Background and Purpose: It has been widely reported that stress hyperglycemia contributes to poor prognosis in patients experiencing acute ischemic stroke (AIS). However, its predictive value for early neurological deterioration (END) after intravenous administration of recombinant tissue-type plasminogen activator (IV-rtPA) in AIS patients is still unclear. The aim of this study was to evaluate the impact of stress hyperglycemia on the risk of END after IV-rtPA.Methods: A total of 798 consecutive patients treated with IV-rtPA were included in this study. The stress hyperglycemia ratio (SHR) was calculated as fasting plasma glucose level at admission (mg/dl)/glycosylated hemoglobin (HbAlc) (%). END was defined as a National Institutes of Health Stroke Scale Score (NIHSS) ≥ 4 points 24 h after IV-rtPA, and poor functional outcome at discharge was defined as a modified Rankin Scale (mRS) score of 3–6 at discharge. Patients with a prior history of diabetes or HbAlc ≥ 6.5% were considered to have diabetes mellitus. Patients were grouped according to SHR values. Multivariate logistical regression was used to evaluate the risk of END for patients within specific SHR categories.Results: In total, 139 (17.4%) patients had END. After adjusting for confounders, the highest tertile group had higher risks of END and poor functional outcome at discharge than those of patients in the lowest tertile group (OR, 1.95; 95% CI, 1.21–3.15; p = 0.006) (OR, 1.85; 95% CI, 1.163–2.941; p = 0.009), and the predictive value of high SHR for END was also significant in patients with diabetes mellitus (OR, 3.05; 95% CI, 1.29–7.21; p = 0.011). However, a significant association of high SHR and poor functional outcome was only found in patients without diabetes (OR, 1.85; 95% CI, 1.002–3.399; p = 0.045).Conclusion: A higher SHR predicted that patients with severe stress hyperglycemia had higher risks of END and poor functional outcome at discharge after IV-rtPA.

Background and purpose: Recurrent stroke accounts for 25–30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS).Methods: A total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator.Results: Logistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly.Conclusion: This study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.

Objective: To explore the predictors of death in acute ischemic stroke (AIS) patients within 1 year based on machine learning (ML) algorithms.Methods: This study retrospectively analyzed the clinical data of patients hospitalized and diagnosed with AIS in the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. The patients were randomly divided into training and validation sets at a ratio of 7:3, and the clinical characteristic variables of the patients were screened using univariate and multivariate logistics regression. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGB), random forest (RF), decision tree (DT), and naive Bayes classifier (NBC), were applied to develop models to predict death in AIS patients within 1 year. During training, a 10-fold cross-validation approach was used to validate the training set internally, and the models were interpreted using important ranking and the SHapley Additive exPlanations (SHAP) principle. The validation set was used to externally validate the models. Ultimately, the highest-performing model was selected to build a web-based calculator.Results: Multivariate logistic regression analysis revealed that C-reactive protein (CRP), homocysteine (HCY) levels, stroke severity (SS), and the number of stroke lesions (NOS) were independent risk factors for death within 1 year in patients with AIS. The area under the curve value of the XGB model was 0.846, which was the highest among the six ML algorithms. Therefore, we built an ML network calculator (https://mlmedicine-de-stroke-de-stroke-m5pijk.streamlitapp.com/) based on XGB to predict death in AIS patients within 1 year.Conclusions: The network calculator based on the XGB model developed in this study can help clinicians make more personalized and rational clinical decisions.