Prediction of stroke severity: systematic evaluation of lesion representations
Year: 2024
Type: article
Abstract: Objective To systematically evaluate which lesion‐based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets. Methods We utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N1 = 109, N2 = 638, N3 = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes. Results We found that prediction models trained on small single‐center datasets could perform well using within‐dataset cross‐validation, but results did not generalize to independent datasets (median R2N1 = 0.2%). Performance across independent datasets improved using large single‐center training data (R2N2 = 15.8%) and improved further using multicenter training data (R2N3 = 24.4%). These results were consistent across lesion attributes and prediction models. Including either structural or functional disconnection in the models outperformed prediction based on volume or location alone (P < 0.001, FDR‐corrected). Interpretation We conclude that (1) prediction performance in independent datasets of patients with acute stroke cannot be inferred from cross‐validated results within a dataset, as performance results obtained via these two methods differed consistently, (2) prediction performance can be improved by training on large and, importantly, multicenter datasets, and (3) structural and functional disconnection allow for improved prediction of acute stroke severity.
DOI: 10.1002/acn3.52215
Authors: Anna K. Bonkhoff, Alexander L. Cohen, William Drew, Michael A. Ferguson, Aaliya Hussain, Christopher Lin, Frederic L. W. V. J. Schaper, Anthony Bourached, Anne‐Katrin Giese, Lara C. Oliveira, Robert W. Regenhardt, Markus D. Schirmer, Christina Jern, Arne G. Lindgren, Jane Maguire, Ona Wu, Sahar Zafar, John Y. Rhee, Eyal Y. Kimchi, Maurizio Corbetta, Natalia S. Rost, Michael D. Fox
Institutions: Harvard University, Massachusetts General Hospital, Brigham and Women's Hospital, Boston Children's Hospital, Massachusetts Institute of Technology, UCL Queen Square Institute of Neurology, University College London, University Medical Center Hamburg-Eppendorf, Sahlgrenska University Hospital, Region Västra Götaland, University of Gothenburg, Lund University, Skåne University Hospital, University of Technology Sydney, Athinoula A. Martinos Center for Biomedical Imaging, Center for Neuro-Oncology, Dana-Farber Cancer Institute, Dana-Farber/Harvard Cancer Center, Northwestern University, University of Padua, Veneto Institute of Molecular Medicine
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,990
Awards: G101103092, G1636018437, G1778125652, G1866989815, G2446786914, G3197309817, MRI-GENetics Interface Exploration_MRI-GENIE_Study, G4158491720, Transdiagnostic memory, mood and motor circuits in Alzheimer's and neurodegenerative disease, G6912864445, G705991075, G9000026496, G9000029698, G9000060843, G9000085797, G9000106198, G9000250370, G9000315314
OpenAlex ID: W4403343987