Prediction of poor outcome in stroke patients using radiomics analysis of intraparenchymal and intraventricular hemorrhage and clinical factors

Wu, Tzee–Chung; Liu, Yanlin; Chen, Jeon‐Hor; Ho, Chung‐Han; Zhang, Yang; Su, Min–Ying

doi:10.1007/s10072-022-06528-4

Cited by 5 publications

(1 citation statement)

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“…(18) found that the important features for functional outcomes included the GCS, Charlson Comorbidity Index, ICH score, ICH volume, pupillary status, brainstem location, age, anticoagulant/antiplatelet agents, intraventricular hemorrhage, and cerebellar location, and the discriminative ability was high. Wu et al (19) identified 83 patients with ICH and found that the combined model applied radiomic scores obtained from intraparenchymal hemorrhage, intraventricular hemorrhage, and clinical characteristics with high accuracy in predicting poor functional outcomes. However, these studies did not systematically identify the risk factors for poor functional outcomes in patients with ICH, and the characteristics of CMs have not been addressed.…”

Section: Discussionmentioning

confidence: 99%

Risk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factors

Feng,

Wang,

Wang

et al. 2023

Front. Neurol.

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BackgroundThis study aimed to identify the risk factors and construct a prediction model for the prognosis of intracerebral hemorrhage (ICH) at discharge, 3 months, and 12 months.MethodsA total of 269 patients with ICH were retrospectively enrolled at our hospital between January 2014 and August 2016. The prognosis of ICH was assessed using the modified Rankin Scale (mRS); an mRS score > 2 was considered a poor outcome. The primary endpoint was the 3-month mRS, whereas the secondary endpoints included the mRS scores at discharge and 12 months, and mortality.ResultsThe Glasgow Coma Scale (GCS), National Institutes of Health (NIH) stroke scale, International Normalized Ratio (INR), blood urea nitrogen (BUN), epencephalon hemorrhage, and primary hematoma volume were significantly associated with a poor mRS score at 3 months. The predictive value of the prediction model based on these factors for a poor mRS score was 87.8%. Furthermore, a poor mRS score at discharge was affected by the GCS, NIH stroke scale, and primary hematoma volume; the constructed model based on these factors had a predictive value of 87.6%. In addition, the GCS, NIH stroke scale, and surgery were significantly related to a poor mRS score at 12 months; the predictive value of the constructed model based on the aforementioned factors for a poor mRS score was 86.5%. Finally, primary hematoma volume is significantly associated with the risk of 12 months mortality.ConclusionsThe study identified risk factors and constructed a prediction model for poor mRS scores and mortality at discharge, 3 and 12 months in patients with ICH. The prediction models for mRS scores showed a relatively high predictive performance.

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Section: Discussionmentioning

confidence: 99%