Department of Nephrology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
BMC Med Inform Decis Mak. 2024 Jun 19;24(1):173. doi: 10.1186/s12911-024-02568-2.
Because spontaneous remission is common in IMN, and there are adverse effects of immunosuppressive therapy, it is important to assess the risk of progressive loss of renal function before deciding whether and when to initiate immunosuppressive therapy. Therefore, this study aimed to establish a risk prediction model to predict patient prognosis and treatment response to help clinicians evaluate patient prognosis and decide on the best treatment regimen.
From September 2019 to December 2020, a total of 232 newly diagnosed IMN patients from three hospitals in Liaoning Province were enrolled. Logistic regression analysis selected the risk factors affecting the prognosis, and a dynamic online nomogram prognostic model was constructed based on extreme gradient boost, random forest, logistic regression machine learning algorithms. Receiver operating characteristic and calibration curves and decision curve analysis were utilized to assess the performance and clinical utility of the developed model.
A total of 130 patients were in the training cohort and 102 patients in the validation cohort. Logistic regression analysis identified four risk factors: course ≥ 6 months, UTP, D-dimer and sPLA2R-Ab. The random forest algorithm showed the best performance with the highest AUROC (0.869). The nomogram had excellent discrimination ability, calibration ability and clinical practicability in both the training cohort and the validation cohort.
The dynamic online nomogram model can effectively assess the prognosis and treatment response of IMN patients. This will help clinicians assess the patient's prognosis more accurately, communicate with the patient in advance, and jointly select the most appropriate treatment plan.
由于特发性膜性肾病（IMN）常自发缓解，免疫抑制治疗存在不良反应，因此在决定是否及何时开始免疫抑制治疗前，评估肾功能进行性丧失的风险非常重要。为此，本研究旨在建立风险预测模型，以预测患者的预后和治疗反应，帮助临床医生评估患者的预后并选择最佳治疗方案。
本研究纳入了 2019 年 9 月至 2020 年 12 月来自辽宁省 3 家医院的 232 例新诊断的 IMN 患者。采用逻辑回归分析筛选影响预后的风险因素，并基于极端梯度提升、随机森林、逻辑回归机器学习算法构建动态在线列线图预测模型。采用受试者工作特征曲线和校准曲线以及决策曲线分析评估模型的性能和临床实用性。
共纳入 130 例患者进入训练队列，102 例患者进入验证队列。逻辑回归分析确定了 4 个风险因素：病程≥6 个月、尿蛋白总量（UTP）、D-二聚体和 sPLA2R-Ab。随机森林算法的 AUC 最高（0.869），表现出最佳性能。该列线图在训练队列和验证队列中均具有良好的判别能力、校准能力和临床实用性。
动态在线列线图模型可有效评估 IMN 患者的预后和治疗反应。这将有助于临床医生更准确地评估患者的预后，与患者提前沟通，并共同选择最合适的治疗方案。
