A new nomogram for individualized prediction of the probability of hemorrhagic transformation after intravenous thrombolysis for ischemic stroke patients

Nomogram established on account of Lasso-logistic regression for predicting hemorrhagic transformation in patients with acute ischemic stroke after endovascular thrombectomy

BACKGROUND

Hemorrhagic transformation (HT) is a common and serious complication in patients with acute ischemic stroke (AIS) after endovascular thrombectomy (EVT). This study was performed to determine the predictive factors associated with HT in stroke patients with EVT and to establish and validate a nomogram that combines with independent predictors to predict the probability of HT after EVT in patients with AIS.

METHODS

All patients were randomly divided into development and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal factors, and multivariate logistic regression analysis was used to build a clinical prediction model. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance.

RESULTS

LASSO regression analysis showed that Alberta Stroke Program Early CT Scores (ASPECTS), international normalized ratio (INR), uric acid (UA), neutrophils (NEU) were the influencing factors for AIS with HT after EVT. A novel prognostic nomogram model was established to predict the possibility of HT with AIS after EVT. The calibration curve showed that the model had good consistency. The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting HT was 0.797 in the development cohort and 0.786 in the validation cohort.

CONCLUSION

This study proposes a novel and practical nomogram based on ASPECTS, INR, UA, NEU, which can well predict the probability of HT after EVT in patients with AIS.

Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis

Objective

This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models.

Methods

We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software.

Results

A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance.

Conclusion

Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings.

Registration

This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.

Predictive model for the risk of hemorrhagic transformation after rt-PA intravenous thrombolysis in patients with acute ischemic stroke: A systematic review and meta-analysis

OBJECTIVE

To systematically review the risk prediction model of Hemorrhages Transformation (HT) after intravenous thrombolysis in patients with Acute Ischemic Stroke (AIS).

METHODS

Web of Science, The Cochrane Library, PubMed, Embase, CINAHL, CNKI, CBM, WanFang, and VIP were searched from inception to February 25, 2023 for literature related to the risk prediction model for HT after thrombolysis in AIS.

RESULTS

A total of 17 included studies contained 26 prediction models, and the AUC of all models at the time of modeling ranged from 0.662 to 0.9854, 16 models had AUC>0.8, indicating that the models had good predictive performance. However, most of the included studies were at risk of bias. the results of the Meta-analysis showed that atrial fibrillation (OR=2.72, 95% CI:1.98-3.73), NIHSS score (OR=1.09, 95% CI:1.07-1.11), glucose (OR=1.12, 95% CI:1.06-1.18), moderate to severe leukoaraiosis (OR=3.47, 95% CI:1.61-7.52), hyperdense middle cerebral artery sign (OR=2.35, 95% CI:1.10-4.98), large cerebral infarction (OR=7.57, 95% CI:2.09-27.43), and early signs of infarction (OR=4.80, 95% CI:1.74-13.25) were effective predictors of HT after intravenous thrombolysis in patients with AIS.

CONCLUSIONS

The performance of the models for HT after thrombolysis in patients with AIS in the Chinese population is good, but there is some risk of bias. Future post-intravenous HT conversion prediction models for AIS patients in the Chinese population should focus on predictors such as atrial fibrillation, NIHSS score, glucose, moderate to severe leukoaraiosis, hyperdense middle cerebral artery sign, massive cerebral infarction, and early signs of infarction.

Risk prediction models for intracranial hemorrhage in acute ischemic stroke patients receiving intravenous alteplase treatment: a systematic review

Objectives

To identify and compare published models that use related factors to predict the risk of intracranial hemorrhage (ICH) in acute ischemic stroke patients receiving intravenous alteplase treatment.

Methods

Risk prediction models for ICH in acute ischemic stroke patients receiving intravenous alteplase treatment were collected from PubMed, Embase, Web of Science, and the Cochrane Library up to April 7, 2023. A meta-analysis was performed using Stata 13.0, and the included models were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).

