Construction and validation of a nomogram model to predict symptomatic intracranial hemorrhage after intravenous thrombolysis in severe white matter lesions

Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis

BACKGROUND

Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS) and often leads to poor outcomes. Currently, there is no ideal tool for early prediction of HT risk. Recently, machine learning has gained traction in stroke management, prompting the exploration of predictive models for HT. However, systematic evidence on these models is lacking.

OBJECTIVE

In this study, we assessed the predictive capability of machine learning models for HT risk in AIS, aiming to inform the development of HT prediction tools.

METHODS

We conducted a thorough search of medical databases, such as Web of Science, Embase, Cochrane, and PubMed up until March 2025. The risk of bias was determined through the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analysis was performed based on treatment backgrounds, diagnostic criteria, and types of HT.

RESULTS

A total of 83 eligible articles were included, containing 106 models and 88,197 patients with AIS with 9323 HT cases. There were 104 validation sets with a total c-index of 0.832 (95% CI 0.814-0.849), sensitivity of 0.82 (95% CI 0.79-0.84), and specificity of 0.78 (95% CI 0.74-0.81). Subgroup analysis indicated that the combined model achieved superior prediction accuracy. Moreover, we also analyzed the predictive performance of 6 mature models.

CONCLUSIONS

Currently, although several prediction methods for HT have been developed, their predictive values are not satisfactory. Fortunately, our findings suggest that machine learning methods, particularly those combining clinical features and radiomics, hold promise for improving predictive accuracy. Our meta-analysis may provide evidence-based guidance for the subsequent development of more efficient clinical predictive models for HT.

TRIAL REGISTRATION

PROSPERO CRD42024498997; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024498997.

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).

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.