Development of Predictive Scale for Diabetic Kidney Disease Progression Based on Decision Tree Classification Model
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摘要:
目的 基于决策树分类模型建立糖尿病肾病(diabetic kidney disease,DKD)进展预测量表。 方法 回顾性收集六盘水市第二人民医院内分泌科2020年7月至2021年7月收治的308例糖尿病肾病患者作为研究对象,并将其分为微量蛋白尿组(n = 224)与显性蛋白尿组(n = 84)。对2组患者的人口学资料、既往基础病史等指标进行单因素和多因素Logistic回归分析,并运用决策树分类模型建立DKD进展预测量表。 结果 308例研究对象中84例(27.27%)为显性蛋白尿,224例(72.73%)为微量蛋白尿。多因素Logistic回归分析显示收缩压(OR = 1.022,P = 0.003)和血肌酐(OR = 1.012,P < 0.001)和总蛋白水平(OR = 0.953,P = 0.003)是引起显性蛋白尿的独立风险因素。运用决策树分类模型建立预测量表,量表总分为60分,诊断阈值为33分,决策树模型ROC曲线面积(0.781)大于多因素Logistic回归(0.769),灵敏度为95.2%,特异度为78.9%。 结论 DKD进展预测量表能够较准确的评估DKD进展,对于早期预测DKD进展具有较好的临床价值。 Abstract:Objective To establish a diabetic kidney disease (DKD) progression prediction scale based on the decision tree classification model. Methods A retrospective analysis was conducted on 308 patients with diabetic kidney disease admitted to Department of Endocrinology, the Second People's Hospital of Liupanshui from July 2020 to July 2021. The patients were divided into two groups: microalbuminuria group (n = 224) and macroalbuminuria group (n = 84). Univariate and multivariate Logistic regression analysis were performed on demographic data, past medical history and other indicators of the two groups of patients, and a DKD progression prediction scale was established using the decision tree classification model. Results Among the 308 subjects, 84 (27.27%) had macroalbuminuria and 224 (72.73%) had microalbuminuria. Multivariate Logistic regression analysis showed that systolic blood pressure (OR = 1.022, P = 0.003) and serum creatinine (OR = 1.012, P < 0.001) and total protein levels (OR = 0.953, P = 0.003) were risk factors for macroalbuminuria. The decision tree classification model was used to establish a prediction scale with a total score of 60 points and a diagnostic threshold of 33 points. The area under the ROC curve of the decision tree model (0.781) was greater than that of the multivariate logistic regression model (0.769). The sensitivity was 95.2% and the specificity was 78.9%. Conclusion DKD progression prediction scale can accurately assess the progression of DKD and has good clinical value for the early prediction of DKD progression. -
表 1 研究对象一般特征[n(%)/($\bar x \pm s $)/M(Q25,Q75)]
Table 1. Characteristics of participants [n(%)/($\bar x \pm s $)/M(Q25,Q75)]
一般特征 n=308 性别 男 169 (54.87) 女 139 (45.13%) 年龄(岁) 60.09±12.40 民族 汉族 303 (98.38) 少数民族 5 (1.62) BMI(kg/m2) 23.87±4.04 婚育史 有 303 (98.38) 无 5 (1.62) 文化程度 初中及以上 134 (43.51) 初中以下 174 (56.49) 吸烟 是 114 (37.01) 否 194 (62.99) 饮酒 是 78 (25.32) 否 230 (74.68) 糖尿病家族史 是 27 (8.77) 否 281 (91.23) 糖尿病病程(a) 6.00 (2.00,10.00) 高血压病史 是 137 (44.48) 否 171 (55.52) 高血压病程(a) 2.00 (0.00,4.00) 高血脂 是 40 (12.99) 否 268 (87.01) 高尿酸 是 23 (7.47) 否 285 (92.53) 冠心病 是 15 (4.87) 否 293 (95.13) 脑血管病史 是 48 (15.58) 否 260 (84.42) 表 2 2组间一般特征比较[n(%)/($\bar x \pm s $)/M(Q25,Q75)]
Table 2. Comparison of characteristics between the two groups [n(%)/($\bar x \pm s $)/M(Q25,Q75)]
