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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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Hui M, Ma J, Yang H, Gao B, Wang F, Wang J, Lv J, Zhang L, Yang L, Zhao M. ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study. J Clin Med 2023; 12:jcm12041504. [PMID: 36836039 PMCID: PMC9965616 DOI: 10.3390/jcm12041504] [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: 12/28/2022] [Revised: 01/29/2023] [Accepted: 02/12/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND AND OBJECTIVES In light of the growing burden of chronic kidney disease (CKD), it is of particular importance to create disease prediction models that can assist healthcare providers in identifying cases of CKD individual risk and integrate risk-based care for disease progress management. The objective of this study was to develop and validate a new pragmatic end-stage kidney disease (ESKD) risk prediction utilizing the Cox proportional hazards model (Cox) and machine learning (ML). DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS The Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE), a multicenter CKD cohort in China, was employed as the model's training and testing datasets, with a split ratio of 7:3. A cohort from Peking University First Hospital (PKUFH cohort) served as the external validation dataset. The participants' laboratory tests in those cohorts were conducted at PKUFH. We included individuals with CKD stages 1~4 at baseline. The incidence of kidney replacement therapy (KRT) was defined as the outcome. We constructed the Peking University-CKD (PKU-CKD) risk prediction model employing the Cox and ML methods, which include extreme gradient boosting (XGBoost) and survival support vector machine (SSVM). These models discriminate metrics by applying Harrell's concordance index (Harrell's C-index) and Uno's concordance (Uno's C). The calibration performance was measured by the Brier score and plots. RESULTS Of the 3216 C-STRIDE and 342 PKUFH participants, 411 (12.8%) and 25 (7.3%) experienced KRT with mean follow-up periods of 4.45 and 3.37 years, respectively. The features included in the PKU-CKD model were age, gender, estimated glomerular filtration rate (eGFR), urinary albumin-creatinine ratio (UACR), albumin, hemoglobin, medical history of type 2 diabetes mellitus (T2DM), and hypertension. In the test dataset, the values of the Cox model for Harrell's C-index, Uno's C-index, and Brier score were 0.834, 0.833, and 0.065, respectively. The XGBoost algorithm values for these metrics were 0.826, 0.825, and 0.066, respectively. The SSVM model yielded values of 0.748, 0.747, and 0.070, respectively, for the above parameters. The comparative analysis revealed no significant difference between XGBoost and Cox, in terms of Harrell's C, Uno's C, and the Brier score (p = 0.186, 0.213, and 0.41, respectively) in the test dataset. The SSVM model was significantly inferior to the previous two models (p < 0.001), in terms of discrimination and calibration. The validation dataset showed that XGBoost was superior to Cox, regarding Harrell's C, Uno's C, and the Brier score (p = 0.003, 0.027, and 0.032, respectively), while Cox and SSVM were almost identical concerning these three parameters (p = 0.102, 0.092, and 0.048, respectively). CONCLUSIONS We developed and validated a new ESKD risk prediction model for patients with CKD, employing commonly measured indicators in clinical practice, and its overall performance was satisfactory. The conventional Cox regression and certain ML models exhibited equal accuracy in predicting the course of CKD.
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Affiliation(s)
- Miao Hui
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
| | - Jun Ma
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
| | - Hongyu Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
| | - Bixia Gao
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
| | - Fang Wang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
| | - Jinwei Wang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- Correspondence: (J.W.); (J.L.)
| | - Jicheng Lv
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- Correspondence: (J.W.); (J.L.)
| | - Luxia Zhang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- National Institute of Health Data Science at Peking University, Beijing 100191, China
| | - Li Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
| | - Minghui Zhao
- Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China
- Institute of Nephrology, Peking University, Beijing 100034, China
- Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
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