The prevalence and prognosis of asymptomatic intracranial atherosclerosis in a community-based population: Results based on high-resolution magnetic resonance imaging

Mortality risk among older adults of indigenous ancestry with asymptomatic intracranial atherosclerotic stenosis. A population-based, longitudinal prospective study in rural Ecuador

BACKGROUND

Intracranial atherosclerotic stenosis (ICAS)-related mortality may vary according to race/ethnicity, but information about this association in diverse populations is limited. We aimed to assess mortality according to ICAS severity in stroke-free older adults of indigenous ancestry living in Ecuador.

METHODS

We invited stroke-free individuals ≥60 years old enrolled in the population-based Atahualpa Project cohort to undergo time-of-flight brain MRA. Participants were followed to ascertain mortality (as the primary outcome) during the observation period. Luminal stenosis in 11 large intracranial arteries was calculated to reflect the stenosis score. We categorized prevalent ICAS as a stenosis score ≥3 points or as the presence of moderate-to-severe stenosis (≥50 %). Cox proportional hazards models were fitted to estimate mortality risk according to ICAS severity.

RESULTS

Analysis included 358 participants (mean age: 67.5 ± 6.9 years; 57 % women) followed on average for 10.1 ± 2.9 years. Seventy-four (21 %) participants had a stenosis score ≥3 points, and 37 (10 %) had moderate-to-severe stenosis. In adjusted analysis, mortality risk was higher in participants with a ICAS score ≥3 points (HR: 2.38; 95 % C.I.: 1.49-3.80; p < 0.001) and among those with moderate-to-severe stenosis (HR: 1.96; 95 % C.I.: 1.12-3.43; p = 0.018). Thirty-five (10 %) participants had incident strokes. Overall, 97 (27 %) participants died during the follow-up, including 11/35 who developed an incident stroke and 86/323 who did not (31 % versus 27 %; p = 0.544).

DISCUSSION

The burden of asymptomatic ICAS is high in older adults of indigenous Ecuadorian ancestry and is significantly associated with mortality. Incident strokes do not influence mortality in this population.

High-risk plaque characteristics associated with recurrent stroke in patients with intracranial stenosis: a systematic review and meta-analysis

BACKGROUND

Risk stratification based on intracranial plaque characteristics is crucial for patients with intracranial atherosclerosis (ICAS). Nonetheless, there remains a significant deficit of validated imaging markers capable of predicting recurrent strokes. Consequently, we conducted a systematic review and meta-analysis to investigate the prognostic significance of high-risk plaque characteristics (HPCs) in relation to recurrent stroke.

METHODS

The systematic review was registered in PROSPERO (CRD420245820945). We systematically searched PubMed, Ovid Medline, and Web of Science for studies evaluating the association between HPCs and risk of stroke recurrence. Data were aggregated and pooled using a random-effects meta-analysis. Heterogenicity and publication bias were assessed, with subgroup and sensitivity analyses performed where appropriate.

RESULTS

Eighteen studies, comprising 13 prospective and 5 retrospective, involving a total of 4967 patients (3594 symptomatic, and 1373 asymptomatic), were included in the analysis. Among symptomatic patients, those with HPCs exhibited a higher incidence of stroke recurrence compared to those without HPCs (adjusted HR, 3.90 ([95% CI, 2.15-7.08]). ICAS patients with baseline plaque enhancement (adjusted HR, 5.20 [95% CI, 3.12-8.66]), calcification (adjusted HR, 2.92 [95% CI, 1.32-6.45]), high plaque steepness (adjusted HR, 110.27 [95% CI, 4.75-2559.74]), and progression in plaque burden (adjusted HR, 6.29 [95% CI, 1.62-24.45]) were identified as being at an increased risk of stroke recurrence. Subgroup analyses revealed that traditional cerebrovascular risk factors, including increasing age, hypertension, diabetes mellitus, and smoking, further elevated the risk of HPC-related stroke recurrence in ICAS patients.

CONCLUSION

The identification of HPCs confers independent prognostic value for the prediction of stroke recurrence in ICAS patients, which could be instrumental for patients risk stratification.

Rare RNF213 variants is related to early-onset intracranial atherosclerosis: A Chinese community-based study

BACKGROUND

The relationship between rare variants in Ring finger protein 213 (RNF213) and intracranial atherosclerosis (ICAS) remained unelucidated. Using whole-exome sequencing (WES) and high-resolution magnetic resonance imaging (HR-MRI), this study aimed at investigating the association between rare RNF213 variants and ICAS within a Chinese community-dwelling population.

METHODS

The present study included 821 participants from Shunyi cohort. Genetic data of rare RNF213 variants were acquired by WES and were categorized by functional domains. Intracranial and extracranial atherosclerosis were assessed by brain HR-MRI and carotid ultrasound, respectively. Logistic regression and generalized linear regression were applied to evaluate the effects of rare RNF213 variants on atherosclerosis. Stratification by age were conducted with 50 years old set as the cutoff value.