Results

A total of 656 references were screened, resulting in 13 studies being included. Among these, one was a prospective cohort study. Ten studies used internal validation; five studies used external validation, with two of them using both. The area under the receiver operating characteristic (ROC) curve for subjects reported in the models ranged from 0.68 to 0.985. Common predictors in the prediction models include National Institutes of Health Stroke Scale (NIHSS) (OR = 1.17, 95% CI 1.09-1.25, p < 0.0001), glucose (OR = 1.54, 95% CI 1.09-2.17, p < 0.05), and advanced age (OR = 1.50, 95% CI 1.15-1.94, p < 0.05), and the meta-analysis shows that these are independent risk factors. After PROBAST evaluation, all studies were assessed as having a high risk of bias but a low risk of applicability concerns.

Conclusion

This study systematically reviews available evidence on risk prediction models for ICH in acute ischemic stroke patients receiving intravenous alteplase treatment. Few models have been externally validated, while the majority demonstrate significant discriminative power.

A Novel Nomogram to Predict Symptomatic Intracranial Hemorrhage in Ischemic Stroke Patients After Intravenous Thrombolysis

Objective

This study aimed to create and validate a novel nomogram to predict the risk of symptomatic intracranial hemorrhage (sICH) in patients with acute ischemic stroke (AIS) who underwent intravenous thrombolysis (IVT).

Methods

In this retrospective study, 784 patients with AIS who received IVT were enrolled. The patients were randomly divided into two groups: a training set (n=550, 70%) and a testing set (n=234, 30%). Utilizing multivariable logistic regression analysis, relevant factors for the predictive nomogram were selected. The performance of the nomogram was evaluated using various metrics, including the area under the receiver operating characteristic curve (AUC-ROC), the Hosmer-Lemeshow goodness-of-fit test, calibration plots, and decision curve analysis (DCA).

Results

Multivariable logistic regression analysis showed that specific factors, including National Institutes of Health Stroke Scale (NIHSS) scores, Early infarct signs (EIS), and serum sodium, were identified as independent predictors of sICH. Subsequently, a nomogram was constructed using these predictors. The AUC-ROC values of the nomogram were 0.864 (95% CI: 0.810-0.919) and 0.831 (95% CI: 0.770-0.891) in the training and the validation sets, respectively. Both the calibration plots and the Hosmer-Lemeshow goodness-of-fit test showed favorable agreement in both the training and the validation sets. Additionally, the DCA indicated the practical clinical utility of the nomogram.

Conclusion

The novel nomogram, which included NIHSS, EIS and serum sodium as variables, had the potential for predicting the risk of sICH in patients with AIS after IVT.

Predictors of 7-day symptomatic hemorrhagic transformation in patients with acute ischemic stroke and proposal of a novel screening tool: A retrospective cohort study

OBJECTIVES

Hemorrhagic transformation (HT) is significantly related to poor neurological outcomes and mortality. Although variables and models that predict HT have been reported in the literature, the need for a model with high diagnostic performance continues. We aimed to propose a model that can accurately predict symptomatic HT within 7 days of acute ischemic stroke (AIS).

METHODS

Patients with AIS admitted to the emergency department of a tertiary training and research hospital between November 07, 2021, and August 26, 2022, were included in this single-center retrospective study. For the model, binary logistics with the forced-entry method was used and the model was validated with 3-fold cross-validation. After the final model was created, the optimal cutoff point was determined with Youden's index. Another cut-off point was determined at which the sensitivity was the highest.

RESULTS

The mean age of the 423 patients included in the study was 70 (60-81) and 53.7% (n = 227) of the patients were male. Symptomatic HT was present in 31 (7.3%) patients. Mechanical thrombectomy, atrial fibrillation, and diabetes mellitus were the independent predictors (P < 0.001, P = 0.003, P = 0.006, respectively). The mean area under the curve of the receiver operating characteristics of the model was 0.916 (95% confidence interval [CI] = 0.876-0.957). The sensitivity for the optimal cut-off point was 90.3% (95% CI = 74.3%-97.9%) and specificity was 80.6% (95% CI = 76.4%-84.4%). For the second cutoff point where the sensitivity was 100%, the specificity was 60.5% (95% CI = 55.4%-65.3%).

