Division of Nephrology, University of British Columbia, Vancouver, British Columbia, Canada.
BC Renal, Vancouver, British Columbia, Canada.
JAMA Intern Med. 2019 Jul 1;179(7):942-952. doi: 10.1001/jamainternmed.2019.0600.
Although 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.
To 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 PARTICIPANTS: We 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.
Cox 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 R2D measure, Akaike information criterion (AIC), C statistic, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and calibration plots.
The 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 R2D (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 (ΔC, 0.04; 95% CI, 0.03-0.04 and ΔC, 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 R2D (both 35.3%) were similar or better than in the validation cohort, with excellent calibration.
In 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.
尽管 IgA 肾病（IgAN）是世界上最常见的肾小球肾炎，但目前尚无经过验证的工具可以预测疾病进展。这限制了患者特异性风险分层和治疗决策、临床试验招募以及生物标志物验证。
开发并在全球多个种族的人群中，从活检时就可应用的 IgAN 疾病进展的预测模型，并对其进行外部验证。
设计、地点和参与者：我们使用临床和组织学危险因素来开发和外部验证预测模型，这些危险因素在临床实践中很容易获得。来自欧洲、北美、中国和日本的大型、多民族成人活检证实的 IgAN 队列被纳入研究。
使用 Cox 比例风险模型分析估计肾小球滤过率（eGFR）下降 50%或终末期肾病的风险，并使用 R2D 测量、赤池信息量准则（AIC）、C 统计量、连续净重新分类改善（NRI）、综合判别改善（IDI）和校准图进行评估。
研究共纳入 3927 例患者；平均年龄为 35.4 岁（四分位距，28.0-45.4）；2173 例（55.3%）为男性。在一个 2781 例患者的推导队列中创建了以下预测模型：一个包含活检时 eGFR、血压和蛋白尿的临床模型；以及两个完整的模型，还包含 MEST 组织学评分、年龄、药物使用以及种族/民族特征（白人、日本人或中国人）或没有种族/民族特征，以允许在其他种族群体中应用。与临床模型相比，包含种族/民族特征的完整模型和不包含种族/民族特征的完整模型的 R2D 更高（分别为 26.3%和 25.3%，而 20.3%），AIC 更低（分别为 6338 和 6379，而 6485），C 统计量显著增加（分别为 0.78 至 0.82 和 0.81，ΔC 为 0.04；95%CI 为 0.03-0.04 和 ΔC 为 0.03；95%CI 为 0.02-0.03），重新分类评估的 NRI 显著改善（分别为 0.18；95%CI 为 0.07-0.29 和 0.51；95%CI 为 0.39-0.62）和 IDI（分别为 0.07；95%CI 为 0.06-0.08 和 0.06；95%CI 为 0.05-0.06）。在一个 1146 例患者的外部验证队列中进行了外部验证。对于两个完整的模型，C 统计量（种族/民族特征的模型为 0.82；95%CI 为 0.81-0.83；没有种族/民族特征的模型为 0.81；95%CI 为 0.80-0.82）和 R2D（均为 35.3%）与验证队列相似或更好，具有出色的校准度。
在这项研究中，两个完整的预测模型被证明是准确的，可用于预测多民族人群中 IgAN 的疾病进展和患者风险分层，并且在临床试验设计和生物标志物研究中也有额外的应用。
