在零星的克鲁茨菲尔特-雅各布病中,可解释深度学习的生存预测
Johnny Tam1, John Centola2, Hatice Kurucu2
1The UK National CJD Research and Surveillance Unit, Centre for Clinical Brain Sciences, Chancellor's Building, University of Edinburgh, Edinburgh, EH16 4TG, UK. jtam@ed.ac.uk.
Journal of neurology
|December 16, 2024
概括
我们使用英国监测数据开发了一种零星克鲁茨菲尔特-雅各布病 (sCJD) 的生存模型. 该模型准确地预测了患者的存活率,有助于对这种致命的病的护理计划.
科学领域:
- 神经学 神经学
- 子疾病是子疾病.
- 生物统计学 生物统计学
背景情况:
- 散发性克鲁茨菲尔特-雅各布病 (sCJD) 是一种快速进展的,致命的病.
- 患者的生存率是高度可变的,复杂的预后和护理.
- 英国的监测数据被用来开发一种新的生存模型.
研究的目的:
- 开发和验证零散克鲁茨菲尔特-雅各布病 (sCJD) 的生存模型.
- 为了利用常规收集的诊断数据进行预后.
- 为改善sCJD患者的护理规划和临床试验分层.
主要方法:
- 来自英国国家监测的655个可能或确定的sCJD病例的分析 (01/2017-01/2022).
- 包括临床症状,中枢神经的RT-QuIC,14-3-3,MRI,EEG,PRNP编码129多态,以及中枢神经中的蛋白质水平.
- 应用基于人工神经网络的多任务物流回归来进行生存分析.
主要成果:
- 开发的算法实现了0.732的c指数和0.866 (5个月) 和0.872 (10个月) 的AUC.
- 性能与考克斯的比例危险模型相当,并且略有改进.
- 确定了PRNP编码子129的多态性和CSF 14-3-3作为重要的预测特征.
结论:
- 在诊断时常规收集的临床数据可以有效预测sCJD生存率.
- 分析管道提供了临床上有意义的解释,增强对sCJD的理解.
- 进一步验证可以改善预后,护理规划和临床试验分层.
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