Lin Wei, Shi Songchang, Huang Huibin, Wang Nengying, Wen Junping, Chen Gang
Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.
Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Brance, Fujian Provincial Hospital Jinshan Branch, Fuzhou, China.
Front Med (Lausanne). 2022 Feb 7;9:775275. doi: 10.3389/fmed.2022.775275. eCollection 2022.
Microalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.
This cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with <0.1 were extracted to build the second risk model. The second-stage model was then analyzed using a chi-square test, in which a ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with <0.05 in the second-stage model were considered important features related to the prevalence of MAU. A confusion matrix and calibration curve were used to evaluate the validity and reliability of the model. A series of risk prediction scores were established based on machine learning algorithms.
Systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.
Based on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU.
微量白蛋白尿（MAU）是由普遍的内皮损伤引起的，它与肾脏疾病、中风、心肌梗死和冠状动脉疾病密切相关。筛查MAU高危患者可能有助于早期识别心血管事件风险和死亡率增加的个体。因此，本研究旨在应用机器学习算法建立MAU风险模型。
本横断面研究纳入了3294名年龄在16至93岁之间的参与者。使用R软件分析缺失值并进行多重填补。根据7:3的比例将观察人群分为训练集和验证集。使用准备好的数据构建第一个风险模型，然后提取P值<0.1的变量构建第二个风险模型。然后使用卡方检验分析第二阶段模型，其中P≥0.05被认为表明模型拟合无差异。第二阶段模型中P<0.05的变量被认为是与MAU患病率相关的重要特征。使用混淆矩阵和校准曲线评估模型的有效性和可靠性。基于机器学习算法建立了一系列风险预测分数。
收缩压（SBP）、舒张压（DBP）、空腹血糖（FBG）、甘油三酯（TG）水平、性别、年龄和吸烟被确定为MAU患病率的预测因素。使用卡方检验、混淆矩阵和校准曲线进行验证表明，可以根据风险评分预测MAU风险。
基于我们的机器学习算法建立有效风险评分的能力，我们建议在MAU筛查过程中应包括对SBP、DBP、FBG、TG、性别、年龄和吸烟的综合评估。
