Yau Sarah Tsz Yui, Hung Chi Tim, Leung Eman Yee Man, Chong Ka Chun, Lee Albert, Yeoh Eng Kiong
JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
J Clin Med. 2024 Dec 24;14(1):4. doi: 10.3390/jcm14010004.
Previous epidemiological studies have shown that diabetes is associated with an increased risk of several cancers, including bladder cancer. However, prediction models for bladder cancer among diabetes patients remain scarce. This study aims to develop a scoring system for bladder cancer risk prediction among diabetes patients who receive routine care in general outpatient clinics using a machine learning-guided approach. A territory-wide retrospective cohort study was conducted using electronic health records of Hong Kong. Patients who received diabetes care in public general outpatient clinics between 2010 and 2019 without a history of malignancy were identified and followed up until December 2019. To develop a scoring system for bladder cancer risk prediction, random survival forest was employed to guide variable selection, and Cox regression was subsequently applied for weight assignment. Of the 382,770 patients identified, 644 patients developed bladder cancer during follow-up (median: 6.2 years). The incidence rate was 0.29 per 1000 person-years. In the final time-to-event scoring system, age, serum creatinine, sex, and smoking were included as predictors. Serum creatinine ≥94 µmol/L appeared to be associated with an increased risk of developing bladder cancer. The 2-year and 5-year AUCs on test set were 0.88 (95%CI: 0.84-0.92) and 0.86 (95%CI: 0.80-0.92) respectively. Renal dysfunction could be a potential predictor of bladder cancer among diabetes patients. The proposed scoring system could be potentially useful for providing individualized risk prediction among diabetes patients.
以往的流行病学研究表明，糖尿病与包括膀胱癌在内的多种癌症风险增加有关。然而，糖尿病患者中膀胱癌的预测模型仍然很少。本研究旨在使用机器学习引导的方法，为在普通门诊接受常规护理的糖尿病患者开发一种膀胱癌风险预测评分系统。利用香港的电子健康记录进行了一项全地区的回顾性队列研究。确定了2010年至2019年期间在公立普通门诊接受糖尿病护理且无恶性肿瘤病史的患者，并随访至2019年12月。为了开发膀胱癌风险预测评分系统，采用随机生存森林指导变量选择，随后应用Cox回归进行权重分配。在确定的382,770名患者中，644名患者在随访期间患上了膀胱癌（中位数：6.2年）。发病率为每1000人年0.29例。在最终的事件发生时间评分系统中，纳入年龄、血清肌酐、性别和吸烟作为预测因素。血清肌酐≥94µmol/L似乎与患膀胱癌的风险增加有关。测试集上的2年和5年AUC分别为0.88（95%CI：0.84-0.92）和0.86（95%CI：0.80-0.92）。肾功能不全可能是糖尿病患者膀胱癌的潜在预测因素。所提出的评分系统可能有助于为糖尿病患者提供个性化的风险预测。
