PMID- 36836039 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20230301 IS - 2077-0383 (Print) IS - 2077-0383 (Electronic) IS - 2077-0383 (Linking) VI - 12 IP - 4 DP - 2023 Feb 14 TI - ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study. LID - 10.3390/jcm12041504 [doi] LID - 1504 AB - 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. FAU - Hui, Miao AU - Hui M AUID- ORCID: 0000-0002-5467-6378 AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. FAU - Ma, Jun AU - Ma J AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. FAU - Yang, Hongyu AU - Yang H AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. FAU - Gao, Bixia AU - Gao B AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. FAU - Wang, Fang AU - Wang F AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. FAU - Wang, Jinwei AU - Wang J AUID- ORCID: 0000-0003-0128-8408 AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. FAU - Lv, Jicheng AU - Lv J AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. FAU - Zhang, Luxia AU - Zhang L AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. AD - National Institute of Health Data Science at Peking University, Beijing 100191, China. FAU - Yang, Li AU - Yang L AUID- ORCID: 0000-0002-2528-5087 AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. FAU - Zhao, Minghui AU - Zhao M AD - Renal Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China. AD - Institute of Nephrology, Peking University, Beijing 100034, China. AD - Key Laboratory of Renal Disease, National Health Commission of China, Beijing 100034, China. AD - Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China. AD - Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China. LA - eng PT - Journal Article DEP - 20230214 PL - Switzerland TA - J Clin Med JT - Journal of clinical medicine JID - 101606588 PMC - PMC9965616 OTO - NOTNLM OT - chronic kidney disease OT - machine learning OT - prediction model OT - progression COIS- The authors declare no conflict of interest. EDAT- 2023/02/26 06:00 MHDA- 2023/02/26 06:01 PMCR- 2023/02/14 CRDT- 2023/02/25 03:05 PHST- 2022/12/28 00:00 [received] PHST- 2023/01/29 00:00 [revised] PHST- 2023/02/12 00:00 [accepted] PHST- 2023/02/25 03:05 [entrez] PHST- 2023/02/26 06:00 [pubmed] PHST- 2023/02/26 06:01 [medline] PHST- 2023/02/14 00:00 [pmc-release] AID - jcm12041504 [pii] AID - jcm-12-01504 [pii] AID - 10.3390/jcm12041504 [doi] PST - epublish SO - J Clin Med. 2023 Feb 14;12(4):1504. doi: 10.3390/jcm12041504.