Qiuyue Zhang, Qi Zhang, Zhi-juan Duan, Pu Chen, Jing-jing Chen, Ming-xv Li, Jing-jie Zhang, Yan-hong Huo, Wu-xing Zhang, Chen Yang, Yu Zhang, Xiangmei Chen, G. Cai
{"title":"国际 IgA 肾病预测工具在老年患者中的外部验证","authors":"Qiuyue Zhang, Qi Zhang, Zhi-juan Duan, Pu Chen, Jing-jing Chen, Ming-xv Li, Jing-jie Zhang, Yan-hong Huo, Wu-xing Zhang, Chen Yang, Yu Zhang, Xiangmei Chen, G. Cai","doi":"10.2147/cia.s455115","DOIUrl":null,"url":null,"abstract":"Purpose: The International IgA Nephropathy Prediction Tool (IIgAN-PT) can predict the risk of End-stage renal disease (ESRD) or estimated glomerular filtration rate (eGFR) decline ≥ 50% for adult IgAN patients. Considering the differential progression between older adult and adult patients, this study aims to externally validate its performance in the older adult cohort. Patients and Methods: We analyzed 165 IgAN patients aged 60 and above from six medical centers, categorizing them by their predicted risk. The primary outcome was a ≥50% reduction in estimated glomerular filtration rate (eGFR) or kidney failure. Evaluation of both models involved concordance statistics (C-statistics), time-dependent receiver operating characteristic (ROC) curves, Kaplan– Meier survival curves, and calibration plots. Comparative reclassification was conducted using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: The study included 165 Chinese patients (median age 64, 60% male), with a median follow-up of 5.1 years. Of these, 21% reached the primary outcome. Both models with or without race demonstrated good discrimination (C-statistics 0.788 and 0.790, respectively). Survival curves for risk groups were well-separated. The full model without race more accurately predicted 5-year risks, whereas the full model with race tended to overestimate risks after 3 years. No significant reclassification improvement was noted in the full model without race (NRI 0.09, 95% CI: −0.27 to 0.34; IDI 0.003, 95% CI: −0.009 to 0.019). Conclusion: : Both models exhibited excellent discrimination among older adult IgAN patients. The full model without race demonstrated superior calibration in predicting the 5-year risk.","PeriodicalId":48841,"journal":{"name":"Clinical Interventions in Aging","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"External Validation of the International IgA Nephropathy Prediction Tool in Older Adult Patients\",\"authors\":\"Qiuyue Zhang, Qi Zhang, Zhi-juan Duan, Pu Chen, Jing-jing Chen, Ming-xv Li, Jing-jie Zhang, Yan-hong Huo, Wu-xing Zhang, Chen Yang, Yu Zhang, Xiangmei Chen, G. Cai\",\"doi\":\"10.2147/cia.s455115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose: The International IgA Nephropathy Prediction Tool (IIgAN-PT) can predict the risk of End-stage renal disease (ESRD) or estimated glomerular filtration rate (eGFR) decline ≥ 50% for adult IgAN patients. Considering the differential progression between older adult and adult patients, this study aims to externally validate its performance in the older adult cohort. Patients and Methods: We analyzed 165 IgAN patients aged 60 and above from six medical centers, categorizing them by their predicted risk. The primary outcome was a ≥50% reduction in estimated glomerular filtration rate (eGFR) or kidney failure. Evaluation of both models involved concordance statistics (C-statistics), time-dependent receiver operating characteristic (ROC) curves, Kaplan– Meier survival curves, and calibration plots. Comparative reclassification was conducted using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: The study included 165 Chinese patients (median age 64, 60% male), with a median follow-up of 5.1 years. Of these, 21% reached the primary outcome. Both models with or without race demonstrated good discrimination (C-statistics 0.788 and 0.790, respectively). Survival curves for risk groups were well-separated. The full model without race more accurately predicted 5-year risks, whereas the full model with race tended to overestimate risks after 3 years. No significant reclassification improvement was noted in the full model without race (NRI 0.09, 95% CI: −0.27 to 0.34; IDI 0.003, 95% CI: −0.009 to 0.019). Conclusion: : Both models exhibited excellent discrimination among older adult IgAN patients. The full model without race demonstrated superior calibration in predicting the 5-year risk.\",\"PeriodicalId\":48841,\"journal\":{\"name\":\"Clinical Interventions in Aging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Interventions in Aging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/cia.s455115\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GERIATRICS & GERONTOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Interventions in Aging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/cia.s455115","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
External Validation of the International IgA Nephropathy Prediction Tool in Older Adult Patients
Purpose: The International IgA Nephropathy Prediction Tool (IIgAN-PT) can predict the risk of End-stage renal disease (ESRD) or estimated glomerular filtration rate (eGFR) decline ≥ 50% for adult IgAN patients. Considering the differential progression between older adult and adult patients, this study aims to externally validate its performance in the older adult cohort. Patients and Methods: We analyzed 165 IgAN patients aged 60 and above from six medical centers, categorizing them by their predicted risk. The primary outcome was a ≥50% reduction in estimated glomerular filtration rate (eGFR) or kidney failure. Evaluation of both models involved concordance statistics (C-statistics), time-dependent receiver operating characteristic (ROC) curves, Kaplan– Meier survival curves, and calibration plots. Comparative reclassification was conducted using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: The study included 165 Chinese patients (median age 64, 60% male), with a median follow-up of 5.1 years. Of these, 21% reached the primary outcome. Both models with or without race demonstrated good discrimination (C-statistics 0.788 and 0.790, respectively). Survival curves for risk groups were well-separated. The full model without race more accurately predicted 5-year risks, whereas the full model with race tended to overestimate risks after 3 years. No significant reclassification improvement was noted in the full model without race (NRI 0.09, 95% CI: −0.27 to 0.34; IDI 0.003, 95% CI: −0.009 to 0.019). Conclusion: : Both models exhibited excellent discrimination among older adult IgAN patients. The full model without race demonstrated superior calibration in predicting the 5-year risk.
期刊介绍:
Clinical Interventions in Aging, is an online, peer reviewed, open access journal focusing on concise rapid reporting of original research and reviews in aging. Special attention will be given to papers reporting on actual or potential clinical applications leading to improved prevention or treatment of disease or a greater understanding of pathological processes that result from maladaptive changes in the body associated with aging. This journal is directed at a wide array of scientists, engineers, pharmacists, pharmacologists and clinical specialists wishing to maintain an up to date knowledge of this exciting and emerging field.