CONCLUSION

The diagnostic performance of our model was satisfactory and it seems to be promising for symptomatic HT. External validation studies are required to implement our results into clinical use.

Serum biomarkers to predict hemorrhagic transformation and ischemic stroke outcomes in a prospective cohort study

Ischemic stroke (IS) remains one of the most frequent causes of death and disability worldwide. Identifying possible prognosis factors for IS outcomes, including hemorrhagic transformation (HT), could improve patients' recovery. This study aimed to investigate the potential prognosis role of non-specific laboratory data at admission and baseline MMP-2 and MMP-9 serum levels in predicting HT risk, discharge, and 3-month follow-up status of IS patients. Data from 150 successive acute cerebral infarction patients were analyzed in a prospective cohort study. The active group included patients who developed HT during hospitalization (55 persons). There were no significant differences in age, gender distribution, time to admission, or time to blood sample collection for MMPs measurement between patients in the active and control groups. IS patients from the active group had a significantly higher rate of AF (atrial fibrillation) in the past (p=0.003), while differences in other factors such as diabetes, hypertension, myocardial infarction, previous stroke, obesity, smoking, and alcohol were not significant. Admission NIHSS score and mRS (modified Rankin Scale) values (at discharge and 90 days) were significantly worse in the active group (p<0.001). Among the analyzed admission laboratory factors (glycemia, lipid profile, coagulation panel, inflammatory reaction parameters, MMP-2, MMP-9), INR presented an inverse correlation, with lower values in the HT cohort (univariate analysis - p=0.01, OR=0.11; multivariate analysis - p=0.03, OR=0.09). Further research on larger cohorts is warranted to determine the specific laboratory biomarkers for predicting hemorrhagic transformation and ischemic stroke outcomes.

Effects of silent brain infarction on the hemorrhagic transformation and prognosis in patients with acute ischemic stroke after intravenous thrombolysis

Background

Silent brain infarction (SBI) is a special type of stroke with no definitive time of onset, which can be found on pre-thrombolysis imaging examination in some patients with acute ischemic stroke (AIS). However, the significance of SBI on intracranial hemorrhage transformation (HT) and clinical outcomes after intravenous thrombolysis therapy (IVT) is uncertain. We aimed to explore the effects of SBI on intracranial HT and the 3-month clinical outcome in patients with AIS after IVT.

Methods

We consecutive collected patients who were diagnosed with ischemic stroke and received IVT from August 2016 to August 2022, and conducted a retrospective analysis in this study. The clinical and laboratory data were obtained from hospitalization data. Patients were divided into SBI and Non-SBI groups based on clinical and neuroimaging data. We use Cohen's Kappa to assess the interrater reliability between the two evaluators, and multivariate logistic regression analysis was used to further assess the association between SBI, HT and clinical outcomes at 3 months after IVT.

Results

Of the 541 patients, 231 (46.1%) had SBI, 49 (9.1%) had HT, 438 (81%) had favorable outcome, 361 (66.7%) had excellent outcome. There was no significant difference in the incidence of HT (8.2 vs. 9.7%, p = 0.560) and favorable outcome (78.4% vs. 82.9%, p = 0.183) between patients with SBI and Non-SBI. However, patients with SBI had a lower incidence of excellent outcome than the patients with Non-SBI (60.2% vs. 71.6%%, p = 0.005). After adjustment for major covariates, multivariate logistic regression analysis disclosed that SBI was independently associated with the increased risk of worse outcome (OR = 1.922, 95%CI: 1.229-3.006, p = 0.004).

Conclusion

We found that SBI was no effect for HT after thrombolysis in ischemic stroke patients, and no effect on favorable functional outcome at 3 months. Nevertheless, SBI remained an independent risk factor for non-excellent functional outcomes at 3 months.

