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Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke.

ABSTRACT: Background and Purpose- The ability to model long-term functional outcomes after acute ischemic stroke represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club organization, a high capacity backbone system of brain function. Methods- In a hospital-based cohort of 41 acute ischemic stroke patients, we investigated the effect of acute infarcts on the brain's prestroke rich club backbone and poststroke functional connectomes with respect to poststroke outcome. Functional connectomes were created using 3 anatomic atlases, and characteristic path-length (L) was calculated for each connectome. The number of rich club regions affected were manually determined using each patient's diffusion weighted image. We investigated differences in L with respect to outcome (modified Rankin Scale score; 90 days) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including number of rich club regions and L in outcome models, using linear regression and assessing the explained variance (R2). Results- Of 41 patients (mean age [range]: 70 [45-89] years), 61% were male. Lower L was generally associated with better outcome. Including number of rich club regions in the backward selection models of outcome, R2 increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and modified Rankin Scale score. Conclusions- In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of poststroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

SUBMITTER: Ktena SI

PROVIDER: S-EPMC6756947 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke.

Ktena Sofia Ira SI Schirmer Markus D MD Etherton Mark R MR Giese Anne-Katrin AK Tuozzo Carissa C Mills Brittany B BB Rueckert Daniel D Wu Ona O Rost Natalia S NS

Stroke 20190912 10

Background and Purpose- The ability to model long-term functional outcomes after acute ischemic stroke represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club organizat ...[more]

PMID: 31510905

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Project description:BackgroundLow-to-moderate dose statins (LMDSs) are more commonly used among Asian acute ischemic stroke (AIS) patients in clinical practice. However, the correlation between the LMDS use and prognosis has not been evaluated in AIS patients with conventional medication treatment alone. This study aimed to investigate the influence of LMDS on the prognosis of AIS patients and how prognosis and potential prognostic factors interact with different statin doses.MethodsThis retrospective cohort study included AIS patients who were admitted within 7 days after symptom onset and received conventional medication treatment alone from November 2019 to November 2020 in the Neurology, Department of West China Hospital, Sichuan University. From a total of 782 initial patients, a final cohort of 327 patients was included in the study. These patients were divided into three groups based on statin doses: non-statin (48 patients), LMDS (152 patients), and high-dose statin (HDS) (127 patients). The follow-up period was 3 months after the onset of stroke and the primary outcome was defined as a modified Rankin scale (mRS) score of 0 to 2 at 3 months, secondary outcomes were hemorrhagic transformation (HT) and death within 3 months. Stratified analysis was also conducted to test the robustness of the relationship between the use of different statin doses and functional outcomes in various subgroups.ResultsCompared with non-statin therapy, both LMDS therapy and HDS therapy were associated with good functional outcomes [odds ratio (OR) =3.68, 95% confidence interval (CI): 1.13-12.01, P=0.0309; OR =3.45, 95% CI: 1.06-11.26, P=0.0402, respectively] and a lower risk of HT (OR =0.30, 95% CI: 0.11-0.86, P=0.0253; OR =0.36, 95% CI: 0.13-0.99, P=0.0488, respectively). However, there was no significant difference in all-cause death within 3 months among the three groups (OR =0.84, 95% CI: 0.29-2.46, P=0.7468; OR =0.76, 95% CI: 0.26-2.22, P=0.6104). Additionally, no significant differences were observed between LMDS therapy and HDS therapy regarding good functional outcomes at 3 months (OR =0.94, 95% CI: 0.50-1.77, P=0.8411) and the occurrence of HT (OR =1.19, 95% CI: 0.47-3.02, P=0.7093). The results of the relationship between different statin doses and 3-month good functional outcome were consistent after interaction tests.ConclusionsOur findings provide evidence for the benefit and safety of LMDS therapy in AIS patients with medication treatment alone. LMDS therapy is associated with favorable impacts on 3-month functional outcomes and a reduced risk of HT compared to non-statin therapy. There were no significant differences in achieving 3-month good functional outcome, the risk of HT or death within 3 months were observed between LMDS and HDS therapy in our study. Further studies with prospective design and larger sample sizes are necessary to validate our results.

