Dataset Information

Risk Factors for Acute Ischemic Stroke Caused by Anterior Large Vessel Occlusion.

ABSTRACT: Background and Purpose- Accurate prediction of acute ischemic stroke (AIS) caused by anterior large vessel occlusion (LVO) that is amendable to mechanical thrombectomy remains a challenge. We developed and validated a prediction model for anterior circulation LVO stroke using past medical history elements present on admission and neurological examination. Methods- We retrospectively reviewed AIS patients admitted between 2009 and 2017 to 3 hospitals within a large healthcare system in the United States. Patients with occlusions of the internal carotid artery or M1 or M2 segments of the middle cerebral artery were randomly split into 2/3 derivation and 1/3 validation cohorts for development of an anterior circulation LVO prediction model and score that was further curtailed for potential use in the prehospital setting. Results- A total of 1654 AIS were reviewed, including 248 (15%) with proximal anterior circulation LVO AIS. In the derivation cohort, National Institutes of Health Stroke Scale score at the time of cerebrovascular imaging, current smoking status, type 2 diabetes mellitus, extracranial carotid, and intracranial atherosclerotic stenosis was significantly associated with anterior circulation LVO stroke. The prehospital score was curtailed to National Institutes of Health Stroke Scale score, current smoking status, and type 2 diabetes mellitus. The areas under the curve for the prediction model, prehospital score, and National Institutes of Health Stroke Scale score alone were 0.796, 0.757, and 0.725 for the derivation cohort and 0.770, 0.689, and 0.665 for the validation cohort, respectively. The Youden index J was 0.46 for a score of >6 with 84.7% sensitivity and 62.0% specificity for the prediction model. Conclusions- Previously reported LVO stroke prediction scores focus solely on elements of the neurological examination. In addition to stroke severity, smoking, diabetes mellitus, extracranial carotid, and intracranial atherosclerotic stenosis were associated with anterior circulation LVO AIS. Although atherosclerotic stenosis may not be known until imaging is obtained, smoking and diabetes mellitus history can be readily obtained in the field and represent important elements of the prehospital score supplementing National Institutes of Health Stroke Scale score.

SUBMITTER: Hendrix P

PROVIDER: S-EPMC6509557 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Risk Factors for Acute Ischemic Stroke Caused by Anterior Large Vessel Occlusion.

Hendrix Philipp P Sofoluke Nelson N Adams Matthew D MD Kunaprayoon Saran S Zand Ramin R Kolinovsky Amy N AN Person Thomas N TN Gupta Mudit M Goren Oded O Schirmer Clemens M CM Rost Natalia S NS Faber James E JE Griessenauer Christoph J CJ

Stroke 20190501 5

Background and Purpose- Accurate prediction of acute ischemic stroke (AIS) caused by anterior large vessel occlusion (LVO) that is amendable to mechanical thrombectomy remains a challenge. We developed and validated a prediction model for anterior circulation LVO stroke using past medical history elements present on admission and neurological examination. Methods- We retrospectively reviewed AIS patients admitted between 2009 and 2017 to 3 hospitals within a large healthcare system in the United ...[more]

PMID: 31009355

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Project description:BackgroundNeuroinflammation plays an important role in the pathogenesis of acute ischemic stroke (AIS) and peripheral leukocyte counts have proved to be independent predictors of stroke severity and outcomes. Clinical significance of large vessel occlusion (LVO) in AIS is increasing, as these patients are potential candidates for endovascular thrombectomy and likely to have worse outcomes if not treated urgently. The aim of our study was to assess the relationship between on admission leukocyte counts and the presence of LVO in the early phase of AIS.MethodsWe have conducted a cross-sectional, observational study based on a registry of consecutive AIS patients admitted up to 4.5 h after stroke onset. Blood samples were taken at admission and leukocyte counts were measured immediately. The presence of LVO was verified based on the computed tomography angiography scan on admission.ResultsTotal white blood cell (WBC) and neutrophil counts were significantly higher in patients with LVO than those without LVO (P < 0.001 respectively). After adjustment for potential confounders total WBC counts (adjusted OR: 1.405 per 1 × 109/L increase, 95% CI: 1.209 to 1.632) and neutrophil counts (adjusted OR: 1.344 per 1 × 109/L increase, 95% CI: 1.155 to 1.564) were found to have the strongest associations with the presence of LVO. Total WBC and neutrophil counts had moderate ability to discriminate an LVO in AIS (AUC: 0.667 and 0.655 respectively). No differences were recorded in leukocyte counts according to the size of the occluded vessel and the status of collateral circulation in the anterior vascular territory. However, total WBC and neutrophil counts tended to be higher in patients with LVO in the posterior circulation (p = 0.005 and 0.010 respectively).ConclusionHigher admission total WBC and neutrophil counts are strongly associated with the presence of LVO and has moderate ability to discriminate an LVO in AIS. Detailed evaluation of stroke-evoked inflammatory mechanisms and changes according to the presence of LVO demands further investigation.

Project description:Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission. Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked. Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57-0.74) for LR, 0.66 (95% CI 0.57-0.74) for RLR, 0.55 (95% CI 0.45-0.64) for RF and 0.67 (95% CI 0.58-0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts. Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.

