu T, Fernandes SL, Zhou Q, Zhang YD, Sun J. SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot recognition. WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2021; 2021:1-17. [PMID: 35573891 PMCID: PMC7612722 DOI: 10.1155/2021/5792975] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
AIM This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF). METHODS Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model. RESULTS The results on ten runs of 10-fold cross-validation show that our SOSPCNN model yields a sensitivity of 92.25±2.19, a specificity of 92.75±2.49, a precision of 92.79±2.29, an accuracy of 92.50±1.18, an F1 score of 92.48±1.17, an MCC of 85.06±2.38, an FMI of 92.50±1.17, and an AUC of 0.9587. CONCLUSION The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.
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Affiliation(s)
- Shui-Hua Wang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
| | - Kaihong Wu
- The Affiliated Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Tianshu Chu
- Nanjing Yirongda Institute of Intelligent Medicine and Additive Manufacturing, Nanjing, China
| | - Steven L. Fernandes
- Department of Computer Science, Design & Journalism, Creighton University, Omaha, Nebraska, USA
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
- Nanjing Yirongda Institute of Intelligent Medicine and Additive Manufacturing, Nanjing, China
- Co-Correspondence should be addressed to Yu-Dong Zhang & Jian Sun. ;
| | - Jian Sun
- The Affiliated Children's Hospital of Nanjing Medical University, Nanjing, China
- Co-Correspondence should be addressed to Yu-Dong Zhang & Jian Sun. ;
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