机器学习算法使用国家注册数据来预测结核病治疗期间的后续损失
Moreno M S Rodrigues1, Beatriz Barreto-Duarte2, Caian L Vinhaes3
1Fundação Oswaldo Cruz.
Research square
|January 3, 2024
概括
一个新的风险评分预测结核病患者在治疗期间失去随访的风险. 该工具使用国家注册表数据帮助医疗保健工作者识别有风险的个体,并提高治疗遵守率.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 识别患有失去随访 (LTFU) 风险的患者对于优化结核病 (TB) 临床管理至关重要.
- 国家注册数据可以为预测模型提供信息,以提醒医疗保健工作者了解LTFU风险.
- 这项研究使用来自巴西可报告疾病信息系统 (SINAN) 的全国数据开发了抗结核治疗 (ATT) 期间LTFU的风险评分.
研究的目的:
- 开发和验证一个风险评分来预测在巴西接受ATT的成年结核病患者中LTFU的风险.
- 利用来自巴西可报告疾病信息系统 (SINAN) 的数据进行预测建模.
- 为医疗保健专业人员创建一个易于使用的工具来评估LTFU风险.
主要方法:
- 对向SINAN报告的结核病例 (2015-2022年) 的回顾性分析,不包括儿童,弱势群体和耐药结核病.
- 使用预处理数据开发和内部验证三种预测模型 (逻辑回归,随机森林,光梯度增强).
- 数据分为培训 (~80%) 和测试 (~20%) 集,用于基于AUC指标的模型比较.
主要成果:
- 该研究包括243,726例结核病例;41,373人 (17%) 经历了LTFU.
- 八个关键特征 (以前的结核病,药物使用,年龄,性别,艾滋病毒感染,学业水平) 用于开发预测得分.
- 光梯度增强模型获得了最佳性能 (AUC 0.71-0.72),并开发了一个网络计算器应用程序.
结论:
- 全国风险评分有效预测巴西成年人的LTFU风险在ATT开始之前.
- 这种工具可以帮助决策资源分配和直接观察处理 (DOT) 指示.
- 该得分旨在改善结核病治疗的坚持和患者的治疗结果.
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