The challenge of long-term stroke outcome prediction and how statistical correlates do not imply predictive value

Personalized prediction of stroke outcome using lesion imaging markers is still too imprecise to make a breakthrough in clinical practice. We performed a combined prediction and brain mapping study on topographic and connectomic lesion imaging data to evaluate (i) the relationship between lesion-deficit associations and their predictive value and (ii) the influence of time since stroke. In patients with first-ever ischaemic stroke, we first applied high-dimensional machine learning models on lesion topographies or structural disconnection data to model stroke severity (National Institutes of Health Stroke Scale 24 h/3 months) and functional outcome (modified Rankin Scale 3 months) in cross-validation. Second, we mapped the topographic and connectomic lesion impact on both clinical measures. We retrospectively included 685 patients [age 67.4 ± 15.1, National Institutes of Health Stroke Scale 24 h median(IQR) = 3(1; 6), modified Rankin Scale 3 months = 1(0; 2), National Institutes of Health Stroke Scale 3 months = 0(0; 2)]. <i>Predictions</i> for acute stroke severity (National Institutes of Health Stroke Scale 24 h) were better with topographic lesion imaging (<i>R</i>² = 0.41) than with disconnection data (<i>R</i>² = 0.29, <i>P</i> = 0.0015), whereas predictions at 3 months (National Institutes of Health Stroke Scale/modified Rankin Scale) were generally close to chance level. In the analysis of lesion-deficit <i>associations</i>, the correlates of more severe acute stroke (National Institutes of Health Stroke Scale 24 h > 4) and poor functional outcome (modified Rankin Scale 3 months ≥ 2) were left-lateralized. The lesion location impact of both variables corresponded in right-hemisphere stroke with peaks in primary motor regions, but it markedly differed in left-hemisphere stroke. Topographic and disconnection lesion features predict <i>acute</i> stroke severity better than the <i>3-months</i> outcome. This suggests a likely higher impact of lesion-independent factors in the longer term and highlights challenges in the prediction of global functional outcome. Prediction and brain mapping diverge, and the existence of statistically significant associations-as here for 3-months outcomes-does not imply predictive value. Routine neurological scores better capture left- than right-hemispheric lesions, further complicating the challenge of outcome prediction.

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Publication Details

Journal
Brain Communications
Published
2024-12-24
DOI
https://doi.org/10.1093/braincomms/fcaf003
Citations
2
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
0.77

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article

The challenge of long-term stroke outcome prediction and how statistical correlates do not imply predictive value

Roza M. Umarova, Marcel Arnold, Christoph Sperber, Laura Gallucci
2 citations
Brain Communications
Acute Ischemic Stroke Management
0.77
article

The challenge of long-term stroke outcome prediction and how statistical correlates do not imply predictive value

Roza M. Umarova, Marcel Arnold, Christoph Sperber, Laura Gallucci
article en
2 citations

Abstract

Personalized prediction of stroke outcome using lesion imaging markers is still too imprecise to make a breakthrough in clinical practice. We performed a combined prediction and brain mapping study on topographic and connectomic lesion imaging data to evaluate (i) the relationship between lesion-deficit associations and their predictive value and (ii) the influence of time since stroke. In patients with first-ever ischaemic stroke, we first applied high-dimensional machine learning models on lesion topographies or structural disconnection data to model stroke severity (National Institutes of Health Stroke Scale 24 h/3 months) and functional outcome (modified Rankin Scale 3 months) in cross-validation. Second, we mapped the topographic and connectomic lesion impact on both clinical measures. We retrospectively included 685 patients [age 67.4 ± 15.1, National Institutes of Health Stroke Scale 24 h median(IQR) = 3(1; 6), modified Rankin Scale 3 months = 1(0; 2), National Institutes of Health Stroke Scale 3 months = 0(0; 2)]. <i>Predictions</i> for acute stroke severity (National Institutes of Health Stroke Scale 24 h) were better with topographic lesion imaging (<i>R</i>² = 0.41) than with disconnection data (<i>R</i>² = 0.29, <i>P</i> = 0.0015), whereas predictions at 3 months (National Institutes of Health Stroke Scale/modified Rankin Scale) were generally close to chance level. In the analysis of lesion-deficit <i>associations</i>, the correlates of more severe acute stroke (National Institutes of Health Stroke Scale 24 h > 4) and poor functional outcome (modified Rankin Scale 3 months ≥ 2) were left-lateralized. The lesion location impact of both variables corresponded in right-hemisphere stroke with peaks in primary motor regions, but it markedly differed in left-hemisphere stroke. Topographic and disconnection lesion features predict <i>acute</i> stroke severity better than the <i>3-months</i> outcome. This suggests a likely higher impact of lesion-independent factors in the longer term and highlights challenges in the prediction of global functional outcome. Prediction and brain mapping diverge, and the existence of statistically significant associations-as here for 3-months outcomes-does not imply predictive value. Routine neurological scores better capture left- than right-hemispheric lesions, further complicating the challenge of outcome prediction.

Brain CommunicationsVol. 7(1)
University of Bern (CH), University Hospital of Bern (CH)
Heidi Seiler-Stiftung
No poverty
Openalex Percentile: Top 23%
Acute Ischemic Stroke Management
0.77
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