机器学习在糖尿病患者中识别了与代谢功能障碍相关的脂肪性肝病
Katarzyna Nabrdalik1,2, Hanna Kwiendacz1, Krzysztof Irlik2,3
1Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland.
The Journal of clinical endocrinology and metabolism
|February 8, 2024
概括
机器学习模型在糖尿病患者中准确地识别了与代谢功能障碍相关的脂肪性肝病 (MASLD). 这有助于高风险个体早期预防心血管风险.
科学领域:
- 心脏病学 心脏病学
- 肝病学 肝病学是一种肝病学.
- 医疗信息学 医疗信息学
背景情况:
- 糖尿病患者的代谢功能障碍相关的脂肪性肝病 (MASLD) 增加了心血管疾病 (CVD) 的风险.
- 在这个高风险人群中,MASLD的诊断往往不足.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于评估DM患者的MASLD风险.
- 在糖尿病患者中确定MASLD预测的关键区分参数.
主要方法:
- 特征选择确定了八个歧视性参数:年龄,BMI,DM类型,肝酶,血小板计数,高尿血和甲胺使用.
- 用灵敏度,特异性,ROC曲线分析 (AUC) 和决策曲线分析 (DCA) 来评估模型性能.
- 使用单独的测试队列证实了模型的概括性.
主要成果:
- 在DM患者中,ML模型在识别MASLD方面取得了很高的准确性 (灵敏度0.75,特异性0.79;AUC0.84).
- DCA表明了显著的临床实用性,净益处在30%至84%的值概率之间.
- 该模型在测试组中显示了概括性 (灵敏度0.80,特异性0.74;AUC0.81),无监督的集群识别了高风险子组.
结论:
- 机器学习为在DM患者中检测MASLD提供了一种高性能方法.
- 这种ML工具可以改善风险分层,并为患有MASLD的高危DM患者指导心血管风险预防策略.
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