Project description:BackgroundMixed data exist regarding the association between hyperglycemia and functional outcome after acute ischemic stroke when accounting for the impact of leptomeningeal collateral flow. We sought to determine whether collateral status modifies the association between treatment group and functional outcome in a subset of patients with large vessel occlusion enrolled in the Stroke Hyperglycemia Insulin Network Effort (SHINE) trial.MethodsIn this post-hoc analysis, we analyzed patients enrolled into the SHINE trial with anterior circulation large vessel occlusion who underwent imaging with CT angiography prior to glucose control treatment group assignment. The primary analysis assessed the degree to which collateral status modified the effect between treatment group and functional outcome as defined by the 90-day modified Rankin Scale score. Logistic regression was used to model the data, with adjustments made for thrombectomy status, age, post-perfusion thrombolysis in cerebral infarction (TICI) score, tissue plasminogen activator (tPA) use, and baseline National Institutes of Health Stroke Scale (NIHSS) score. Five SHINE trial centers contributed data for this analysis. Statistical significance was defined as a p-value < 0.05.ResultsAmong the 1151 patients in the SHINE trial, 57 with angiographic data were included in this sub-analysis, of whom 19 had poor collaterals and 38 had good collaterals. While collateral status had no effect (p = 0.855) on the association between glucose control treatment group and functional outcome, patients with good collaterals were more likely to have a favorable functional outcome (p = 0.001, OR 5.02; 95% CI 1.37-16.0).ConclusionsIn a post-hoc analysis using a subset of patients with angiographic data enrolled in the SHINE trial, collateral status did not modify the association between glucose control treatment group and functional outcome. However, consistent with prior studies, there was a significant association between good collateral status and favorable outcome in patients with large vessel occlusion stroke.Trial registrationClinicalTrials.gov Identifier is NCT01369069. Registration date is June 8, 2011.

Project description:Currently, few studies are reported on the composition of microbiota in stroke patients and the association with stroke prognosis. This study investigated the differing microbiota composition in stroke patients and confirmed the association of microbiota composition with poor functional outcome. Between January of 2018 and December of 2019, 198 patients with acute cerebral infarction were included in this study. For the case-control study, age and sex-matched normal healthy subjects (n = 200) were included when receiving their health screening examinations. We isolated bacterial extracellular membrane vesicles and extracted DNA from blood samples. Taxonomic assignments were performed by using the sequence reads of 16S rRNA genes following blood microbiota analysis. Statistical analysis was conducted appropriately by using Statistical Analysis System software. The mean age of the stroke patients were 63.7 ± 12.5 years, and the male sex was 58.5%. Of the total enrolled patients, poor functional outcome (modified Rankin Score ≥ 3) was noted in 19.7%. The principal component analysis of microbiota composition revealed significant differences between healthy control subjects and stroke patients. At the genus level, Aerococcaceae(f), ZB2(c), TM7-1(c), and Flavobacterium were significantly increased in stroke patients compared to the healthy controls, whereas Mucispirillum, rc4-4, Akkermansia, Clostridiales(o), Lactobacillus, and Stenotrophomonas were decreased considerably. For the functional outcome after ischemic stroke, Anaerococcus, Blautia, Dialister, Aerococcaceae(f), Propionibacterium, Microbacteriaceae(f), and Rothia were enriched in the group with good outcomes, whereas Ruminococcaceae(f) and Prevotella were enriched in the group with poor outcome. There was apparent dysbiosis of blood microbiota in patients with acute ischemic stroke compared to healthy people. Ruminococcaceae(f) and Prevotella were elevated in stroke patients with poor functional outcome.

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Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke.

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Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke.

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