Dataset Information

Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization.

ABSTRACT: Benefit of endovascular recanalization beyond established treatment time windows likely exists in select stroke patients. However, there is currently no imaging model that predicts infarction adjusting for elapsed time between the pathologic snapshot of admission imaging until endovascular recanalization. We trained and cross validated a multivariate generalized linear model (GLM) that uses computer tomography perfusion and clinical data to quantify patient-specific dynamic change of tissue infarction depending on degree and time of recanalization. Multicenter data of 161 patients with proximal anterior circulation occlusion undergoing endovascular therapy were included. Multivariate voxelwise infarct probability was calculated within the GLM. The effect of increasing time to treatment and degree of recanalization on voxelwise infarction was calculated in each patient. Tissue benefit of successful relative to unsuccessful recanalization was shown up to 15?hours after onset in individual patients and decreased nonlinearly with time. On average, the relative reduction of infarct volume at the treatment interval of 5?hours was 53% and this salvage effect decreased by 5% units per hour to <5% after 10 additional hours to treatment. Treatment time-adjusted multivariate prediction of infarction by perfusion and clinical status may identify patients who benefit from extended time to recanalization therapy.

SUBMITTER: Kemmling A

PROVIDER: S-EPMC4640330 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization.

Kemmling André A Flottmann Fabian F Forkert Nils Daniel ND Minnerup Jens J Heindel Walter W Thomalla Goetz G Eckert Bernd B Knauth Michael M Psychogios Marios M Langner Soenke S Fiehler Jens J

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20150708 9

Benefit of endovascular recanalization beyond established treatment time windows likely exists in select stroke patients. However, there is currently no imaging model that predicts infarction adjusting for elapsed time between the pathologic snapshot of admission imaging until endovascular recanalization. We trained and cross validated a multivariate generalized linear model (GLM) that uses computer tomography perfusion and clinical data to quantify patient-specific dynamic change of tissue infa ...[more]

PMID: 26154867

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Project description:Background and objectivesVery poor outcome despite intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in about 1 of 4 patients with ischemic stroke and is associated with a high logistic and economic burden. We aimed to develop and validate a multivariable prognostic model to identify futile recanalization therapies (FRT) in patients undergoing those therapies.Materials and methodsPatients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The dataset was split into a training (N=1808, 80%) and internal validation (N=453, 20%) cohort. We used gradient boosted decision tree machine-learning models after k-NN imputation of 32 variables available at admission to predict FRT defined as modified Rankin-Scale (mRS) 5-6 at 3 months. We report feature importance, ability for discrimination, calibration and decision curve analysis.Results2261 patients with a median (IQR) age 75 years (64-83), 46% female, median NIHSS 9 (4-17), 34% IVT alone, 41% MT alone, 25% bridging were included. Overall 539 (24%) had FRT, more often in MT alone (34%) as compared to IVT alone (11%). Feature importance identified clinical variables (stroke severity, age, active cancer, prestroke disability), laboratory values (glucose, CRP, creatinine), imaging biomarkers (white matter hyperintensities) and onset-to-admission time as the most important predictors. The final model was discriminatory for predicting 3-month FRT (AUC 0.87, 95% CI 0.87-0.88) and had good calibration (Brier 0.12, 0.11-0.12). Overall performance was moderate (F1-score 0.63 ± 0.004) and decision curve analyses suggested higher mean net benefit at lower thresholds of treatment (up to 0.8).ConclusionsThis FRT prediction model can help inform shared decision making and identify the most relevant features in the emergency setting. While it might be particularly useful in low resource healthcare settings, incorporation of further multifaceted variables is necessary to further increase the predictive performance.

Dataset Information

Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization.

Publications

Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization.

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