IgA肾病患者个性化风险预测模型的开发和验证:一项全国性多中心队列研究

IF 2.6 4区 医学 Q2 UROLOGY & NEPHROLOGY
Keita Hirano, Tatsuyoshi Ikenoue, Tomohisa Seki, Sho Komukai, Hirosuke Nakata, Takashi Yasuda, Yoshinari Yasuda, Keiichi Matsuzaki, Tetsuya Kawamura, Takashi Yokoo, Shoichi Maruyama, Hitoshi Suzuki, Yusuke Suzuki, Shingo Fukuma
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引用次数: 0

摘要

背景:有效预测免疫球蛋白A肾病(IgAN)进展对早期干预和治疗至关重要。我们的目标是开发和验证不同的IgAN预测模型用于临床和研究应用。方法:我们分析了来自日本全国IgAN回顾性队列研究(n = 1174)的数据,收集时间超过10年。这些模型的开发和测试使用了来自初级保健的普通医生、三级保健医院的专家和学术研究机构的研究人员的数据。三个量身定制的预测模型(初级保健、三级保健和研究所模型)被创建以满足不同临床环境的独特需求。主要终点为复合肾事件,定义为血清肌酐水平升高1.5倍或进展为肾衰竭。使用C-statistics评估预测性能。结果:在衍生队列中,初级保健模型包括肾小球滤过率2、蛋白尿≥0.5 g/天和未使用皮质类固醇等预测因子,c统计量为0.796(95%可信区间[CI] 0.686-0.895)。三级保健模型的c统计量为0.807 (95% CI 0.713-0.886),使用肾小球数量和组织学严重程度等预测因子。研究机构模型包含38个变量,其c统计量为0.802 (95% CI 0.686-0.906)。结论:初级和三级医疗机构的预测模型为预测IgAN患者的肾脏预后提供了有效的工具,并且与研究中使用的更复杂的基于机器学习的模型具有竞争力。这些模型可以帮助指导各种医疗保健环境中的临床决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of personalized risk prediction models for patients with IgA nephropathy: a nationwide multicenter cohort study.

Background: Effective prediction of immunoglobulin A nephropathy (IgAN) progression is crucial for early intervention and management. We aimed to develop and validate distinct IgAN prediction models for clinical and research applications.

Methods: We analyzed data from the Japanese Nationwide Retrospective Cohort Study in IgAN (n = 1174) gathered over 10 years. The models were developed and tested using data from general physicians in primary care, specialists in tertiary care hospitals, and researchers at academic research institutes. Three tailored prediction models (Primary Care, Tertiary Care, and Research Institute Models) were created to address the unique needs of different clinical environments. The primary outcome was a composite renal event defined as a 1.5-fold increase in serum creatinine level or progression to kidney failure. The predictive performance was assessed using C-statistics.

Results: In the derivation cohort, the primary care model included predictors such as estimated glomerular filtration rate < 45 mL/min/1.73 m2, proteinuria ≥ 0.5 g/day, and non-use of corticosteroids, achieving a C-statistic of 0.796 (95% confidence interval [CI] 0.686-0.895). The tertiary care model showed a C-statistic of 0.807 (95% CI 0.713-0.886), using predictors such as glomerular number and histological severity. The research institute model, incorporating 38 variables, demonstrated a C-statistic of 0.802 (95% CI 0.686-0.906).

Conclusions: The prediction models for primary and tertiary care settings provided effective tools for forecasting renal outcomes in IgAN patients and are competitive with more complex machine learning-based models used in research. These models can help guide clinical decisions in various healthcare settings.

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来源期刊
Journal of Nephrology
Journal of Nephrology 医学-泌尿学与肾脏学
CiteScore
5.60
自引率
5.90%
发文量
289
审稿时长
3-8 weeks
期刊介绍: Journal of Nephrology is a bimonthly journal that considers publication of peer reviewed original manuscripts dealing with both clinical and laboratory investigations of relevance to the broad fields of Nephrology, Dialysis and Transplantation. It is the Official Journal of the Italian Society of Nephrology (SIN).
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