Predicting post-stroke cognitive impairment using acute CT neuroimaging: A systematic review and meta-analysis.
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ABSTRACT: Background
Identifying whether acute stroke patients are at risk of cognitive decline could improve prognostic discussions and management. Structural computed tomography neuroimaging is routine in acute stroke, and may identify those at risk of post-stroke dementia or post-stroke cognitive impairment (PSCI).Aim
To systematically review the literature to identify which stroke or pre-stroke features on brain computed tomography scans, performed at the time of stroke, are associated with post-stroke dementia or PSCI.Summary of review
We searched electronic databases to December 2020. We included studies reporting acute stroke brain computed tomography, and later diagnosis of a cognitive syndrome. We created summary estimates of size of unadjusted association between computed tomography features and cognition. Of 9536 citations, 28 studies (41 papers) were eligible (N = 7078, mean age 59.8-78.6 years). Cognitive outcomes were post-stroke dementia (10 studies), PSCI (17 studies), and one study analyzed both. Fifteen studies (N = 2952) reported data suitable for meta-analyses. White matter lesions (WML) (six studies, N = 1054, OR = 2.46, 95% CI = 1.25-4.84), cerebral atrophy (four studies, N = 558, OR = 2.80, 95% CI = 1.21-6.51), and pre-existing stroke lesions (three studies, N = 352, OR = 2.38, 95% CI = 1.06-5.32) were associated with post-stroke dementia. WML (four studies, N = 473, OR = 3.46, 95% CI = 2.17-5.52) were associated with PSCI. Other computed tomography features were either not associated with cognitive outcome, or there were insufficient data.Conclusions
Cognitive impairment following stroke is of great concern to patients and carers. Features seen on visual assessment of acute stroke computed tomography brain scans are strongly associated with cognitive outcomes. Clinicians should consider when and how this information should be discussed with stroke survivors.
SUBMITTER: Ball EL
PROVIDER: S-EPMC9260488 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Stroke is a primary debilitating disease in adults, occurring in 15 million individuals each year and causing high mortality and disability rates. The latest estimate revealed that stroke is currently the second leading cause of death worldwide. Post-stroke cognitive impairment (PSCI), one of the major complications after stroke, is frequently underdiagnosed. However, stroke has been reported to increase the risk of cognitive impairment by at least five to eight times. In recent decades, peripheral blood molecular biomarkers for stroke have emerged as diagnostic, prognostic, and therapeutic targets. In this study, we aimed to evaluate some blood-derived proteins for stroke, especially related to brain damage and cognitive impairments, by conducting a systematic review and meta-analysis and discussing the possibility of these proteins as biomarkers for PSCI. Articles published before 26 July 2021 were searched in PubMed, Embase, the Web of Science, and the Cochrane Library to identify all relevant studies reporting blood biomarkers in patients with stroke. Among 1820 articles, 40 were finally identified for this study. We meta-analyzed eight peripheral biomarker candidates: homocysteine (Hcy), high-density lipoprotein cholesterol (HDL-C), C-reactive protein (CRP), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG), uric acid, and glycated hemoglobin (HbA1c). The Hcy, CRP, TC, and LDL-C levels were significantly higher in patients with PSCI than in the non-PSCI group; however, the HDL-C, TG, uric acid, and HbA1c levels were not different between the two groups. Based on our findings, we suggest the Hcy, CRP, TC, and LDL-C as possible biomarkers in patients with post-stroke cognitive impairment. Thus, certain blood proteins could be suggested as effective biomarkers for PSCI.
| S-EPMC8775398 | biostudies-literature
Project description:ImportanceSecondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.ObjectiveTo develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.DesignCohort study with patients enrolled between 2003-2016 with follow-up through 2022.SettingPrimary care practices affiliated with two academic medical centers.ParticipantsIndividuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).ExposuresPredictors of PSCI were ascertained from the electronic health record.Main outcomeThe outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.ResultsThe analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.Conclusions and relevanceFive-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.
