Yunlong Qin, Jin Zhao, Yan Xing, Zixian Yu, Panpan Liu, Yuwei Wang, Anjing Wang, Yueqing Hui, Wei Zhao, Mei Han, Meng Liu, Xiaoxuan Ning, Shiren Sun
{"title":"Advancing Precision Medicine for hypertensive nephropathy: a novel prognostic model incorporating pathological indicators.","authors":"Yunlong Qin, Jin Zhao, Yan Xing, Zixian Yu, Panpan Liu, Yuwei Wang, Anjing Wang, Yueqing Hui, Wei Zhao, Mei Han, Meng Liu, Xiaoxuan Ning, Shiren Sun","doi":"10.1159/000545524","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>This study aims to assess the long-term renal prognosis of patients with hypertensive nephropathy (HN) diagnosed through renal biopsy, utilizing the random survival forest (RSF) algorithm.</p><p><strong>Methods: </strong>From December 2010 to December 2022, HN patients diagnosed by renal biopsy in Xijing Hospital were enrolled and randomly divided into training set and testing set at a ratio of 7∶3. The study's composite endpoint was defined as a ≥ 50% decline in estimated glomerular filtration rate (eGFR), ESRD, or death. RSF and Cox regression were used to establish a renal prognosis prediction model based on the factors screened by the RSF algorithm. The Concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate discrimination, calibration, and risk classification, respectively.</p><p><strong>Results: </strong>A total of 225 patients were included in this study, with 72 (32.0%) patients experiencing combined events after a median follow-up of 29.9 (16.6, 52.1) months. Six eligible variables (overall chronicity grade of renal pathology, eGFR, high-density lipoprotein cholesterol, hematocrit, monocyte, and stroke volume) were selected from clinical data and introduced into the RSF model. The RSF model had a higher C-index in both the training set [0.904 (95%CI 0.842 - 0.938) vs 0.831 (95%CI 0.768 - 0.894), P < 0.001] and the testing set [0.893 (95%CI 0.770 - 0.944) vs 0.841 (95%CI 0.751 - 0.931), P = 0.021] compared to the Cox model. NRI and IDI indicated that the RSF model outperformed the Cox model regarding risk classification and discrimination.</p><p><strong>Conclusion: </strong>In this study, the RSF algorithm was employed to identify the risk factors affecting the prognosis of HN patients, and a clinical prognostic RSF model was constructed to predict the adverse outcomes of HN patients based on renal pathology. Compared to the traditional Cox regression model, the RSF model offers superior performance and can provide valuable new insights for clinical diagnosis and treatment strategies.</p>","PeriodicalId":17813,"journal":{"name":"Kidney & blood pressure research","volume":" ","pages":"1-21"},"PeriodicalIF":2.3000,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kidney & blood pressure research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000545524","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: This study aims to assess the long-term renal prognosis of patients with hypertensive nephropathy (HN) diagnosed through renal biopsy, utilizing the random survival forest (RSF) algorithm.
Methods: From December 2010 to December 2022, HN patients diagnosed by renal biopsy in Xijing Hospital were enrolled and randomly divided into training set and testing set at a ratio of 7∶3. The study's composite endpoint was defined as a ≥ 50% decline in estimated glomerular filtration rate (eGFR), ESRD, or death. RSF and Cox regression were used to establish a renal prognosis prediction model based on the factors screened by the RSF algorithm. The Concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate discrimination, calibration, and risk classification, respectively.
Results: A total of 225 patients were included in this study, with 72 (32.0%) patients experiencing combined events after a median follow-up of 29.9 (16.6, 52.1) months. Six eligible variables (overall chronicity grade of renal pathology, eGFR, high-density lipoprotein cholesterol, hematocrit, monocyte, and stroke volume) were selected from clinical data and introduced into the RSF model. The RSF model had a higher C-index in both the training set [0.904 (95%CI 0.842 - 0.938) vs 0.831 (95%CI 0.768 - 0.894), P < 0.001] and the testing set [0.893 (95%CI 0.770 - 0.944) vs 0.841 (95%CI 0.751 - 0.931), P = 0.021] compared to the Cox model. NRI and IDI indicated that the RSF model outperformed the Cox model regarding risk classification and discrimination.
Conclusion: In this study, the RSF algorithm was employed to identify the risk factors affecting the prognosis of HN patients, and a clinical prognostic RSF model was constructed to predict the adverse outcomes of HN patients based on renal pathology. Compared to the traditional Cox regression model, the RSF model offers superior performance and can provide valuable new insights for clinical diagnosis and treatment strategies.
期刊介绍：
This journal comprises both clinical and basic studies at the interface of nephrology, hypertension and cardiovascular research. The topics to be covered include the structural organization and biochemistry of the normal and diseased kidney, the molecular biology of transporters, the physiology and pathophysiology of glomerular filtration and tubular transport, endothelial and vascular smooth muscle cell function and blood pressure control, as well as water, electrolyte and mineral metabolism. Also discussed are the (patho)physiology and (patho) biochemistry of renal hormones, the molecular biology, genetics and clinical course of renal disease and hypertension, the renal elimination, action and clinical use of drugs, as well as dialysis and transplantation. Featuring peer-reviewed original papers, editorials translating basic science into patient-oriented research and disease, in depth reviews, and regular special topic sections, ''Kidney & Blood Pressure Research'' is an important source of information for researchers in nephrology and cardiovascular medicine.
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