The predictors of death within 1 year in acute ischemic stroke patients based on machine learning

Predicting functional outcomes of patients with spontaneous intracerebral hemorrhage based on explainable machine learning models: a multicenter retrospective study

Background

Spontaneous intracerebral hemorrhage (SICH) is the second most common cause of cerebrovascular disease after ischemic stroke, with high mortality and disability rates, imposing a significant economic burden on families and society. This retrospective study aimed to develop and evaluate an interpretable machine learning model to predict functional outcomes 3 months after SICH.

Methods

A retrospective analysis was conducted on clinical data from 380 patients with SICH who were hospitalized at three different centers between June 2020 and June 2023. Seventy percent of the samples were randomly selected as the training set, while the remaining 30% were used as the validation set. Univariate analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Pearson correlation analysis were used to screen clinical variables. The selected variables were then incorporated into five machine learning models: complementary naive bayes (CNB), support vector machine (SVM), gaussian naive bayes (GNB), multilayer perceptron (MLP), and extreme gradient boosting (XGB), to assess their performance. Additionally, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model, and global and individual interpretive analyses were conducted using importance ranking and Shapley additive explanations (SHAP).

Results

Among the 380 patients, 95 ultimately had poor prognostic outcomes. In the validation set, the AUC values for CNB, SVM, GNB, MLP, and XGB models were 0.899 (0.816-0.979), 0.916 (0.847-0.982), 0.730 (0.602-0.857), 0.913 (0.834-0.986), and 0.969 (0.937-0.998), respectively. Therefore, the XGB model performed the best among the five algorithms. SHAP analysis revealed that the GCS score, hematoma volume, blood pressure changes, platelets, age, bleeding location, and blood glucose levels were the most important variables for poor prognosis.

Conclusion

The XGB model developed in this study can effectively predict the risk of poor prognosis in patients with SICH, helping clinicians make personalized and rational clinical decisions. Prognostic risk in patients with SICH is closely associated with GCS score, hematoma volume, blood pressure changes, platelets, age, bleeding location, and blood glucose levels.

An integrated nomogram combining clinical and radiomic features of hyperattenuated imaging markers to predict malignant cerebral edema following endovascular thrombectomy

Background

Malignant cerebral edema (MCE), a potential complication following endovascular thrombectomy (EVT) in the treatment of acute ischemic stroke (AIS), can result in significant disability and mortality. This study aimed to develop a nomogram model based on the hyperattenuated imaging marker (HIM), characterized by hyperattenuation on head noncontrast computed tomography (CT) immediately after thrombectomy, to predict MCE in patients receiving EVT.

Methods

In this retrospective cohort study, we selected 151 patients with anterior circulation large-vessel occlusion who received endovascular treatment. The patients were randomly allocated into training (n=121) and test (n=30) cohorts. HIM was used to extract radiomics characteristics. Conventional clinical and radiological features associated with MCE were also extracted. A model based on extreme gradient boosting (XGBoost) machine learning using fivefold cross-validation was employed to acquire radiomics and clinical features. Based on HIM, clinical and radiological signatures were used to construct a prediction nomogram for MCE. Subsequently, the signatures were merged through logistic regression (LR) analysis in order to create a comprehensive clinical radiomics nomogram.

Results

A total of 28 patients out of 151 (18.54%) developed MCE. The analysis of the receiver operating characteristic curve indicated an area under the curve (AUC) of 0.999 for the prediction of MCE in the training group and an AUC of 0.938 in the test group. The clinical and radiomics nomogram together showed the highest accuracy in predicting outcomes in both the training and test groups.

Conclusions

The novel nomogram, which combines clinical manifestations and imaging findings based on postinterventional HIM, may serve as a predictor for MCE in patients experiencing AIS after EVT.

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

BACKGROUND

Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality.

METHODS

We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction.

RESULTS

The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk.

CONCLUSIONS

Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

Efficacy and safety of mechanical thrombectomy in the posterior cerebral circulation-a single center study

Mechanical thrombectomy (MT) is the current standard treatment for strokes in the anterior cerebral circulation (AMT) and has recently been proven to be beneficial in the posterior circulation strokes (PMT). Our study aims to evaluate parameters for favorable outcomes in PMT-patients and to compare the clinical characteristics of individuals who received AMT and PMT. For this purpose, we confronted AMT and PMT-receipients and performed a multivariate regression analysis to assess the influence of factors on favorable outcomes in the study group and in the AMT and PMT subgroups. When analysing 623 MT-patients, those who received PMT had significantly lower admission National Institutes of Health Stroke Scale (NIHSS) scores (9 vs. 13; p < 0.001) and 24 h post-MT (7 vs. 12; p = 0.006). Key parameters influencing the favorable outcomes of PMT at discharge and at 90th day include: NIHSS scores (OR: 0.865, 95% CI: 0.813-0.893, and OR: 0.900, 95% CI: 0.861-0.925), MT time (OR: 0.993, 95% CI: 0.987-0.998 and OR: 0.993, 95% CI: 0.990-0.997), and leukocytosis (OR: 0.961, 95% CI: 0.928-0.988 and OR: 0.974, 95% CI: 0.957-0.998). Different clinical profiles exist between AMT and PMT-recipients, with the neurological status post-MT being decisive for the prognosis. Several factors play an important role in predicting outcome, especially in the PMT group.