4.8 Article

Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning

期刊

NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41467-022-33269-x

关键词

-

资金

  1. International Progressive Multiple Sclerosis Alliance [PA-1412-02420]
  2. Natural Sciences and Engineering Research Council of Canada [RGPIN-2015-05471]
  3. Canada Institute for Advanced Research (CIFAR) Artificial Intelligence Chairs program
  4. technology maturation grant from Mila-Quebec AI Institute
  5. endMS Personnel Award from the Multiple Sclerosis Society of Canada
  6. Canada Graduate Scholarship-Masters Award from the Canadian Institutes of Health Research
  7. Fonds de recherche du Quebec-Sante/Ministere de la Sante et des Services sociaux training program

向作者/读者索取更多资源

This study aims to find a biomarker for the treatment of disability progression in multiple sclerosis and proposes a strategy of using deep learning to increase statistical power.
Disability progression in multiple sclerosis remains resistant to treatment. The absence of a suitable biomarker to allow for phase 2 clinical trials presents a high barrier for drug development. We propose to enable short proof-of-concept trials by increasing statistical power using a deep-learning predictive enrichment strategy. Specifically, a multi-headed multilayer perceptron is used to estimate the conditional average treatment effect (CATE) using baseline clinical and imaging features, and patients predicted to be most responsive are preferentially randomized into a trial. Leveraging data from six randomized clinical trials (n = 3,830), we first pre-trained the model on the subset of relapsing-remitting MS patients (n = 2,520), then fine-tuned it on a subset of primary progressive MS (PPMS) patients (n = 695). In a separate held-out test set of PPMS patients randomized to anti-CD20 antibodies or placebo (n = 297), the average treatment effect was larger for the 50% (HR, 0.492; 95% CI, 0.266-0.912; p = 0.0218) and 30% (HR, 0.361; 95% CI, 0.165-0.79; p = 0.008) predicted to be most responsive, compared to 0.743 (95% CI, 0.482-1.15; p = 0.179) for the entire group. The same model could also identify responders to laquinimod in another held-out test set of PPMS patients (n = 318). Finally, we show that using this model for predictive enrichment results in important increases in power.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据