A Novel Nomogram for Predicting Malignant Cerebral Edema After Endovascular Thrombectomy in Acute Ischemic Stroke: A Retrospective Cohort Study

BACKGROUND

Malignant cerebral edema (MCE) is a common and feared complication after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS). This study aimed to establish a nomogram to predict MCE in anterior circulation large vessel occlusion stroke (LVOS) patients receiving EVT in order to guide the postoperative medical care in the acute phase.

METHODS

In this retrospective cohort study, 381 patients with anterior circulation LVOS receiving EVT were screened from 636 hospitalized patients with LVOS at 2 stroke medical centers. Clinical baseline data and imaging data were collected within 2-5 days of admission to the hospital. The patients were divided into 2 groups based on whether MCE occurred after EVT. Multivariate logistic regression analysis was used to evaluate the independent risk factors for MCE and to establish a nomogram.

RESULTS

Sixty-six patients out of 381 (17.32%) developed MCE. The independent risk factors for MCE included admission National Institutes of Health Stroke Scale (NIHSS) ≥16 (odds ratio [OR] 1.851; 95% CI 1.029-3.329; P = 0.038), ASPECT score (OR 0.621; 95% CI 0.519-0.744; P < 0.001), right hemisphere (OR 1.636; 95% CI 0.941-2.843; P = 0.079), collateral circulation (OR 0.155; 95% CI 0.074-0.324; P < 0.001), recanalization (OR 0.223; 95% CI 0.109-0.457; P < 0.001), hematocrit (OR, 0.937; 95% CI: 0.892-0.985; P =0.010), and glucose (OR 1.118; 95% CI 1.023-1.223; P = 0.036), which were adopted as parameters of the nomogram. The receiver operating characteristic curve analysis showed that the area under the curve of the nomogram in predicting MCE was 0.901(95% CI 0.848-0.940; P < 0.001). The Hosmer-Lemeshow test results were not significant (P = 0.685), demonstrating a good calibration of the nomogram.

CONCLUSIONS

The novel nomogram composed of admission NIHSS, ASPECT scores, right hemisphere, collateral circulation, recanalization, hematocrit, and serum glucose provide a potential predictor for MCE in patients with AIS after EVT.

Symptomatic Intracranial Hemorrhage After Endovascular Stroke Treatment: External Validation of Prediction Models

BACKGROUND

Symptomatic intracranial hemorrhage (sICH) is a severe complication of reperfusion therapy for ischemic stroke. Multiple models have been developed to predict sICH or intracranial hemorrhage (ICH) after reperfusion therapy. We provide an overview of published models and validate their ability to predict sICH in patients treated with endovascular treatment in daily clinical practice.

METHODS

We conducted a systematic search to identify models either developed or validated to predict sICH or ICH after reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) for ischemic stroke. Models were externally validated in the MR CLEAN Registry (n=3180; Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). The primary outcome was sICH according to the Heidelberg Bleeding Classification. Model performance was evaluated with discrimination (c-statistic, ideally 1; a c-statistic below 0.7 is considered poor in discrimination) and calibration (slope, ideally 1, and intercept, ideally 0).

RESULTS

We included 39 studies describing 40 models. The most frequently used predictors were baseline National Institutes of Health Stroke Scale (NIHSS; n=35), age (n=22), and glucose level (n=22). In the MR CLEAN Registry, sICH occurred in 188/3180 (5.9%) patients. Discrimination ranged from 0.51 (SPAN-100 [Stroke Prognostication Using Age and National Institutes of Health Stroke Scale]) to 0.61 (SITS-SICH [Safe Implementation of Treatments in Stroke Symptomatic Intracerebral Hemorrhage] and STARTING-SICH [STARTING Symptomatic Intracerebral Hemorrhage]). Best calibrated models were IST-3 (intercept, -0.15 [95% CI, -0.01 to -0.31]; slope, 0.80 [95% CI, 0.50-1.09]), SITS-SICH (intercept, 0.15 [95% CI, -0.01 to 0.30]; slope, 0.62 [95% CI, 0.38-0.87]), and STARTING-SICH (intercept, -0.03 [95% CI, -0.19 to 0.12]; slope, 0.56 [95% CI, 0.35-0.76]).

