临床特征的机器学习辅助查,用于预测难以治疗的类风湿性关节炎
Jiří Baloun1,2, Lucie Andrés Cerezo1,2, Tereza Kropáčková1,2
1Institute of Rheumatology, Na Slupi 450/4, Prague, 128 00, Czechia.
Scientific reports
|October 6, 2025
概括
机器学习模型可以使用临床数据预测难以治疗的 (D2T) 类风湿性关节炎 (RA). 通过这些模型,通过早期识别D2T RA,可以及时进行干预,以改善患者的治疗结果.
科学领域:
- 类风湿病学 类风湿病学
- 人工智能在医学中的应用
- 临床预测建模临床预测建模
背景情况:
- 类风湿性关节炎 (RA) 管理的目标是缓解,但有些患者会发展出难以治疗的 (D2T) RA.
- 提前预测D2T RA对于优化治疗策略和改善长期预后至关重要.
研究的目的:
- 提前识别预测符合难以治疗 (D2T) 类风湿性关节炎 (RA) 定义风险的临床特征.
- 开发和验证用于早期D2T RA预测的机器学习模型.
主要方法:
- 来自ATTRA注册表的RA患者的回顾性分析启动了生物 (b-) 或向合成 (ts-) 疾病修饰性抗风湿药物 (DMARDs).
- 使用各种机器学习技术 (例如随机森林,XGBoost) 开发预测模型.
- 利用Shapley增量解释 (SHAP) 值来解释模型预测并识别关键预测变量.
主要成果:
- 机器学习模型实现了0.606-0.747的精度范围和0.656-0.832的AUC,用于预测D2T RA.
- 关键预测变量包括疾病活性指标 (DAS28-ESR,CDAI,CRP),患者报告的结果 (HAQ) 和b/tsDMARD治疗持续时间.
- 在满足正式标准前长达一年的时间内,确定了可预测D2T RA的临床特征.
结论:
- 在基线和最多一年之前评估的临床特征可以预测D2T RA的发展.
- 早期识别D2T RA指标对于及时的治疗干预至关重要.
- 这些发现支持主动管理策略,以改善D2T RA风险的RA患者的长期结果.
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