Mean value of CKD and non-CKD patients.

Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk. Our model utilizes electronic medical record (EMR) data, specifically by incorporating patient demographics, laboratory results, chronic conditions, risk factors, and medication codes to predict the onset of CKD in diabetic patients six months in advance, achieving an average Area Under the Curve (AUC) of 0.88. We leverage aggregated EMR data to effectively capture relevant information within the observation year instead of using temporal EMR data. Furthermore, we identify the most significant features for predicting CKD onset, including mean, minimum, and first quartile of estimated glomerular filtration rate (eGFR) during the observation year, along with variables such as diagnosis age and duration of hypertension, osteoarthritis, and diabetes, as well as levels of hemoglobin and fasting blood glucose (FBG). We also explored a refined model utilizing only these most significant features, which yields a slightly lower AUC of 0.86. These variables are typically available in primary data, empowering physicians for real-time risk assessment. The proposed model’s ability to identify higher-risk patients is essential for timely intervention, personalized care, risk stratification, patient education, and potential cost savings. This research contributes valuable insights for healthcare practitioners seeking efficient tools for early CKD detection in diabetic populations.慢性肾脏病（Chronic kidney disease, CKD）影响全球超过13%的人口，总患病人数逾8亿。及时识别与干预对于延缓CKD进展、改善患者预后至关重要。本研究聚焦于开发一款预测模型，基于糖尿病患者的CKD发病风险，对出现肾功能受损迹象的糖尿病患者进行分类。本模型利用电子病历（electronic medical record, EMR）数据，具体纳入患者人口统计学信息、实验室检验结果、慢性疾病史、危险因素及药物编码，以提前6个月预测糖尿病患者的CKD发病风险，最终取得了0.88的平均受试者工作特征曲线下面积（Area Under the Curve, AUC）。 本研究未采用时序型电子病历数据，而是借助观测年内的聚合电子病历数据，有效捕获相关临床信息。此外，本研究明确了预测CKD发病的关键特征：包括观测年内估算肾小球滤过率（estimated glomerular filtration rate, eGFR）的均值、最小值与第一四分位数，同时涵盖确诊年龄、高血压、骨关节炎及糖尿病的患病时长，以及血红蛋白与空腹血糖（fasting blood glucose, FBG）水平等变量。本研究还探索了仅使用上述核心特征的精简模型，其AUC略降至0.86。上述变量通常可从原始临床数据中获取，能够助力医师开展实时风险评估。 本研究所提出的模型识别高危患者的能力，对于及时干预、个性化诊疗、风险分层、患者教育及潜在医疗成本节约均具有重要价值。本研究可为寻求高效工具以实现糖尿病群体CKD早期筛查的医疗从业者提供极具参考意义的见解。