CONCLUSIONS

The investigated models to predict sICH or ICH discriminate poorly between patients with a low and high risk of sICH after endovascular treatment in daily clinical practice and are, therefore, not clinically useful for this patient population.

A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Background

Thrombolysis is the first-line treatment for patients with acute ischemic stroke. Previous studies leveraged machine learning to assist neurologists in selecting patients who could benefit the most from thrombolysis. However, when designing the algorithm, most of the previous algorithms traded interpretability for predictive power, making the algorithms hard to be trusted by neurologists and be used in real clinical practice.

Methods

Our proposed algorithm is an advanced version of classical k-nearest neighbors classification algorithm (KNN). We achieved high interpretability by changing the isotropy in feature space of classical KNN. We leveraged a cohort of 189 patients to prove that our algorithm maintains the interpretability of previous models while in the meantime improving the predictive power when compared with the existing algorithms. The predictive powers of models were assessed by area under the receiver operating characteristic curve (AUC).

Results

In terms of interpretability, only onset time, diabetes, and baseline National Institutes of Health Stroke Scale (NIHSS) were statistically significant and their contributions to the final prediction were forced to be proportional to their feature importance values by the rescaling formula we defined. In terms of predictive power, our advanced KNN (AUC 0.88) outperformed the classical KNN (AUC 0.75, p = 0.0192 ).

Conclusions

Our preliminary results show that the advanced KNN achieved high AUC and identified consistent significant clinical features as previous clinical trials/observational studies did. This model shows the potential to assist in thrombolysis patient selection for improving the successful rate of thrombolysis.

Risk factors of hemorrhagic transformation in acute ischaemic stroke: A systematic review and meta-analysis

Background

Hemorrhagic transformation (HT) following reperfusion therapies for acute ischaemic stroke often predicts a poor prognosis. This systematic review and meta-analysis aims to identify risk factors for HT, and how these vary with hyperacute treatment [intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT)].

Methods

Electronic databases PubMed and EMBASE were used to search relevant studies. Pooled odds ratio (OR) with 95% confidence interval (CI) were estimated.

Results

A total of 120 studies were included. Atrial fibrillation and NIHSS score were common predictors for any intracerebral hemorrhage (ICH) after reperfusion therapies (both IVT and EVT), while a hyperdense artery sign (OR = 2.605, 95% CI 1.212-5.599, I ² = 0.0%) and number of thrombectomy passes (OR = 1.151, 95% CI 1.041-1.272, I ² = 54.3%) were predictors of any ICH after IVT and EVT, respectively. Common predictors for symptomatic ICH (sICH) after reperfusion therapies were age and serum glucose level. Atrial fibrillation (OR = 3.867, 95% CI 1.970-7.591, I ² = 29.1%), NIHSS score (OR = 1.082, 95% CI 1.060-1.105, I ² = 54.5%) and onset-to-treatment time (OR = 1.003, 95% CI 1.001-1.005, I ² = 0.0%) were predictors of sICH after IVT. Alberta Stroke Program Early CT score (ASPECTS) (OR = 0.686, 95% CI 0.565-0.833, I ² =77.6%) and number of thrombectomy passes (OR = 1.374, 95% CI 1.012-1.866, I ² = 86.4%) were predictors of sICH after EVT.

Conclusion

Several predictors of ICH were identified, which varied by treatment type. Studies based on larger and multi-center data sets should be prioritized to confirm the results.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=268927, identifier: CRD42021268927.

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements

In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images.

Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis

Background and purpose

The prediction of neurological outcomes in ischemic stroke patients is very useful in treatment choices, as well as in post-stroke management. This study is to develop a convenient nomogram for the bedside evaluation of stroke patients with intravenous thrombolysis.

Materials and methods

We reviewed all enrolled stroke patients with intravenous thrombolysis retrospectively. Favorable outcome was defined as modified Rankin Score (mRs) less than 2 at 90 days post thrombolysis. We compared the clinical characteristics between patients with favorable outcome and poor outcome. Then, we applied logistic regression models and compared their predictability.

