beyondPRR

This is the official repository for the research paper

Beyond proportional recovery in wake-up stroke: unsupervised recovery clusters based on the NIHSS 

Submitted to Journal of NeuroEngineering and Rehabilitation.

Abstract

Post-stroke rehabilitation is a complex process influenced by several neurophysiological factors. The recovery is traditionally predicted based on initial impairment using linear models. The Proportional Recovery Rule (PRR), developed on the Fugl-Meyer scale, has even been proposed as a therapeutic target. In this framework, patients are classified as "fitters" or "non-fitters", though this distinction depends on the methodology used. Additionally, issues like mathematical coupling and ceiling effects on clinical scales could raise concerns about the validity of these models. To overcome these issues, Repeated Spectral Clustering (RSC) was used to identify recovery patterns based on NIHSS. We selected 201 patients from the WAKE-UP trail, all moderately impaired at onset and still impaired at 22/36 hours. Clustering was performed using a similarity matrix based on pairwise absolute differences between recovery ratios, calculated from 22/36 hours to 90 days post-stroke. Cluster differences were tested with prognostic factors, including lesion volume, side, treatment, and the Heidelberg scale. The PRR was fit to the cohort for comparison with clustering results. The linear fit reproduced findings consistent with the literature, such as a correlation of $\rho(x,\Delta) = .73$ and an average recovery ratio of 70% for the "fitters". RSC grouped patients into six recovery clusters: $C_0$ (full recovery), $C_1$ (above average), $C_2$ and $C_3$ (average, PRR-aligned), $C_4$ (below average), and $C_5$ (deterioration). NIHSS scores in most patients declined non-proportionally. Lesion volume was not significantly different across clusters, while left-sided strokes were higher in low recovery clusters. Patients with a recovery ratio $\geq 0.3$ within two weeks mostly fell into favorable clusters ($C_0$–$C_3$), covering $\approx 90%$ of such cases. The identified clusters provide a refined view of stroke recovery following wake-up stroke. Clustering better captures patient similarities, enabling the assessment of neurophysiological differences between groups and supporting tailored interventions.

Provided code

Scripts used to generate the results presented in the paper are available in this repository. The data are publicly available here clinicaltrials.gov/study/NCT01525290.

Authors and Citation

If you find codes and results useful for your research, please concider citing our work. It would help us to continue our research. At the moment, we are working on a research paper to submit to Nature Human Behaviour.

Contributors:

• M.Sc. Andrea Zanola
• Dr. Antonio Luigi Bisogno
• PhD. Veronika Vadinova
• Dr. Thomalla Götz
• Dr. Cheng Bastian
• Prof. Manfredo Atzori
• Prof. Maurizio Corbetta

License

The code is released under the MIT License.