| S-EPMC10863024 | biostudies-literature
Project description:Background: Post-stroke cognitive impairment (PSCI) affects more than one-third of stroke patients, and causes much greater harm to long-term function than the initial brain damage. No conventional Western medications have shown convincing clinical effectiveness for treating PSCI. Research shows that Traditional Chinese medicine (TCM) can improve cognitive function in patients. However, the clinical efficacy and safety remain controversial. The aim of this study was to examine the effectiveness and harmful effects of TCMs in the treatment of PSCI. Method: We searched seven databases and two clinical registration websites for randomized controlled trials (RCTs). The revised Cochrane risk of bias tool (RoB 2.0) was used to evaluate the methodological quality and RevMan 5.4 was used for data analysis. This study has been submitted to PROSPERO with registration number is CRD42020149299. Results: We included 34 studies in this review. The results of this study showed that TCM adjuvant therapy improved scores on the MoCA [MD = 2.55, 95% CI (1.56, 3.53), p < 0.00001; MD = 3.07, 95% CI (1.98, 4.17), p < 0.00001 at treatment duration of <3 and 3 months, respectively], MMSE [MD = 2.55, 95% CI (1.99, 3.10), p < 0.00001; MD = 2.53, 95% CI (1.59, 3.47), p < 0.00001; MD = 2.91, 95% CI (1.26, 4.56), p = 0.0006; MD = 3.11, 95% CI (-0.04, 6.27), p = 0.05 at treatment duration of <3, 3, 4, and 6 months, respectively], and BI [MD = 7.34, 95% CI (3.83, 10.85), p < 0.0001; MD = 8.98, 95% CI (4.76, 13.21), p < 0.0001 at treatment duration of <3 and 3 months, respectively] and reduced scores on the ADL (MD = -8.64, 95% CI (-9.83, -7.45), p < 0.00001; MD = -2.00, 95% CI (-2.94, -1.06), p < 0.0001 at treatment duration of 3 and 4 months, respectively], NIHSS [MD = -2.48, 95% CI (-4.97, 0.00), p = 0.05; MD = -3.81, 95% CI (-6.21, -1.40), p = 0.002 at treatment duration of <3 and 3 months, respectively], and CSS [MD = -2.47, 95% CI (-3.49, -1.45), p < 0.00001 at a treatment duration of 3 months]. No serious adverse reactions were observed. Conclusion: Despite the significant positive results, the present evidence supports, to a limited extent because of the methodological flaws and herbal heterogeneity, that TCM adjuvant therapy can be used for patients with PSCI. While, further rigorous RCTs are warranted to confirm the efficacy and safety of TCM. Systematic Review Registration: https://www.crd.york.ac.uk/prospero, identifier CRD42020149299.
| S-EPMC8883343 | biostudies-literature
Project description:BackgroundA considerable number of stroke survivors suffered from cognitive impairment, and more than one third of stroke survivors are affected at 3 and 12 months after the stroke. Although the published systematic reviews suggest that acupuncture can help improve post-stroke cognitive dysfunction, the power of the results is low due to study limitations. Therefore, this review is necessary to analyze the effect of acupuncture on cognitive impairment after stroke and to provide evidence for cognitive impairment in stroke.MethodsThis study will be carried out in strict accordance with the Cochrane Handbook for Systematic Reviews of Interventions. According to the pre-established search strategy (PICOS framework), all the literature will be obtained from online databases including Cochrane Central Register of Controlled Trials in the Cochrane Library, Medline (via PubMed), EMBASE (via embase.com), CINAHL (via EBSCOhost), China National Knowledge Infrastructure database, WanFang Database, Chinese Science and Technology Periodical Database, and Sino-Med Database from inception until December 31, 2021 with no language limitations. Two reviewers will screen the records and include quality studies according to inclusion criteria independently. The data needed will be extracted independently by 2 authors according to a table of data extraction. Any inconsistencies in literature screening and data collection will be resolved to reach a consensus via discussion with a third author. Risk of bias for each study will be assessed using risk of bias tool. RevMan5.3 will be used to analyze the data. Heterogeneity will be identified and measured by Chi2. Subgroup analyses and sensitivity analysis will be carried out. Grading of Recommendations Assessment, Development and Evaluation will be used to evaluate the evidence for each outcome.ConclusionThe results of this study will provide a theoretical basis for the clinical use of electro-acupuncture to treat cognitive dysfunction after stroke.Unique inplasy numberINPLASY202210038.
