Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University; Key Laboratory of Nephrology, Ministry of Health and Guangdong Province, Guangzhou, Guangdong 510080, China.
Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
Chin Med J (Engl). 2018 Jun 5;131(11):1275-1281. doi: 10.4103/0366-6999.232809.
Lupus nephritis (LN) is classified by renal biopsy into proliferative and nonproliferative forms, with distinct prognoses, but renal biopsy is not available for every LN patient. The present study aimed to establish an alternate tool by building a predictive model to evaluate the probability of proliferative LN.
In this retrospective cohort with biopsy-proven LN, 382 patients in development cohort, 193 in internal validation cohort, and 164 newly diagnosed patients in external validation cohort were selected. Logistic regression model was established, and the concordance statistics (C-statistics), Akaike information criterion (AIC), integrated discrimination improvement, Hosmer-Lemeshow test, and net reclassification improvement were calculated to evaluate the performance and validation of models.
The prevalence of proliferative LN was 77.7% in the whole cohort. A model, including age, gender, systolic blood pressure, hemoglobin, proteinuria, hematuria, and serum C3, performed well on good-of-fit and discrimination in the development chohort to predict the risk of proliferative LN (291 for AIC and 0.84 for C-statistics). In the internal and external validation cohorts, this model showed good capability for discrimination and calibration (0.84 and 0.82 for C-statistics, and 0.99 and 0.75 for P values, respectively).
This study developed and validated a model including demographic and clinical indices to evaluate the probability of presenting proliferative LN to guide therapeutic decisions and outcomes.
狼疮肾炎 (LN) 通过肾活检分为增殖性和非增殖性两种形式，具有不同的预后，但并非每个 LN 患者都能进行肾活检。本研究旨在通过构建预测模型来评估增殖性 LN 的概率，建立一种替代工具。
本研究回顾性分析了经肾活检证实的 LN 患者，共纳入了 382 例来自发展队列的患者、193 例来自内部验证队列的患者和 164 例来自外部验证队列的新诊断患者。建立了逻辑回归模型，并计算了一致性统计量（C 统计量）、赤池信息量准则（AIC）、综合判别改善、Hosmer-Lemeshow 检验和净重新分类改善，以评估模型的性能和验证。
整个队列中增殖性 LN 的患病率为 77.7%。一个包含年龄、性别、收缩压、血红蛋白、蛋白尿、血尿和血清 C3 的模型，在发展队列中对预测增殖性 LN 的风险具有良好的拟合优度和判别能力（AIC 为 291，C 统计量为 0.84）。在内、外部验证队列中，该模型也显示出良好的判别能力和校准能力（C 统计量分别为 0.84 和 0.82，P 值分别为 0.99 和 0.75）。
本研究开发并验证了一个包含人口统计学和临床指标的模型，以评估出现增殖性 LN 的概率，从而指导治疗决策和预后。
