在1型糖尿病成年人中,机器学习驱动的并发症和死亡率的预测
Jonas Dahl Andersen1,2, Carsten Wridt Stoltenberg1,2, Morten Hasselstrøm Jensen1,3
1Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Journal of diabetes science and technology
|August 2, 2024
概括
通过新的模型,预测心血管疾病 (CVD) 和1型糖尿病 (T1D) 的死亡率是可行的. 社会经济地位 (SES) 显著影响T1D患者糖尿病病 (DKD) 的预测.
科学领域:
- 内分泌学和糖尿病研究研究
- 公共卫生和流行病学
- 生物统计学和预测建模
背景情况:
- 1型糖尿病 (T1D) 由于心血管疾病 (CVD) 和糖尿病病 (DKD) 等并发症而带来重大负担.
- 早期识别患有这些并发症高风险的个体对于及时干预和改善患者结果至关重要.
- 结合社会经济地位 (SES) 的预测模型可以提高T1D管理中的风险分层.
研究的目的:
- 开发和验证CVD,DKD和T1D成年人死亡率的预测模型.
- 评估社会经济地位 (SES) 对这些模型预测性能的贡献.
- 改进针对性干预的高风险T1D个体的早期识别.
主要方法:
- 使用了丹麦全国登记处的数据,对一组患有T1D的成年人 (≥21岁) 进行了研究.
- 开发了后勤回归模型来预测CVD,DKD和T1D诊断后五年内死亡率.
- 使用交叉验证,AUROC和PR-AUC评估模型性能,分析特征重要性,包括SES指标 (收入,教育).
主要成果:
- 该研究包括6572名患有T1D的成年人;6%患有心血管疾病,10%患有DKD,14%在五年内死亡.
- 模型对心血管疾病 (AUROC 0.79) 和死亡率 (AUROC 0.87) 的预测性表现良好,但对DKD (AUROC 0.61) 的预测性表现适度.
- 社会经济地位是DKD预测最重要的特征,但对心血管疾病和死亡率模型的影响最小.
结论:
- 开发的模型对早期识别心血管疾病和T1D患者的死亡风险充满希望.
- 在T1D内预测个别结果中SES的作用需要进一步研究,特别是对于DKD.
- 这些发现支持了数据驱动方法的潜力,以个性化T1D护理和风险管理.
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