| S-EPMC8878786 | biostudies-literature
Project description:Background and objectivesPost-stroke cognitive impairment (PSCI) occurs in up to 50% of patients with acute ischemic stroke (AIS). Thus, the prediction of cognitive outcomes in AIS may be useful for treatment decisions. This PSCI cohort study aimed to determine the applicability of a machine learning approach for predicting PSCI after stroke.MethodsThis retrospective study used a prospective PSCI cohort of patients with AIS. Demographic features, clinical characteristics, and brain imaging variables previously known to be associated with PSCI were included in the analysis. The primary outcome was PSCI at 3-6 months, defined as an adjusted z-score of less than - 2.0 standard deviation in at least one of the four cognitive domains (memory, executive/frontal, visuospatial, and language), using the Korean version of the Vascular Cognitive Impairment Harmonization Standards-Neuropsychological Protocol (VCIHS-NP). We developed four machine learning models (logistic regression, support vector machine, extreme gradient boost, and artificial neural network) and compared their accuracies for outcome variables.ResultsA total of 951 patients (mean age 65.7 ± 11.9; male 61.5%) with AIS were included in this study. The area under the curve for the extreme gradient boost and the artificial neural network was the highest (0.7919 and 0.7365, respectively) among the four models for predicting PSCI according to the VCIHS-NP definition. The most important features for predicting PSCI include the presence of cortical infarcts, mesial temporal lobe atrophy, initial stroke severity, stroke history, and strategic lesion infarcts.ConclusionOur findings indicate that machine-learning algorithms, particularly the extreme gradient boost and the artificial neural network models, can best predict cognitive outcomes after ischemic stroke.
| S-EPMC10468853 | biostudies-literature
Project description:ObjectiveCognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI.MethodsRelevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed.ResultsA total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77-0.87), 0.77 (95% CI 0.72-0.80), and 0.80 (95% CI 0.71-0.86) in the training set, and 0.82 (95% CI 0.77-0.87), 0.82 (95% CI 0.70-0.90), and 0.80 (95% CI 0.68-0.82) in the validation set, respectively.ConclusionML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.
| S-EPMC10434510 | biostudies-literature
Project description:BackgroundFatigue is a common and disabling symptom following stroke, but its underlying mechanisms are unknown. Associations with a number of imaging features have been proposed.AimsWe aimed to assess whether neuroimaging parameters could better inform our understanding of possible causes of post-stroke fatigue (PSF) through systematic review and meta-analysis.MethodsUsing a predefined protocol registered with PROSPERO (ID: CRD42022303168), we searched EMBASE, MEDLINE, PubMed, and PsycInfo for studies assessing PSF and computerized tomography (CT), magnetic resonance (MR), positron emission tomography (PET) imaging, or diffusion tensor imaging (DTI). We extracted neuroimaging parameters and narratively analyzed study results to assess any association with PSF. Where there were 3+ similar studies, we carried out a meta-analysis using inverse-variance random-effects model to estimate the total association of each neuroimaging parameter on PSF. The risk of bias was assessed using the Newcastle and Ottawa Scale.ResultsWe identified 46 studies (N = 6543); in many studies, associations with fatigue were secondary or subanalyses (28.3%). Imaging parameters were assessed across eight variables: lesion lateralization, lesion location, lesion volume, brain atrophy, infarct number, cerebral microbleeds, white matter hyperintensities (WMHs), and network measures. Most variables showed no conclusive evidence for any association with fatigue. Meta-analysis, where possible, showed no association of the following with PSF; left lesion lateralization (OR: 0.88, 95% CI (0.64, 1. 22) (p = 0.45)), infratentorial lesion location (OR: 1.83, 95% CI (0.63, 5.32) (p = 0.27)), and WMH (OR: 1.21, 95% CI (0.84, 1.75) (p = 0.29)). Many studies assessed lesion location with mixed findings; only one used voxel-symptom lesion-mapping (VSLM). Some small studies suggested an association between altered functional brain networks, namely frontal, fronto-striato-thalamic, and sensory processing networks, with PSF.ConclusionThere was little evidence for the association between any neuroimaging parameters and PSF. Future studies should utilize advanced imaging techniques to fully understand the role of lesion location in PSF, while the role of altered brain networks in mediating PSF merits further research.
