[Establishment and validation of clinical prediction model for steroid-resistant nephrotic syndrome in children].

Objective: To identify the clinically relevant factors of steroid-resistant nephrotic syndrome (SSNS) in children and establish a predictive model followed by verifying its feasibility. Methods: A retrospective analysis was performed in a total of 111 children with nephrotic syndrome admitted to Children's Hospital of ShanXi from January 2016 to December 2021. The clinical data of general conditions, manifestations, laboratory tests, treatment, and prognosis were collected. According to the steroid response, patients were divided into SSNS and steroid resistant nephrotic syndrome (SRNS) group. Single factor Logistic regression analysis was used for comparison between the 2 groups, and variables with statistically significant differences were included in multivariate Logistic regression analysis. The multivariate Logistic regression analysis was used to identify the related variables of children with SRNS. The area under the receiver operating characteristic curve (ROC), the calibration curve and the clinical decision curve were used to evaluate its effectiveness of the variables. Results: Totally 111 children with nephrotic syndrome was composed of 66 boys and 45 girls, aged 3.2 (2.0, 6.6) years. There were 65 patients in the SSNS group and 46 in the SRNS group.Univariate Logistic regression analysis showed that the 6 variables, including erythrocyte sedimentation rate, 25-hydroxyvitamin D, suppressor T cells, D-dimer, fibrin degradation products, β2-microglobulin, had statistically significant differences between SSNS and SRNS groups (85 (52, 104) vs. 105 (85, 120) mm/1 h, 18 (12, 39) vs. 16 (12, 25) nmol/L, 0.23 (0.19, 0.27) vs. 0.25 (0.20, 0.31), 0.7 (0.6, 1.1) vs. 1.1 (0.9, 1.7) g/L, 3.1 (2.3, 4.1) vs. 3.3 (2.7, 5.8) g/L, 2.3 (1.9,2.8) vs. 3.0 (2.5, 3.7) g/L, χ2=3.73, -2.42, 2.24, 3.38, 2.24,3.93,all P<0.05), were included in the multivariate Logistic regression analysis. Finally, we found that 4 variables including erythrocyte sedimentation rate, suppressor T cells, D-dimer and β2-microglobulin (OR=1.02, 1.12, 25.61, 3.38, 95%CI 1.00-1.04, 1.03-1.22, 1.92-341.04, 1.65-6.94, all P<0.05) had significant correlation with SRNS. The optimal prediction model was selected. The ROC curve cut-off=0.38, with the sensitivity of 0.83, the specificity of 0.77 and area under curve of 0.87. The calibration curve showed that the predicted probability of SRNS group occurrence was in good agreement with the actual occurrence probability, χ2=9.12, P=0.426. The clinical decision curve showed good clinical applicability. The net benefit is up to 0.2. Make the nomogram. Conclusions: The prediction model based on the 4 identified risk factors including erythrocyte sedimentation rate, suppressor T cells, D-dimer and β2-microglobulin was suitable for the early diagnosis and prediction of SRNS in children. The prediction effect was promising in clinical application.目的： 筛选儿童激素耐药型肾病综合征的临床相关因素，并构建预测模型，同时验证有效性。 方法： 回顾性分析2016年1月至2021年12月山西省儿童医院收治的111例肾病综合征患儿的临床资料，包括一般情况、临床表现、实验室检查、治疗和预后情况等。根据患儿对激素治疗是否敏感，分为激素敏感型肾病综合征和激素耐药型肾病综合征2组，两组间比较使用单因素Logistic回归分析；儿童激素耐药型肾病综合征的相关性采用多因素Logistic回归分析，采用受试者工作特征（ROC）曲线、校准曲线以及临床决策曲线评价其有效性。 结果： 111例肾病综合征患儿中男66例、女45例，年龄3.2（2.0，6.6）岁，其中激素敏感型组65例、激素耐药型组46例。单因素Logistic回归分析示，激素敏感型和激素耐药型两组患儿在红细胞沉降率、25羟维生素D、抑制性T细胞、D-二聚体、纤维蛋白降解产物、β2微球蛋白水平方面差异均有统计学意义［85（52，104）比105（85，120）mm/1 h，18（12，39）比16（12，25）nmol/L，0.23（0.19，0.27）比0.25（0.20，0.31），0.7（0.6，1.1）比1.1（0.9，1.7）g/L，3.1（2.3，4.1）比3.3（2.7，5.8）g/L，2.3（1.9，2.8）比3.0（2.5，3.7）g/L，χ2=3.73、-2.42、2.24、3.38、2.24、3.93，均P<0.05］。多因素Logistic回归分析显示，红细胞沉降率、抑制性T细胞、D-二聚体及β2微球蛋白与激素耐药型肾病综合征均有显著相关性（OR=1.02、1.12、25.61、3.38，95%CI 1.00~1.04、1.03~1.22、1.92~341.04、1.65~6.94，均P<0.05）。根据模型绘制ROC曲线示截断值为0.38，灵敏度0.83，特异度0.77，曲线下面积0.87；校准曲线显示预测激素耐药组发生概率与实际发生概率一致性较好（χ2=9.12，P=0.426）；临床决策曲线显示临床适用度好，净获益最高达0.2。制作列线图。 结论： 儿童激素耐药型肾病综合征的危险因素包括红细胞沉降率、抑制性T细胞、D-二聚体及β2微球蛋白，建立预测模型适用于儿童激素耐药型肾病综合征的早期诊断及预测，预测效果较好，具有一定的临床价值。.