在前列腺癌患者中预测勃起功能障碍的PROMs的价值与贝叶斯网络
Biche Osong1, Hajar Hasannejadasl1, Henk van der Poel2
1Department of Radiation Oncology (MAASTRO), Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands.
Technical innovations & patient support in radiation oncology
|August 27, 2024
概括
这项研究使用贝叶斯网络预测前列腺癌患者的勃起功能障碍. 患者报告的结果与临床数据相结合,与单独的临床数据相比,提供更高的预测准确性.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 健康 结果 研究 研究 结果
背景情况:
- 勃起功能障碍 (ED) 显著影响前列腺癌患者的生活质量.
- 最佳的治疗选择是具有挑战性的,因为潜在的副作用,如ED.
- 之前的逻辑回归模型识别了高风险患者,但贝叶斯网络可能更好地代表因果关系.
研究的目的:
- 开发和验证贝叶斯网络,用于预测前列腺癌患者的一年性障碍.
- 将专家知识与临床和患者报告结果测量 (PROM) 数据相结合.
- 将来自PROMs的预测结构与常规临床数据进行比较.
主要方法:
- 贝叶斯网络结构是使用来自荷兰65家医院的946名前列腺癌患者的数据开发的.
- 根据专家意见和文献对变量进行了分辨;缺失>25%的数据被排除在外.
- 医生确定的关系使用登算法进行了改进,通过AUC和校准图表评估性能.
主要成果:
- 最终的队列包括505名用于模型开发的患者和216名用于测试的患者.
- 来自PROMs的贝叶斯网络结构表现出比临床数据结构更高的性能 (AUC).
- 组合结构的AUC为0.94 (列车) 和0.84 (测试),表明具有强大的预测能力.
结论:
- 贝叶斯网络整合了PROM和专家知识,为ED预测提供了临床可信和高性能结构.
- 纳入患者的观点可以提高研究成果和决策.
- 综合数据方法提供了一个整体的患者视图,改善了辨别能力.
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