| S-EPMC10614177 | biostudies-literature
Project description:Background and Aims: Systemic inflammation is associated with an increased risk of cognitive impairment and dementia, but the associations between them in stroke patients are less clear. We examined the impact of systemic inflammation represented as the neutrophil-lymphocyte ratio (NLR) on the development of post-stroke cognitive impairment (PSCI) and domain-specific cognitive outcomes 3-month after ischemic stroke. Methods: Using prospective stroke registry data, we consecutively enrolled 345 participants with ischemic stroke whose cognitive functions were evaluated 3-month after stroke. Their cognition was assessed with the Korean version of the Vascular Cognitive Impairment Harmonization Standards and the Korean-Mini Mental Status Examination. PSCI was defined as a z-score of < -2 standard deviations for age, sex, and education adjusted means in at least one cognitive domain. The participants were categorized into five groups according to the quintiles of NLR (lowest NLR, Q1). The cross-sectional association between NLR and PSCI was assessed using multiple logistic regression, adjusting for age, sex, education, vascular risk factors, and stroke type. Results: A total of 345 patients were enrolled. The mean age was 63.0 years and the median NIHSS score and NLR were 2 [1-4] and 2.26 [1.65-2.91], respectively. PSCI was identified in 71 (20.6%) patients. NLR was a significant predictor for PSCI both as a continuous variable (adjusted OR, 1.14; 95% CI, 1.00-1.31) and as a categorical variable (Q5, adjusted OR, 3.26; 95% CI, 1.17-9.08). Patients in the Q5 group (NLR ≥ 3.80) showed significantly worse performance in global cognition and in visuospatial and memory domains. Conclusions: NLR in the acute stage of ischemic stroke was independently associated with PSCI at 3 months after stroke, and high NLR was specifically associated with cognitive dysfunction in the memory and visuospatial domains. Thus, systemic inflammation may be a modifiable risk factor that may influence cognitive outcomes after stroke.
