Danilov Sergey D, Matveev Georgiy A, Babenko Alina Yu, Shlyakhto Evgeny V
Laboratory of Prediabetes and Metabolic Disorders, WCRC "Centre for Personalized Medicine", Almazov National Medical Research Centre, Saint Petersburg 197341, Russia.
J Pers Med. 2024 Jul 31;14(8):811. doi: 10.3390/jpm14080811.
The development of models predicting response to weight loss therapy using sibutramine is found in only a few cases. The objective of the work is to develop a data-driven method of personalized recommendation for obesity treatment that would predict the response to sibutramine based on the current set of patient parameters. The decision system is built on the XGBoost classification algorithm along with recursive feature selection and Shapley data valuation. Using the results of clinical trials, it was trained to estimate the probability of overcoming a weight loss threshold. The model was evaluated by the accuracy metric using the Leave-One-Out cross-validation. The model for predicting response to sibutramine treatment over 3 months has an accuracy of 71%. The model for predicting outcomes at the sixth month visit based on results at 3 months has an accuracy of 80%. Although our developed prediction model may not exhibit high precision compared to certain benchmarks, it significantly outperforms random chance or models relying only on BMI parameters. Our model used the available range of laboratory tests, which makes it possible to use this model for routine clinical use and help doctors decide whether to prescribe sibutramine.
仅在少数案例中发现了使用西布曲明预测减肥治疗反应的模型。这项工作的目的是开发一种数据驱动的个性化肥胖治疗推荐方法，该方法将基于当前患者参数集预测对西布曲明的反应。决策系统基于XGBoost分类算法以及递归特征选择和沙普利数据评估构建。利用临床试验结果，对其进行训练以估计达到减肥阈值的概率。使用留一法交叉验证通过准确率指标对模型进行评估。预测3个月内西布曲明治疗反应的模型准确率为71%。基于3个月结果预测第六个月就诊时结果的模型准确率为80%。尽管与某些基准相比，我们开发的预测模型可能未表现出高精度，但它明显优于随机猜测或仅依赖BMI参数的模型。我们的模型使用了实验室检查的可用范围，这使得该模型可用于常规临床应用，并帮助医生决定是否开具西布曲明。
