An easy scoring system to predict the risk of poor outcome after mechanical thrombectomy among the elderly is currently not available. Therefore, we aimed to develop a nomogram for predicting the probability of negative prognosis in aged patients with acute ischemic stroke undergoing thrombectomy. In addition, we sought to investigate the association between histological thrombus composition and stroke characteristics. To this end, we prospectively studied a developed cohort using data collected from a stroke center from November 2015 to December 2019. The main outcome was functional independence, defined as a modified Rankin   Scale   score ≤ 2 at 90 days following a mechanical thrombectomy. A nomogram model based on multivariate logistic models was generated. The retrieved thrombi were stained with hematoxylin and eosin and assessed according to histological composition. Our results demonstrated that age ≥ 72   years was independently associated with poor outcome. A total of 304 participants completed the follow-up data to generate the nomogram model. After multivariate logistic regression, five variables remained independent predictors of outcome, including older age, hemorrhagic transformation, thrombolysis in cerebral infarction score, National Institute of Health Stroke score, and neutrophil-to-lymphocyte ratio, and were used to generate the nomogram. The area under the receiver-operating characteristic curve of the model was 0.803. The clots from elderly subjects with large-artery atherosclerosis, anterior circulation, and successful recanalization groups had a higher percentage of fibrin compared to those of younger patients. This is the first nomogram to be developed and validated in a stroke center cohort for individualized prediction of poor outcome in elderly patients after mechanical thrombectomy. Clot composition provides valuable information on the underlying pathogenesis of oxidation in older patients.

An easily scoring system to predict the risk of cognitive impairment after minor ischemic stroke has not been available. We aimed to develop and externally validate a nomogram for predicting the probability of post-stroke cognitive impairment (PSCI) among hospitalized population with minor stroke. Moreover, the association of Trimethylamine N-oxide (TMAO) with PSCI is also investigated. We prospectively conducted a developed cohort on collected data in stroke center from June 2017 to February 2018, as well as an external validation cohort between June 2018 and February 2019. The main outcome is cognitive impairment defined as <22 Montreal Cognition Assessment (MoCA) score points 6 – 12 months following a minor stroke onset. Based on multivariate logistic models, the nomogram model was generated. Plasma TMAO levels were assessed at admission using liquid chromatography tandem mass spectrometry. A total of 228 participants completed the follow-up data for generating the nomogram. After multivariate logistic regression, seven variables remained independent predictors of PSCI to compose the nomogram included age, female, Fazekas score, educational level, number of intracranial atherosclerotic stenosis (ICAS), HbA1c, and cortical infarction. The area under the receiver-operating characteristic (AUC-ROC) curve of model was 0.829, C index was good (0.810), and the AUC-ROC of the model applied in validation cohort was 0.812. Plasma TMAO levels were higher in patients with cognitive impairment than in them without cognitive dysfunction (median 4.56 vs. 3.22 μmol/L; p ≤ 0.001). In conclusion, this scoring system is the first nomogram developed and validated in a stroke center cohort for individualized prediction of cognitive impairment after minor stroke. Higher plasma TMAO level at admission suggests a potential marker of PSCI.

ObjectiveCurrently, the risk of occult atrial fibrillation (AF) could not be predicted in patients with acute ischemic stroke (AIS) using a simple scoring system. Therefore, in this study, we developed and externally validated a nomogram to predict occult AF in patients with AIS.MethodsIn this study, we prospectively conducted a development cohort study with data collected at our stroke center from July 2017 to February 2018, and an external validation cohort from March 2019 to December 2019.ResultsFollow-up data were collected from 177 participants (56.5% older than 65 years, 29.4% female) for generating the nomogram model. Multivariate logistic regression analysis was performed with AF as the dependent variable indicated that age >65 years, heart rate >100, C-reactive protein (CRP), N-terminal pro-B-type natriuretic peptide (NT-proBNP) >270, hemorrhagic transformation (HT) as independent variables for predicting the development of AF, and a nomogram was generated based on these factors. The area under the receiver operating characteristic curve (AUC-ROC) for the model was 0.937, the C-index was 0.926, and the AUC-ROC for the validation cohort was 0.913.ConclusionTo our knowledge, this is the first nomogram developed and externally validated in a stroke center cohort for individualized prediction of risk of developing AIS in patients with occult AF. This nomogram could provide valuable information for the screening of occult AF after a stroke.