| S-EPMC8280279 | biostudies-literature
Project description:BackgroundCognitive impairment is one of the common sequelae after stroke, which not only hinders the recovery of patients but also increases the financial burden on families. In the absence of effective therapeutic measures, acupuncture treatment has been widely used in China to treat post-stroke cognitive impairment (PSCI), but the specific efficacy is unclear. Therefore, this review aimed to evaluate the true efficacy of acupuncture treatment in patients with PSCI.MethodsWe searched eight databases [PubMed, Embase, Web of Science, Cochrane Central Register of Controlled Trials, China Biomedical Literature Database (CBM), China Science and Technology Journal (VIP) database, the China National Knowledge Infrastructure (CNKI) database, and Wan fang database] from the inception to May 2022 for randomized controlled trials (RCTs) related to acupuncture treatment combined with cognitive rehabilitation (CR) for PSCI. Two investigators independently used a pre-designed form to extract valid data from eligible RCTs. The risk of bias was assessed through tools provided by the Cochrane Collaboration. The meta-analysis was implemented through Rev Man software (version 5.4). The strength of the evidence obtained was evaluated using GRADE profiler software. Adverse events (AEs) were collected by reading the full text and used to evaluate the safety of acupuncture treatment.ResultsThirty-eight studies involving a total of 2,971 participants were included in this meta-analysis. Overall, the RCTs included in this meta-analysis were poor in methodological quality. The combined results showed that acupuncture treatment combined with CR showed significant superiority compared to CR alone in terms of improving cognitive function [Mean Difference (MD) = 3.94, 95% confidence intervals (CI): 3.16-4.72, P < 0.00001 (MMSE); MD = 3.30, 95%CI: 2.53-4.07, P < 0.00001 (MoCA); MD = 9.53, 95%CI: 5.61-13.45, P < 0.00001 (LOTCA)]. Furthermore, the combination of acupuncture treatment and CR significantly improved patients' self-care ability compared to CR alone [MD = 8.66, 95%CI: 5.85-11.47, P < 0.00001 (MBI); MD = 5.24, 95%CI: 3.90-6.57, P < 0.00001 (FIM)]. Meanwhile, subgroup analysis showed that MMSE scores were not sufficiently improved in the comparison of electro-acupuncture combined with CR versus CR alone (MD = 4.07, 95%CI: -0.45-8.60, P = 0.08). However, we also observed that electro-acupuncture combined with CR was superior to the use of CR alone in improving MoCA and MBI scores in patients with PSCI [MD = 2.17, 95%CI: 0.65-3.70, P = 0.005 (MoCA); MD = 1.74, 95%CI: 0.13-3.35, P = 0.03 (MBI)]. There was no significant difference in the occurrence of adverse events (AE) between acupuncture treatment combined with CR and CR alone (P > 0.05). The certainty of the evidence was rated low level because of flaws in the study design and considerable heterogeneity among the included studies.ConclusionThis review found that acupuncture treatment combined with CR may have a positive effect on improving cognitive function and self-care ability in PSCI patients. However, our findings should be treated with caution owing to the existence of methodological quality issues. High-quality studies are urgently required to validate our results in the future.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022338905, identifier: CRD42022338905.
| S-EPMC9946978 | biostudies-literature
Project description:Objective: To assess the value of the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) during acute phase in predicting post-stroke cognitive impairment (PSCI) at 3-6 months. Methods: We prospectively recruited 229 patients who had suffered their first-ever ischemic stroke. PSCI was determined in 104 of these patients by a comprehensive neuropsychological battery performed at 3-6 months. Receiver operating characteristic (ROC) curve analysis was then performed to compare the discriminatory ability of the MMSE and MoCA. Also, we applied a decision tree generated by the classification and regression tree methodology. Results: In total, 66 patients had PSCI when evaluated 3-6 months after the onset of minor stroke. Logistic regression analysis revealed that education, body mass index (BMI), and baseline MoCA scores were independently associated with PSCI. ROC curve analysis showed that the ability to predict PSCI was similar when compared between baseline MoCA scores [area under curve (AUC), 0.821; 95% confidence interval (CI), 0.743-0.898] and baseline MMSE scores (AUC, 0.809; 95% CI, 0.725-0.892, P = 0.75). Both MMSE and MoCA exhibited similar predictive values at their optimal cut-off points (MMSE ≤27; sensitivity, 0.682; specificity, 0.816; MoCA ≤21; sensitivity, 0.636; specificity, 0.895). Classification and regression tree-derived analysis yielded an AUC of 0.823 (sensitivity, 0.803; specificity, 0.842). Conclusion: When applied within 2 weeks of stroke, the MMSE and MoCA are both useful and have similar predictive value for PSCI 3-6 months after the onset of minor stroke.
| S-EPMC7424073 | biostudies-literature