Evaluating a New International Risk-Prediction Tool in IgA Nephropathy
Evaluating a New International Risk-Prediction Tool in IgA Nephropathy
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DOI：
10.1001/jamainternmed.2019.0600
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发表时间：
2019-07-01
影响因子：
39
通讯作者：
Trimarchi, H.
中科院分区：
文献类型：
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作者：
Barbour, Sean J.;Coppo, Rosanna;Trimarchi, H.
尽管IgA肾病（IgAN）是世界上最常见的肾小球肾炎，但目前还没有有效的工具来预测疾病进展。这限制了患者特异性风险分层和治疗决策、临床试验招募和生物标志物验证。目的建立并外部验证IgAN疾病进展的预测模型，该模型可用于世界各地多民族的肾活检。设计、设置和参与者我们推导并外部验证了一个使用临床实践中容易获得的临床和组织学危险因素的预测模型。来自欧洲、北美、中国和日本的活检证实的IgAN成人大型多民族队列被纳入研究。主要结局和测量方法cox比例风险模型用于分析肾小球滤过率（eGFR）估计下降50%或终末期肾病的风险，并使用R-D(2)测量、Akaike信息标准（AIC）、C统计、连续净再分类改善（NRI）、综合判别改善（IDI）和校准图进行评估。结果纳入3927例患者；平均年龄35.4岁（四分位数间距28.0-45.4）；男性2173例（55.3%）。在2781例患者的衍生队列中建立了以下预测模型：临床模型包括eGFR、血压和活检时的蛋白尿；2个完整模型，包含MEST组织学评分、年龄、药物使用、种族/民族特征（白人、日本人或中国人）或无种族/民族特征，以便适用于其他种族群体。与临床模型相比，有和没有人种/民族的完整模型具有更好的R-D(2)（分别为26.3%和25.3%,vs . 20.3%）和AIC（分别为6338和6379，vs . 6485）， C统计量分别从0.78显著增加到0.82和0.81 (δ C, 0.04, 95% CI, 0.03-0.04和δ C， 0.03, 95% CI, 0.02-0.03)， NRI评估的再分类显著改善(0.18,95% CI, 0.07-0.29和0.51；95% CI，分别为0.39-0.62)和IDI （0.07; 95% CI，分别为0.06-0.08和0.06;95% CI，分别为0.05-0.06）。外部验证在1146例患者中进行。对于两个完整模型，C统计量（0.82;95% CI，有种族/民族的0.81-0.83;0.81;95% CI，无种族/民族的0.80-0.82）和R-D(2)（均为35.3%）与验证队列相似或更好，具有良好的校准。结论和相关性在本研究中，这两个完整的预测模型被证明是准确和有效的方法，用于预测多种族队列中IgAN的疾病进展和患者风险分层，并可用于临床试验设计和生物标志物研究。
ImportanceAlthough IgA nephropathy (IgAN) is the most common glomerulonephritis in the world, there is no validated tool to predict disease progression. This limits patient-specific risk stratification and treatment decisions, clinical trial recruitment, and biomarker validation. ObjectiveTo derive and externally validate a prediction model for disease progression in IgAN that can be applied at the time of kidney biopsy in multiple ethnic groups worldwide. Design, Setting, and ParticipantsWe derived and externally validated a prediction model using clinical and histologic risk factors that are readily available in clinical practice. Large, multi-ethnic cohorts of adults with biopsy-proven IgAN were included from Europe, North America, China, and Japan. Main Outcomes and MeasuresCox proportional hazards models were used to analyze the risk of a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage kidney disease, and were evaluated using the R-D(2) measure, Akaike information criterion (AIC), C statistic, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and calibration plots. ResultsThe study included 3927 patients; mean age, 35.4 (interquartile range, 28.0-45.4) years; and 2173 (55.3%) were men. The following prediction models were created in a derivation cohort of 2781 patients: a clinical model that included eGFR, blood pressure, and proteinuria at biopsy; and 2 full models that also contained the MEST histologic score, age, medication use, and either racial/ethnic characteristics (white, Japanese, or Chinese) or no racial/ethnic characteristics, to allow application in other ethnic groups. Compared with the clinical model, the full models with and without race/ethnicity had better R-D(2) (26.3% and 25.3%, respectively, vs 20.3%) and AIC (6338 and 6379, respectively, vs 6485), significant increases in C statistic from 0.78 to 0.82 and 0.81, respectively (Delta C, 0.04; 95% CI, 0.03-0.04 and Delta C, 0.03; 95% CI, 0.02-0.03, respectively), and significant improvement in reclassification as assessed by the NRI (0.18; 95% CI, 0.07-0.29 and 0.51; 95% CI, 0.39-0.62, respectively) and IDI (0.07; 95% CI, 0.06-0.08 and 0.06; 95% CI, 0.05-0.06, respectively). External validation was performed in a cohort of 1146 patients. For both full models, the C statistics (0.82; 95% CI, 0.81-0.83 with race/ethnicity; 0.81; 95% CI, 0.80-0.82 without race/ethnicity) and R-D(2) (both 35.3%) were similar or better than in the validation cohort, with excellent calibration. Conclusions and RelevanceIn this study, the 2 full prediction models were shown to be accurate and validated methods for predicting disease progression and patient risk stratification in IgAN in multi-ethnic cohorts, with additional applications to clinical trial design and biomarker research.