RESULTS

Ninety-five participants were identified as carriers of rare RNF213 variants. Carotid plaques were observed in 367 (44.7 %) participants, while ICAS was identified in 306 (37.3 %). Rare variants of RNF213 was not associated with ECAS. Employing HR-MRI, both the presence of rare variants (β = 0.150, P = 0.025) and numerical count of variants (β = 0.182, P = 0.003) were significantly correlated with ICAS within the group of age ≤50 years. Both variant existence (β = 0.154, P = 0.014) and variant count (β = 0.188, P = 0.003) were significantly associated with plaques in middle cerebral arteries within younger subgroup, rather than basilar arteries. Furthermore, a significant association was observed between variants that located outside the N-arm domain and ICAS in the younger subgroup (OR = 2.522, P = 0.030). Statistical results remained robust after adjusted for age, gender, and cardiovascular risk factors.

CONCLUSIONS

Rare variants of RNF213 is associated with age-related ICAS in general Chinese population, highlighting the potential role of RNF213 as a genetic contributor to early-onset ICAS.

Nomogram for predicting asymptomatic intracranial atherosclerotic stenosis in a neurologically healthy population

Asymptomatic intracranial atherosclerotic stenosis (aICAS) is a major risk factor for cerebrovascular events. The study aims to construct and validate a nomogram for predicting the risk of aICAS. Participants who underwent health examinations at our center from September 2019 to August 2023 were retrospectively enrolled. The participants were randomly divided into a training set and a testing set in a 7:3 ratio. Firstly, in the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were performed to select variables that were used to establish a nomogram. Then, the receiver operating curves (ROC) and calibration curves were plotted to assess the model's discriminative ability and performance. A total of 2563 neurologically healthy participants were enrolled. According to LASSO-Logistic regression analysis, age, fasting blood glucose (FBG), systolic blood pressure (SBP), hypertension, and carotid atherosclerosis (CAS) were significantly associated with aICAS in the multivariable model (adjusted P < 0.005). The area under the ROC of the training and testing sets was, respectively, 0.78 (95% CI: 0.73-0.82) and 0.65 (95% CI: 0.56-0.73). The calibration curves showed good homogeneity between the predicted and actual values. The nomogram, consisting of age, FBG, SBP, hypertension, and CAS, can accurately predict aICAS risk in a neurologically healthy population.

Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage

BACKGROUND

Spontaneous intracerebral hemorrhage (sICH) is associated with significant mortality and morbidity. Predicting the prognosis of patients with sICH remains an important issue, which significantly affects treatment decisions. Utilizing readily available clinical parameters to anticipate the unfavorable prognosis of sICH patients holds notable clinical significance. This study employs five machine learning algorithms to establish a practical platform for the prediction of short-term prognostic outcomes in individuals afflicted with sICH.

METHODS

Within the framework of this retrospective analysis, the model underwent training utilizing data gleaned from 413 cases from the training center, with subsequent validation employing data from external validation center. Comprehensive clinical information, laboratory analysis results, and imaging features pertaining to sICH patients were harnessed as training features for machine learning. We developed and validated the model efficacy using all the selected features of the patients using five models: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGboost and LightGBM, respectively. The process of Recursive Feature Elimination (RFE) was executed for optimal feature screening. An internal five-fold cross-validation was employed to pinpoint the most suitable hyperparameters for the model, while an external five-fold cross-validation was implemented to discern the machine learning model demonstrating the superior average performance. Finally, the machine learning model with the best average performance is selected as our final model while using it for external validation. Evaluation of the machine learning model's performance was comprehensively conducted through the utilization of the ROC curve, accuracy, and other relevant indicators. The SHAP diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features and establish a practical prognostic prediction platform.

RESULTS

A total of 413 patients with sICH patients were collected in the training center, of which 180 were patients with poor prognosis. A total of 74 patients with sICH were collected in the external validation center, of which 26 were patients with poor prognosis. Within the training set, the test set AUC values for SVM, LR, RF, XGBoost, and LightGBM models were recorded as 0.87, 0.896, 0.916, 0.885, and 0.912, respectively. The best average performance of the machine learning models in the training set was the RF model (average AUC: 0.906 ± 0.029, P < 0.01). The model still maintains a good performance in the external validation center, with an AUC of 0.817 (95% CI 0.705-0.928). Pertaining to feature importance for short-term prognostic attributes of sICH patients, the NIHSS score reigned supreme, succeeded by AST, Age, white blood cell, and hematoma volume, among others. In culmination, guided by the RF model's variable importance weight and the model's ROC curve insights, the NIHSS score, AST, Age, white blood cell, and hematoma volume were integrated to forge a short-term prognostic prediction platform tailored for sICH patients.

CONCLUSION

We constructed a prediction model based on the results of the RF model incorporating five clinically accessible predictors with reliable predictive efficacy for the short-term prognosis of sICH patients. Meanwhile, the performance of the external validation set was also more stable, which can be used for accurate prediction of short-term prognosis of sICH patients.