Project description:Background and purposeAs "time is brain" in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics.MethodsWe automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: "Radiomics", "Radiomics + Treatment" (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), "Clinical + Treatment" (baseline clinical variables and treatment), and "Combined" (radiomics, treatment, and baseline clinical variables).ResultsFor discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction.ConclusionMachine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool.

Project description:BACKGROUND:Acute ischemic stroke (AIS) due to large vessel occlusion (LVO) is a devastating cerebrovascular disorder, which could benefit from collateral circulation. Proteins associated with acute LVO pathogenesis and endothelial function may appear in blood samples of AIS patients due to LVO, thus permitting development of blood-based biomarkers for its diagnosis and prognosis. METHODS:This study is a single-center, retrospective, observational case-control trial. Consecutive patients who presented at the Department of Neurology of Tongji Hospital were recruited from July 2016 to April 2018. In the discovery phase, a proteomic approach with iTRAQ-based LC-MS/MS was used to investigate the altered proteomic pattern in plasma from patients with AIS due to LVO. In the validation study, Western blots was used to identify biomarkers associated with stroke diagnosis as well as their prognostic value associated with different collateral statuses. RESULTS:For this exploratory study, the proteomic analysis of plasma from 40 patients with AIS due to LVO and 20 healthy controls revealed seven differentially expressed proteins with a 1.2/0.83-fold or greater difference between groups. The four elevated proteins, PPBP (1.58 ± 0.78 vs 0.98 ± 0.37; P < 0.001), THBS1 (1.13 ± 0.88 vs 0.43 ± 0.26; P < 0.001), LYVE1 (1.61 ± 0.55 vs 0.97 ± 0.50; P < 0.001), and IGF2 (1.19 ± 0.42 vs 0.86 ± 0.24; P < 0.001), were verified by Western blots analysis in an independent cohort including 33 patients and 33 controls. A strong interaction was observed between the four-protein panel and the diagnosis of AIS due to LVO (AUC 0.947; P < 0.001). Furthermore, IGF2, LYVE1, and THBS1 were closely associated with collateral status (IGF2 0.115, 95% CI 0.016-0.841, P = 0.033; LYVE1 0.183, 95% CI 0.036-0.918, P = 0.039; THBS1 4.257, 95% CI 1.273-14.228, P = 0.019), and proved to be independent predictors of good outcome (IGF2 0.115, 95% CI 0.015-0.866, P = 0.036; LYVE1 0.028, 95% CI 0.002-0.334, P = 0.005; THBS1 3.294, 95% CI 1.158-9.372, P = 0.025) at a 3-month follow-up. CONCLUSIONS:The identified 4-biomarker panel could provide diagnostic aid to the existing imaging modalities for AIS due to LVO, and the prognostic value of IGF2, LYVE1, and THBS1 was proved in predicting functional outcomes related to collateral status. Trial registration ClinicalTrials.gov NCT03122002. Retrospectively registered April 20, 2017. URL of trial registry record: https://www.clinicaltrials.gov/ct2/show/NCT03122002?term=NCT+03122002&rank=1.

Project description:Background and purposeCollateral blood supply is a key determinant of outcome in large-vessel occlusion acute ischemic stroke. Single- and multiphase CTA collateral scoring systems have been described but are subjective and require training. We aimed to test whether the CTP-derived hypoperfusion intensity ratio is associated with CTA collateral status and whether a threshold hypoperfusion intensity ratio exists that predicts poor CTA collaterals.Materials and methodsImaging and clinical data of consecutive patients with large-vessel occlusion acute ischemic stroke were retrospectively reviewed. Single-phase CTA and multiphase CTA scoring were performed by 2 blinded neuroradiologists using the Tan, Maas, and Calgary/Menon methods. CTP was processed using RApid processing of PerfusIon and Diffusion software (RAPID). Hypoperfusion intensity ratio = ratio of brain volume with time-to-maximum >10 seconds over time-to-maximum >6-second volume. Correlation between the hypoperfusion intensity ratio and CTA collateral scores was calculated using the Pearson correlation. The optimal threshold of the hypoperfusion intensity ratio for predicting poor collaterals was determined using receiver operating characteristic curve analysis.ResultsFifty-two patients with large-vessel occlusion acute ischemic stroke were included. Multiphase CTA collateral scoring showed better interrater agreement (κ = 0.813) than single-phase CTA (Tan, κ = 0.587; Maas, κ = 0.273). The hypoperfusion intensity ratio correlated with CTA collateral scores (multiphase CTA: r = -0.55; 95% CI, -0.67 to -0.40; P ≤ .001). The optimal threshold for predicting poor multiphase CTA collateral status was a hypoperfusion intensity ratio of >0.45 (sensitivity = 78%; specificity = 76%; area under the curve = 0.86). Patients with high hypoperfusion intensity ratio/poor collateral status had lower ASPECTS/larger infarcts, higher NIHSS scores, and larger hypoperfused volumes.ConclusionsThe hypoperfusion intensity ratio is associated with CTA collateral status in patients with large-vessel occlusion acute ischemic stroke. The hypoperfusion intensity ratio is an automated and quantitative alternative to CTA collateral scoring methods for both clinical and future stroke trial settings.

Dataset Information

Risk Factors for Acute Ischemic Stroke Caused by Anterior Large Vessel Occlusion.

Publications

Risk Factors for Acute Ischemic Stroke Caused by Anterior Large Vessel Occlusion.

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