Scholarlink

Background <p>Successfully completing clinical trials for rare and heterogeneous disorders, like spinal cord injuries (SCI), remains challenging, thereby reducing the ability to test and translate promising preclinical findings. We propose synthetic controls, derived from data-driven predictions of recovery in patients undergoing standard treatments, to mitigate these challenges, in particular related to patient recruitment.</p> Methods <p>Based on data from the European Multicenter Study about Spinal Cord Injury (EMSCI) and the Sygen trial, we construct synthetic controls from personalized predictions of neurological recovery of sequences of segmental motor scores. A total of six architectures (linear, tree, and deep learning models) are compared. We demonstrate the applicability of synthetic controls through a simulation framework modeling the randomization process in a clinical trial and a case study that re-evaluates the recently completed Nogo Inhibition in SCI (NISCI) trial as a single-arm trial post hoc.</p> Results <p>The primary dataset included 4196 patients from EMSCI and 587 patients from the Sygen trial for external validation. We identified a convolutional neural network as the best-performing architecture to predict segmental motor score sequences, achieving a median root mean squared error below the neurological level of injury of 0.55. Our trial simulations demonstrate that synthetic controls are a viable alternative to randomization, as the proposed solution reduces intercohort heterogeneity and leads to no significant differences with randomized controls in our case study reassessing a clinical trial.</p> Conclusions <p>We provide a comprehensive benchmark of data-driven prediction architectures for neurological recovery after SCI. Apart from offering individual patients a specific recovery prediction, these models constitute the basis for synthetic controls. Using real-world data from a completed trial in SCI, we show that synthetic controls could mitigate the challenges of small cohorts and patient recruitment in rare disorders, offering the opportunity to maximize the number of patients receiving an investigative treatment.</p> GitHub repository <p><a href="https://gitlab.ethz.ch/BMDSlab/publications/sci/sci-in-silico-trials">https://gitlab.ethz.ch/BMDSlab/publications/sci/sci-in-silico-trials</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月24日

查看全文
  • Louis P. Lukas,
  • Samuel Håkansson,
  • Miklovana Tuci,
  • Abel Torres-Espín,
  • Rüdiger Rupp,
  • Olga Taran,
  • Norbert Weidner,
  • Fred Geisler,
  • Martin Schubert,
  • Frank Röhrich,
  • Yorck B. Kalke,
  • Rainer Abel,
  • Doris Maier,
  • Harvinder S. Chhabra,
  • Thomas Liebscher,
  • John L. K. Kramer,
  • Marc Bolliger,
  • Armin Curt,
  • Catherine R. Jutzeler,
  • Sarah C. Brüningk

摘要

Background

Successfully completing clinical trials for rare and heterogeneous disorders, like spinal cord injuries (SCI), remains challenging, thereby reducing the ability to test and translate promising preclinical findings. We propose synthetic controls, derived from data-driven predictions of recovery in patients undergoing standard treatments, to mitigate these challenges, in particular related to patient recruitment.

Methods

Based on data from the European Multicenter Study about Spinal Cord Injury (EMSCI) and the Sygen trial, we construct synthetic controls from personalized predictions of neurological recovery of sequences of segmental motor scores. A total of six architectures (linear, tree, and deep learning models) are compared. We demonstrate the applicability of synthetic controls through a simulation framework modeling the randomization process in a clinical trial and a case study that re-evaluates the recently completed Nogo Inhibition in SCI (NISCI) trial as a single-arm trial post hoc.

Results

The primary dataset included 4196 patients from EMSCI and 587 patients from the Sygen trial for external validation. We identified a convolutional neural network as the best-performing architecture to predict segmental motor score sequences, achieving a median root mean squared error below the neurological level of injury of 0.55. Our trial simulations demonstrate that synthetic controls are a viable alternative to randomization, as the proposed solution reduces intercohort heterogeneity and leads to no significant differences with randomized controls in our case study reassessing a clinical trial.

Conclusions

We provide a comprehensive benchmark of data-driven prediction architectures for neurological recovery after SCI. Apart from offering individual patients a specific recovery prediction, these models constitute the basis for synthetic controls. Using real-world data from a completed trial in SCI, we show that synthetic controls could mitigate the challenges of small cohorts and patient recruitment in rare disorders, offering the opportunity to maximize the number of patients receiving an investigative treatment.

GitHub repository

https://gitlab.ethz.ch/BMDSlab/publications/sci/sci-in-silico-trials.