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预测儿童群体中肥胖药物的体重结果
Md Mozaharul Mottalib1, Rahmatollah Beheshti1,2, Karthik Viswanathan2
1Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.
Pediatric obesity
|February 12, 2026
概括
现实世界的数据显示,肥胖药物 (OMs) 在青少年中具有相似的有效性,而Semaglutide略高于其他药物. 预测模型可以个性化OM选择以获得更好的结果.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 药物治疗 药物治疗
- 数据科学数据科学数据科学
背景情况:
- 有限的现实研究比较了儿科肥胖药物治疗 (OM) 的结果.
- 了解现实世界的变化和预测因素对于有效治疗至关重要.
研究的目的:
- 分析青少年不同OM的现实结果.
- 根据患者和治疗因素,开发OM治疗成功的预测模型.
主要方法:
- 利用了来自595名患有肥胖的青少年 (12-21岁) 的电子健康记录数据.
- 评估BMI百分位数在95百分位以上的百分位数 (BMIp95) 变化.
- 使用Catboost的机器学习来预测使用患者和治疗变量,BMIp95的减少>=5%.
主要成果:
- 在12个月的时间里,OMs (利拉格卢提德,芬特明,芬特明/托皮拉马特,塞马格卢提德) 显示了可比的结果.
- 塞马格卢提德的平均BMIp95降低率略高 (12.87%).
- 预测模型实现了AUROC>=0.75,其中OM持续时间,基线%BMIp95,以及专业访问作为关键预测因素.
结论:
- 研究的OM在青少年中具有类似的现实世界的有效性.
- 预测模型可以帮助个人化OM治疗的临床决策.
- 为未来的研究,建议对预测模型进行前性验证.
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