Results

A total of 918 patients were enrolled in this study, 448 patients from one hospital were included to develop a nomogram, whereas 470 patients from the other hospital were used for the external validation. Associated risk factors were identified by multivariate logistic regression. The nomogram was validated by the area under the receiver operating characteristic curve (AUC). A nomogram was developed with baseline NIHSS, blood sugar, blood cholesterol level, part-and full anterior circulation infarction (OCSP type). The AUC was 0.767 (95% CI 0.653–0.772) and 0.836 (95% CI 0.697–0.847) in the derivation and external validation cohorts, respectively. The calibration plot for the probability of severe neurological outcome showed an optimal agreement between the prediction by nomogram and actual observation in both derivation and validation cohorts.

Conclusion

A convenient outcome evaluation nomogram for patients with intravenous thrombolysis was developed, which could be used by physicians in making clinical decisions and predicting patients’ prognosis.

A new nomogram including total cerebral small vessel disease burden for individualized prediction of early-onset depression in patients with acute ischemic stroke

Objectives

The present study was designed to evaluate the effects of total cerebral small vessel disease (CSVD) on early-onset depression after acute ischemic stroke (AIS), and to develop a new nomogram including total CSVD burden to predict early-onset post-stroke depression (PSD).

Methods

We continuously enrolled patients with AIS who were hospitalized at the First Affiliated Hospital of Soochow University between October 2017 and June 2019. All patients were assessed for depressive symptoms using the 17-item Hamilton Depression Scale (HAMD-17) at 14 ± 2 days after the onset of AIS. The diagnosis for depression was made according to the American Diagnostic and Statistical Manual of Mental Disorders Version 5 (DSM-5). The demographic and clinical data were collected including total CSVD burden. On the basis of a multivariate logistic model, the independent factors of early-onset PSD were identified and the predictive nomogram was generated. The performance of the nomogram was evaluated by Harrell's concordance index (C-index) and calibration plot.

Results

A total of 346 patients were enrolled. When contrasted to a 0 score of total CSVD burden, the score ≥2 (moderate to severe total CSVD burden) was an independent risk factor for early-onset PSD. Besides, gender, cognitive impairments, baseline Barthel Index (BI), and plasma fibrinogen were independently associated with early-onset PSD. The nomogram based on all these five independent risk factors was developed and validated with an Area Under Curve (AUC) of 0.780. In addition, the calibration plot revealed an adequate fit of the nomogram in predicting the risk of early-onset depression in patients with AIS.

Conclusions

Our study found the total CSVD burden score of 2–4 points was an independent risk factor of early-onset PSD. The proposed nomogram based on total CSVD burden, gender, cognitive impairments, baseline BI, and plasma fibrinogen concentration gave rise to a more accurate and more comprehensive prediction for early-onset PSD.

A New Nomogram for Predicting the Risk of Intracranial Hemorrhage in Acute Ischemic Stroke Patients After Intravenous Thrombolysis

Background

We aimed to develop and validate a new nomogram for predicting the risk of intracranial hemorrhage (ICH) in patients with acute ischemic stroke (AIS) after intravenous thrombolysis (IVT).

Methods

A retrospective study enrolled 553 patients with AIS treated with IVT. The patients were randomly divided into two cohorts: the training set (70%, n = 387) and the testing set (30%, n = 166). The factors in the predictive nomogram were filtered using multivariable logistic regression analysis. The performance of the nomogram was assessed based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA).

Results

After multivariable logistic regression analysis, certain factors, such as smoking, National Institutes of Health of Stroke Scale (NIHSS) score, blood urea nitrogen-to-creatinine ratio (BUN/Cr), and neutrophil-to-lymphocyte ratio (NLR), were found to be independent predictors of ICH and were used to construct a nomogram. The AUC-ROC values of the nomogram were 0.887 (95% CI: 0.842–0.933) and 0.776 (95% CI: 0.681–0.872) in the training and testing sets, respectively. The AUC-ROC of the nomogram was higher than that of the Multicenter Stroke Survey (MSS), Glucose, Race, Age, Sex, Systolic blood Pressure, and Severity of stroke (GRASPS), and stroke prognostication using age and NIH Stroke Scale-100 positive index (SPAN-100) scores for predicting ICH in both the training and testing sets (p < 0.05). The calibration plot demonstrated good agreement in both the training and testing sets. DCA indicated that the nomogram was clinically useful.