一般特征 微量蛋白尿组(n=224) 显性蛋白尿组(n=84) t/χ2/Z P 性别 男 128 (57.14) 41 (48.81) 1.712 0.194 女 96 (42.86) 43 (51.19) 年龄(岁) 60.06±12.75 60.17±11.46 −0.071 0.945 汉族 汉族 221 (98.66) 82 (97.62) 0.422 0.523 少数民族 3 (1.34) 2 (2.38) BMI(kg/m2) 23.86±4.16 23.89±3.74 −0.056 0.945 婚育史 有 220 (98.21) 83 (98.81) 0.144 0.713 无 4 (1.79) 1 (1.19) 文化程度 初中及以上 101 (45.09) 33 (39.29) 0.842 0.366 初中以下 123 (54.91) 51 (60.71) 吸烟 是 84 (37.50) 30 (35.71) 0.084 0.777 否 140 (62.50) 54 (64.29) 饮酒 是 58 (25.89) 20 (23.81) 0.145 0.716 否 166 (74.11) 64 (76.19) 糖尿病家族史 是 21 (9.38) 6 (7.14) 0.385 0.544 否 203 (90.63) 78 (92.86) 糖尿病病程(a) 5.00 (1.00,10.00) 8.00 (3.00,14.50) −3.877 <0.001* 高血压病史 是 93 (41.52) 44 (52.38) 2.922 0.088 否 131 (58.48) 40 (47.62) 高血压病程(a) 0.00 (0.00,4.50) 0.03 (0.00,4.00) −0.863 0.392 高血脂 是 33 (14.73) 7 (8.33) 2.214 0.147 否 191 (85.27) 77 (91.67) 高尿酸 是 17 (7.59) 6 (7.14) 0.022 0.893 否 207 (92.41) 78 (92.86) 冠心病 是 9 (4.02) 6 (7.14) 1.294 0.262 否 215 (95.98) 78 (92.86) 脑血管病史 是 33 (14.73) 15 (17.86) 0.457 0.502 否 191 (85.27) 69 (82.14) *P < 0.05。 表 3 2组临床指标的差异性分析[$\bar x \pm s $/M(Q25,Q75)]
Table 3. Difference analysis of clinical indexes between the two groups [$\bar x \pm s $/M(Q25,Q75)]
临床指标 微量蛋白尿组(n=224) 显性蛋白尿组(n=84) Z/t P 收缩压(mmHg) 129.96±17.67 139.96±24.24 −3.984 <0.001* 舒张压(mmHg) 78.22±10.77 80.98±13.59 −1.852 0.065 总蛋白(g/L) 66.83±8.74 61.72±9.08 4.511 <0.001* 白蛋白(g/L) 42.05±25.87 37.07±11.10 1.713 0.089 空腹血糖(mmol/L) 11.65±4.54 10.45±3.97 2.142 0.033* 餐后血糖(mmol/L) 18.86±6.06 16.64±5.04 3.001 0.003* 空腹C肽(nmol/L) 1.96 (1.32,3.20) 2.03 (1.23,3.05) 0.863 0.394 餐后2hC肽(nmol/L) 3.28 (2.26,4.98) 3.76 (2.19,5.63) −0.633 0.532 甘油三酯(mmol/L) 1.89 (1.23,2.96) 1.82 (1.24,2.72) 0.334 0.742 胆固醇(mmol/L) 4.61±1.25 4.77±1.43 −0.943 0.351 高密度脂蛋白(mmol/L) 1.20 (1.03,1.39) 1.26 (1.05,1.47) −1.113 0.272 低密度脂蛋白(mmol/L) 3.08±1.05 3.20±1.19 −0.872 0.383 总胆红素(μmol/L) 13.55 (9.75,17.30) 8.90 (5.69,13.75) 5.842 <0.001* 直接胆红(μmol/L) 4.01 (2.72,5.49) 2.39 (1.87,3.63) 5.873 <0.001* 血红蛋白(g/L) 133.14±23.43 116.69±25.73 5.344 <0.001* 糖化血红蛋白(%) 11.30 (9.52,13.42) 10.16 (8.37,13.16) 2.832 0.005* 血尿酸(μmol/L) 345.37 (263.31,446.50) 391.50 (300.00,456.39) −1.734 0.084 血肌酐(μmol/L) 67.99 (55.48,89.13) 101.20 (67.90,195.74) −5.812 <0.001* 胱抑素(mg/L) 0.38 (0.30,0.60) 0.66 (0.38,1.17) −3.543 <0.001* 肾小球滤过率[mL/(min·1.73 m2)] 96.17 (70.44,119.82) 60.78 (23.29,92.33) 5.984 <0.001* 24h尿蛋白定量(g/24 h) 168.00 (100.00,300.00) 1188.00 (552.50,3877.50 )−9.547 <0.001* UACR(mg/g) 76.52 (47.95,110.90) 644.70 (473.30,972.95) −13.411 <0.001* *P < 0.05。 表 4 多因素Logistic回归分析
Table 4. Multivariate Logistic regression analysis
变量 B SE Wald df P OR 95%CI 收缩压 0.021 0.007 9.055 1 0.003* 1.022 1.007 1.036 总蛋白 −0.048 0.016 8.538 1 0.003* 0.953 0.923 0.984 血肌酐 0.012 0.003 20.835 1 <0.001* 1.012 1.007 1.017 常量 −1.99 1.470 1.833 1 0.176 0.137 *P < 0.05。 表 5 风险筛查评分量表
Table 5. Risk screening rating scale
自变量 OR(95%CI) 权重 赋值 收缩压 1.022 (1.007,1.036) 10 0(≤110);10(110~130);20(>130) 总蛋白 0.953 (0.923,0.984) 10 10(≤52.7);0(>52.7) 血肌酐 1.012 (1.007,1.017) 10 0(≤45.78);10(45.78~96.20);20(96.20~188.87);20(>188.87) 表 6 决策树模型和Logistic回归分析的比较
Table 6. Comparison of decision tree model and Logistic regression analysis
检验方法 AUC SE P 95%CI Logistic回归 0.769 0.032 <0.001* 0.705 0.832 决策树 0.781 0.030 <0.001* 0.721 0.840 *P < 0.05。 -
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