A novel nomogram to predict hemorrhagic transformation in ischemic stroke patients after intravenous thrombolysis

Predictive value of CT imaging features on the risk of hemorrhagic transformation after mechanical thrombectomy for acute ischemic stroke with large vessel obstruction

OBJECTIVE

To investigate the predictive value of computer tomography (CT) imaging features for the risk of hemorrhagic transformation (HT) after mechanical thrombectomy for acute ischemic stroke with large vessel obstruction (AIS-LVO).

METHODS

A total of 135 patients with AIS-LVO diagnosed and treated in our hospital from August 2021 to May 2023 were selected as the research subjects. Their clinical data were retrospectively analyzed. Mechanical thrombectomy was performed in all patients. The patients were divided into the HT group (n = 27) and the non-HT group (n = 108) according to whether HT occurred within 24 h after thrombectomy. CT examination was performed after mechanical thrombectomy in the two groups, and the changes in CT imaging indexes in the two groups were observed. Logistic regression was used to analyze the influencing factors and a prediction model was constructed based on the influencing factors. The receiver operating characteristic (ROC) curve was established to analyze the predictive value. Additionally, ROC curve was used to analyze the diagnostic value of serum CT imaging features.

RESULTS

Compared with the non-HT group, the proportion of atrial fibrillation history in the HT group was significantly increased, and the National Institute of Health Stroke Scale (NIHSS) score and galectin-3 (Gal-3) level were significantly increased before thrombectomy (P < 0.01). Compared with the non-HT group, the proportion of exudation of contrast medium and Hyperdense Middle Cerebral Artery Sign (HMCAS) in the HT group was significantly increased, time to peak (TTP) was significantly prolonged, and cerebral blood flow (CBF) was significantly decreased (P < 0.001). The history of atrial fibrillation, NIHSS score before thrombectomy, Gal-3, contrast agent exudation, HMCAS, TTP and CBF were the influencing factors of postoperative HT after mechanical thrombectomy in AIS-LVO (P < 0.05). Based on the results of multivariate logistic regression analysis, a prediction model was established as follows: Logit (P) = -3.520 + 1.529 × history of atrial fibrillation + 0.968 × NIHSS score before thrombectomy + 0.806 × Gal-3 + 1.134 × contrast agent exudation + 2.146 × HMCAS + 0.684 × TTP-0.725 × CBF. The area under the curve (AUC) of the logistic prediction model for predicting HT after AIS-LVOLVO mechanical thrombectomy was 0.873 (95% CI 0.817-0.929) with a sensitivity of 78.75% and a specificity of 83.33%, indicating that the prediction model had good prediction efficiency. The AUC of TTP and CBF alone in predicting HT after mechanical thrombectomy in AIS-LVO patients was 0.728 and 0.736, respectively. The AUC of combined detection was 0.783, and the combined detection had a high diagnostic value for HT after mechanical thrombectomy in AIS-LVO patients.

CONCLUSION

The combined detection of TTP and CBF of CT imaging features had certain diagnostic value for HT in AIS-LVO patients after mechanical thrombectomy. The logistic prediction model based on these influencing factors had a high diagnostic value for HT after mechanical thrombectomy.

A New Perspective on the Prediction and Treatment of Stroke: The Role of Uric Acid

Stroke, a major cerebrovascular disease, has high morbidity and mortality. Effective methods to reduce the risk and improve the prognosis are lacking. Currently, uric acid (UA) is associated with the pathological mechanism, prognosis, and therapy of stroke. UA plays pro/anti-oxidative and pro-inflammatory roles in vivo. The specific role of UA in stroke, which may have both neuroprotective and damaging effects, remains unclear. There is a U-shaped association between serum uric acid (SUA) levels and ischemic stroke (IS). UA therapy provides neuroprotection during reperfusion therapy for acute ischemic stroke (AIS). Urate-lowering therapy (ULT) plays a protective role in IS with hyperuricemia or gout. SUA levels are associated with the cerebrovascular injury mechanism, risk, and outcomes of hemorrhagic stroke. In this review, we summarize the current research on the role of UA in stroke, providing potential targets for its prediction and treatment.

Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis

BACKGROUND

Haemorrhagic transformation (HT) is a severe complication after ischaemic stroke, but identifying patients at high risks remains challenging. Although numerous prediction models have been developed for HT following thrombolysis, thrombectomy, or spontaneous occurrence, a comprehensive summary is lacking. This study aimed to review and compare traditional and machine learning-based HT prediction models, focusing on their development, validation, and diagnostic accuracy.

METHODS

PubMed and Ovid-Embase were searched for observational studies or randomised controlled trials related to traditional or machine learning-based models. Data were extracted according to Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Performance data for prediction models that were externally validated at least twice and showed low risk of bias were meta-analysed.

RESULTS

A total of 100 studies were included, with 67 focusing on model development and 33 on model validation. Among 67 model development studies, 44 were traditional model studies involving 47 prediction models (with National Institutes of Health Stroke Scale score being the most frequently used predictor in 35 models), and 23 studies focused on machine learning prediction models (with support vector machines being the most common algorithm, used in 10 models). The 33 validation studies externally validated 34 traditional prediction models. Regarding study quality, 26 studies were assessed as having a low risk of bias, 11 as unclear, and 63 as high risk of bias. Meta-analysis of 15 studies validating eight models showed a pooled area under the receiver operating characteristic curve of approximately 0.70 for predicting HT.

CONCLUSION

While significant progress has been made in developing HT prediction models, both traditional and machine learning-based models still have limitations in methodological rigour, predictive accuracy, and clinical applicability. Future models should undergo more rigorous validation, adhere to standardised reporting frameworks, and prioritise predictors that are both statistically significant and clinically meaningful. Collaborative efforts across research groups are essential for validating these models in diverse populations and improving their broader applicability in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

International Prospective Register of Systematic Reviews (CRD42022332816).

Comprehensive predictive model for cerebral microbleeds: integrating clinical and biochemical markers

Background

Cerebral Microbleeds (CMBs) serve as critical indicators of cerebral small vessel disease and are strongly associated with severe neurological disorders, including cognitive impairments, stroke, and dementia. Despite the importance of diagnosing and preventing CMBs, there is a significant lack of effective predictive tools in clinical settings, hindering comprehensive assessment and timely intervention.

Objective

This study aims to develop a robust predictive model for CMBs by integrating a broad range of clinical and laboratory parameters, enhancing early diagnosis and risk stratification.

Methods

We analyzed extensive data from 587 neurology inpatients using advanced statistical techniques, including Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression. Key predictive factors such as Albumin/Globulin ratio, gender, hypertension, homocysteine levels, Neutrophil to HDL Ratio (NHR), and history of stroke were evaluated. Model validation was performed through Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA).

Results

The model demonstrated strong predictive performance with significant clinical applicability. Key predictors identified include the Albumin/Globulin ratio, homocysteine levels, and NHR, among others. Validation metrics such as the area under the ROC curve (AUC) and decision curve analysis confirmed the model's utility in predicting CMBs, highlighting its potential for clinical implementation.

Conclusion

The comprehensive predictive model developed in this study offers a significant advancement in the personalized management of patients at risk for CMBs. By addressing the gap in effective predictive tools, this model facilitates early diagnosis and targeted intervention, potentially reducing the incidence of stroke and cognitive impairments associated with cerebral microbleeds. Our findings advocate for a more nuanced approach to cerebrovascular disease management, emphasizing the importance of multi-factorial risk profiling.

