在风险预测模型开发中使用患者报告的结果,以支持癌症护理交付:一个范围审查
Roshan Paudel1, Samira Dias1, Carrie G Wade2
1Dana-Farber Cancer Institute, Boston, MA.
JCO clinical cancer informatics
|November 1, 2024
概括
在常规瘤治疗中收集的患者报告结果 (PROs) 可以训练风险预测模型 (RPMs),以改善患者结果预测. 整合PRO可提高模型性能,为个性化癌症护理提供有价值的见解.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 患者报告的结果 (PRO) 越来越多地被整合到电子健康记录 (EHR) 中,以系统地收集症状数据.
- 在常规护理中的PRO数据与改善治疗成功,坚持和患者满意度有关.
- 在临床试验中,PROs在预测临床试验中的存活率方面具有预后价值.
研究的目的:
- 进行范围审查,评估PROs在训练风险预测模型 (RPMs) 中对瘤患者结果的利用情况.
- 了解如何常规收集的PRO数据应用于开发癌症护理中的预测模型.
主要方法:
- 在四个主要数据库 (MEDLINE,CINAHL,Embase,Web of Science) 进行了全面的文献搜索.
- 包括使用PRO作为模型培训预测器的同行评审瘤学文章.
- 数据提取的重点是研究特征,临床结果以及模型开发和验证过程.
主要成果:
- 在1,254项已识别的研究中,18项符合审查的纳入标准.
- 大多数研究都使用了前性收集的PRO数据的回顾性分析来开发模型.
- 包含PRO数据的模型与没有PRO数据的模型相比,显示出更高的区分性能,尽管校准报告有限.
结论:
- 在临床实践中系统地收集PRO,为在瘤学中开发强大的RPM提供了重大机会.
- 将PRO与其他数据源一起纳入可以明显提高预测模型的性能.
- 利用PRO数据可以导致更准确的预测,并有可能改善癌症护理中的患者管理.
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