Categorization of Ischemic Stroke Patients Compared with National Institutes of Health Stroke Scale

Marendić, Mario; Buličić, Ana Repić; Borovina, Tonka; Miše, Nikolina Ivica; Romac, Rinaldo; Suljić, Enra; Titlić, Marina; Milosevic, Marina

doi:10.5455/medarh.2016.70.119-122

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Our analysis of patients with MCI from general clinical data showed that admission NHISS levels were related to MCI occurrence. Currently, the predictive ability of NIHSS values for MCI is widely recognized in clinical practice 15 . A related study 16 reported that for cerebral infarction caused by internal carotid artery system occlusion, the positive predictive value of NIHSS > 9 within 3 h after onset reached 86.4%, and the positive predictive value of NIHSS of 3-7 within 6 h reached 84.4%.…”

Section: Discussionmentioning

confidence: 99%

New Insight in Massive Cerebral Infarction Predictions after Anterior Circulation Occlusion

Chen

Zou

Zhang

et al. 2023

Preprint

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To predict massive cerebral infarction (MCI) occurrence after anterior circulation occlusion (ACO) by cASPECTS-CTA-CS (combined ASPECTS and CTA-CS). Of 112 cerebral infarction patients with the ACO, their collateral circulation scores from CT angiography (CTA) images in two groups (MCI and non-MCI) were evaluated using Alberta Stroke Program Early CT Score (ASPECTS) and CT angiography collateral score (CTA-CS) approaches. Receiver-operating characteristic curve (ROC), clinical impact curve (CIC), and decision curve analysis (DCA) strategies were used to assess the clinical practicality and predictability of both approaches (cASPECTS-CTA-CS and CTA-CS). Using net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses, discrimination levels of the cASPECTS-CTA-CS were compared with CTA-CS. Classification and regression tree (CART) analyses was conducted to identify the best predictive values and identify subgroup of MCI. The discrimination ability of collateral circulation evaluation score using the cASPECTS-CTA-CS [AUC: 0.939, 95% confidence interval (CI): 0.888–0.990, P < 0.01; NRI: 0.429, 95% CI: 0.069–0.789, P < 0 .01; and IDI: 0.118, 95% CI: 0.035–0.202, P < 0 .01] was better than CTA-CS alone (AUC: 0.910, 95% CI: 0.854–0.965, P < 0 .01). DCA indicated the net benefits of the cASPECTS-CTA-CS approach was higher than CTA-CS alone when the threshold probability range over 20%. CIC analyses showed that the number of high risks and true positives were in agreement when the threshold probability > 30%. Less than 25 of cASPECTS-CTA-CS by CART was important factor in determining MCI occurrence, and ASPECTS < 7 was followed factor. The cASPECTS-CTA-CS approach cumulatively predicted MCI after ACO.

show abstract

Section: Discussionmentioning

confidence: 99%

New Insight in Massive Cerebral Infarction Predictions after Anterior Circulation Occlusion

Chen

Zou

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

New insight in massive cerebral infarction predictions after anterior circulation occlusion

Chen,

Zou,

Zhang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

To predict massive cerebral infarction (MCI) occurrence after anterior circulation occlusion (ACO) by cASPECTS-CTA-CS (combined ASPECTS and CTA-CS). Of 185 cerebral infarction patients with the ACO, their collateral circulation scores from CT angiography (CTA) images in two groups (MCI and non-MCI) were evaluated using Alberta Stroke Program Early CT Score (ASPECTS) and CT angiography collateral score (CTA-CS) approaches. The cASPECTS-CTA-CS was validated internally using the bootstrap sampling method with 1000 bootstrap repetitions and compared to CTA-CS. Receiver-operating characteristic curve (ROC), clinical impact curve (CIC), and decision curve analysis (DCA) strategies were used to assess the clinical practicality and predictability of both approaches (cASPECTS-CTA-CS and CTA-CS). Using net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses, discrimination levels of the cASPECTS-CTA-CS were compared with CTA-CS. Classification and regression tree (CART) analyses was conducted to identify the best predictive values and identify subgroup of MCI. The discrimination ability of collateral circulation evaluation score using the cASPECTS-CTA-CS [AUC: 0.918, 95% confidence interval (CI): 0.869–0.967, P < 0.01; NRI: 0.200, 95% CI: −0.104 to 0.505, P = 0.197; and IDI: 0.107, 95% CI: 0.035–0.178, P = 0.004] was better than CTA-CS alone (AUC: 0.885, 95% CI: 0.833–0.937, P < 0.01). DCA indicated the net benefits of the cASPECTS-CTA-CS approach was higher than CTA-CS alone when the threshold probability range over 20%. CIC analyses showed that the number of high risks and true positives were in agreement when the threshold probability > 80%. Less than 23 of cASPECTS-CTA-CS by CART was important factor in determining MCI occurrence, and ASPECTS < 7 was followed factor. The cASPECTS-CTA-CS approach cumulatively predicted MCI after ACO.

show abstract

Categorization of Ischemic Stroke Patients Compared with National Institutes of Health Stroke Scale

Cited by 2 publications

References 14 publications

New Insight in Massive Cerebral Infarction Predictions after Anterior Circulation Occlusion

New Insight in Massive Cerebral Infarction Predictions after Anterior Circulation Occlusion

New insight in massive cerebral infarction predictions after anterior circulation occlusion

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