A nomogram for predicting hemorrhagic transformation in acute ischemic stroke receiving intravenous thrombolysis with extended time window

A recent randomized controlled clinical trial expanded the time window of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS) beyond 4.5 hours by applying neuroimaging standards, enabling more patients to benefit from IVT. However, hemorrhagic transformation (HT) after IVT is still a major concern. We aimed to develop a nomogram to predict HT in AIS patients receiving IVT with extended time window. We aimed to develop a nomogram to predict HT in AIS patients receiving IVT with extended time window. Patients with AIS receiving IVT with extended time window from March 2017 to April 2023 were retrospectively reviewed. They were divided into the HT group and the non-HT group based on computed tomography. Logistic regression analysis was used to screen the predictive factors for HT. A nomogram was developed based on the predictive factors. The predictive accuracy of the nomogram was assessed by the area under the curve (AUC) of the receiver operating characteristic curve (ROC). A calibration plot was used to evaluate the calibration of the nomogram. A decision curve analysis (DCA) was used to test the clinical value. A total of 210 patients were enrolled, and 44 patients (21.0%) had HT. Onset to needle time (ONT) (OR = 1.002, 95% CI: 1.000-1.004, P = .016), atrial fibrillation (OR = 2.853, 95% CI: 1.072-7.594, P = .036), and baseline NIHSS (OR = 1.273, 95% CI: 1.159-1.399, P = .000) were predictive factors of HT. The AUC of the nomogram was 0.833 (95% CI: 0.7623-0.9041), with a sensitivity of 78.9% and specificity of 77.3%. The calibration curve indicates that predicted results of the nomogram were in good agreement with the actual observation results. The DCA showed the nomogram had good clinical applicability in predicting HT. We developed an easy-to-use nomogram to predict HT in AIS patients receiving IVT with extended time window. It could be a potential tool to stratify the risk of HT for patients beyond 4.5 hours of onset who may undergo IVT.

The HALP (hemoglobin, albumin, lymphocyte, and platelet) score is associated with hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke

Background

Hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) with rt-PA can precipitate rapid neurological deterioration, poor prognosis, and even death. The HALP score (hemoglobin, albumin, lymphocyte, and platelet) is a novel indicator developed to reflect both systemic inflammation and the nutritional status of patients. The goal of this study was to reveal the relationship between the HALP score and the risk of HT after IVT in people with acute ischemic stroke (AIS).

Methods

A total of 753 patients with AIS were included in this study. Patients were divided into quartiles according to baseline HALP score. The HALP score was calculated as follows: hemoglobin (g/L) × albumin (g/L) × lymphocytes (/L)/platelets (/L). Binary logistic regression was used to reveal the connection between HALP score and HT.

Results

The baseline HALP score were significantly lower in the HT than non-HT patients (p < 0.001). The HALP score were divided into four quartiles: Q1 (<27.4), Q2 (27.4-37.6), Q3 (37.7-49.6), Q4 (>49.6), respectively. Moreover, the severity of HT increased with decreasing HALP level (p < 0.001). In multivariable logistic regression, taking the Q4 as the reference, the association between Q1 and HT remained, after adjusting for confounding variables [odds ratio (OR) = 3.197, 95% confidence interval (CI) = 1.634-6.635, p = 0.003].

Conclusion

The HALP value can predict the HT risk after IVT in patients with AIS. A lower HALP level was associated with an increased severity of HT post-IVT.

Developing a nomogram model for 3-month prognosis in patients who had an acute ischaemic stroke after intravenous thrombolysis: a multifactor logistic regression model approach

OBJECTIVES

This study is to establish a nomination graph model for individualised early prediction of the 3-month prognosis of patients who had an acute ischaemic stroke (AIS) receiving intravenous thrombolysis with recombinant tissue plasminogen activator.

DESIGN

For the period from January 2016 through August 2022, 991 patients who had an acute stroke eligible for intravenous thrombolysis were included in the retrospective analysis study. The study was based on multifactor logistic regression.

PARTICIPANTS

Patients who received treatment from January 2016 to February 2021 were included in the training cohort, and those who received treatment from March 2021 to August 2022 were included in the testing cohort.

INTERVENTIONS

Each patient received intravenous thrombolysis within 4.5 hours of onset, with treatment doses divided into standard doses (0.9 mg/kg).

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measure was a 3-month adverse outcome (modified Rankin Scale 3-6).