Conclusions

The new nomogram, which included smoking, NIHSS, BUN/Cr, and NLR as variables, had the potential for predicting the risk of ICH in patients with AIS after IVT.

A Prospective Study to Investigate Controlling Blood Pressure Under Transcranial Doppler After Endovascular Treatment in Patients With Occlusion of Anterior Circulation

Objective: The objective of this study was to evaluate the effect of blood pressure (BP) management with transcranial Doppler (TCD) guidance in patients with large-vessel occlusion in the anterior circulation after endovascular thrombectomy (EVT) on the long-term prognosis.

Methods: This was a prospective study; 232 patients were nonrandomized assigned to TCD-guided BP management (TBM) group or non-TCD-guided BP management (NBM) group. In the TBM group, BP was controlled according to TCD showing cerebral blood flow fluctuation. In the NBM group, BP was controlled according to the guidelines. The primary endpoint was a modified Rankin scale (mRS) score of 2 or lower at 90 days. The safety outcomes were the rates of symptomatic or any intracerebral hemorrhage (ICH) and mortality at 90 days.

Results: One hundred sixty-three patients were assigned to the TBM group, and 69 were assigned to the NBM group. In the propensity score-matched cohort (65 matches in both groups), there was significant difference in the proportion of participants with mRS 0–2 at 90 days according to BP management (adjusted odds ratio 3.34, 95% CI 1.36 to 8.22). There was no difference in the rates of symptomatic or any ICH and mortality between two groups. In inverse probability-weighted regression adjustment analysis, mortality decreased significantly in the TBM group than in the NBM group (adjusted odds ratio 0.86, 95% CI 0.76–0.99, p = 0.03).

Conclusion: In patients with acute ischemic stroke from large-vessel occlusion in the anterior circulation, BP management under TCD was superior to NBM in improving the clinical outcomes at 90 days.

Clinical Trial Registration: (URL: https://www.chictr.org.cn/showproj.aspx?proj=55484; Identifier: ChiCTR2000034443.

Acute reperfusion therapies for acute ischemic stroke patients with unknown time of symptom onset or in extended time windows: an individualized approach

Recent randomized controlled clinical trials (RCTs) have revolutionized acute ischemic stroke care by extending the use of intravenous thrombolysis and endovascular reperfusion therapies in time windows that have been originally considered futile or even unsafe. Both systemic and endovascular reperfusion therapies have been shown to improve outcome in patients with wake-up strokes or symptom onset beyond 4.5 h for intravenous thrombolysis and beyond 6 h for endovascular treatment; however, they require advanced neuroimaging to select stroke patients safely. Experts have proposed simpler imaging algorithms but high-quality data on safety and efficacy are currently missing. RCTs used diverse imaging and clinical inclusion criteria for patient selection during the dawn of this novel stroke treatment paradigm. After taking into consideration the dismal prognosis of nonrecanalized ischemic stroke patients and the substantial clinical benefit of reperfusion therapies in selected late presenters, we propose rescue reperfusion therapies for acute ischemic stroke patients not fulfilling all clinical and imaging inclusion criteria as an option in a subgroup of patients with clinical and radiological profiles suggesting low risk for complications, notably hemorrhagic transformation as well as local or remote parenchymal hemorrhage. Incorporating new data to treatment algorithms may seem perplexing to stroke physicians, since treatment and imaging capabilities of each stroke center may dictate diverse treatment pathways. This narrative review will summarize current data that will assist clinicians in the selection of those late presenters that will most likely benefit from acute reperfusion therapies. Different treatment algorithms are provided according to available neuroimaging and endovascular treatment capabilities.