RESULTS

The National Institutes of Health Stroke Scale Score after thrombolysis (OR=1.18; 95% CI: 1.04 to 1.36; p = 0.015), door-to-needle time (OR=1.01; 95% CI: 1.00 to 1.02; p = 0.003), baseline blood glucose (OR=1.08; 95% CI: 1.00 to 1.16; p=0.042), blood homocysteine (OR=7.14; 95% CI: 4.12 to 12.71; p<0.001), monocytes (OR=0.05; 95% CI: 0.01 to 0.043; p=0.005) and monocytes/high-density lipoprotein (OR=62.93; 95% CI: 16.51 to 283.08; p<0.001) were independent predictors of adverse outcomes 3 months after intravenous thrombolysis, and the above six factors were included in the nominated DGHM2N nomogram. The area under the receiver operating characteristic curve value of the training cohort was 0.870 (95% CI: 0.841 to 0.899) and in the testing cohort was 0.822 (95% CI: 0.769 to 0.875).

CONCLUSIONS

A reliable nomogram model (DGHM2N model) was developed and validated in this study. This nomogram could individually predict the adverse outcome of patients who had an AIS receiving intravenous thrombolysis with alteplase for 3 months.

Predictive value of the BDH2-MN2 nomogram model for prognosis at 3 months after receiving intravenous thrombolysis in patients with acute ischemic stroke

Introduction

The present study focused on developing a nomogram model to predict the 3-month survival of patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis with tissue plasminogen activator (tPA).

Material and methods

A total of 709 patients were enrolled in the present study, including 496 patients in the training set and 213 patients in the validation set. All data were statistically analyzed using R software. We applied LASSO regression analysis to construct nomograms by screening statistically significant predictors from all variables.The model discrimination was evaluated based on the area under the receiver operating characteristic curve (AUC-ROC).

Results

LASSO regression analysis was conducted for all variables, which revealed BNP, DNT, HCY, HDL, MHR, NHR and post-thrombolysis NIHSS as independent predictors of adverse outcomes at 3 months after intravenous thrombolysis. Accordingly, these seven factors were incorporated in the nominated BDH2-MN2 nomogram. The resulting AUC-ROC values determined for the training and validation sets were 0.937 (95% CI: 0.822-0.954) and 0.898 (95% CI: 0.748-0.921), respectively.

Conclusions

A robust BDH2-MN2 (BNP, DNT, HCY, HDL, MHR, NHR and post-thrombolysis NIHSS) nomogram model was successfully developed and validated. The developed nomogram enables prediction of adverse outcomes of individual AIS patients receiving intravenous thrombolysis with alteplase for 3 months.

Serum Uric Acid Levels and Outcome of Acute Ischemic Stroke: a Dose-Response Meta-analysis

Previous meta-analyses have reported conflicting results regarding the relationship between baseline uric acid (UA) levels and acute ischemic stroke (AIS) outcomes. Therefore, we conducted a dose-response meta-analysis to elucidate the association's strength and shape. Studies on the association between baseline UA levels and AIS outcomes in the PubMed and EMBASE databases were searched from their inception to April 17, 2023. Two researchers independently reviewed the studies for inclusion. A total of 23 articles involving 15,733 patients with AIS were included. The analysis revealed a significant inverse correlation between UA levels and AIS outcomes. The linear trend estimation indicated that a 50-μmol/L increment in UA level was associated with a 21.7% lower risk of hemorrhagic transformation (odds ratio [OR]: 0.783; 95% confidence interval [CI]: 0.743, 0.826; I2 = 43.4%; n = 4), 7.0% lower risk of 90-day unfavorable outcome [modified Rankin scale score ≥ 2] (OR: 0.930; 95% CI: 0.875, 0.990; I2 = 0%; n = 3), and 7.5% lower risk of 90-day poor outcome [modified Rankin scale score ≥ 3] (OR: 0.925; 95% CI: 0.863, 0.990; I2 = 74.4%; n = 3) in patients with AIS after accounting for relevant covariates. A linear dose-response relationship exists between baseline UA levels and the outcome of patients with AIS within a certain range, with higher baseline UA levels associated with better outcomes after AIS. Further dose-response meta-analyses, including a larger number of original articles, are required to validate our findings.

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.