Establishment and Validation of a Predictive Model for Hemorrhage

Stroke, Acute Clinical Trial

Official title: Establishment and Validation of a Predictive Model for Hemorrhage After Intravenous Thrombolysis in Acute Ischemic Stroke

Status Not yet recruiting

Clinical Trial Summary

Background: Patients with acute ischemic stroke (AIS) are at risk of hemorrhagic transformation (HT) after intravenous thrombolysis. Although there is a risk assessment model for hemorrhagic transformation after thrombolysis, there is no evidence of clinical application in the population of Guangdong Province. .

Purpose: To verify the clinical application effect of the existing risk assessment model for hemorrhage transformation after thrombolysis in the local population; to improve the existing prediction model and verify the predictive value of HT after intravenous thrombolysis.

Methods: (1) Continuously collect AIS patients who received intravenous thrombolysis in our hospital from January 2014 to December 2020 to verify the clinical application effects of three existing models (HAT, SIT-sICH, THRIVE) on bleeding transformation. Collect baseline and bleeding transformation information within 7 days after thrombolysis, and use ROC curve, calibration curve, sensitivity and specificity to evaluate the prediction effect. A logistic regression model was used to construct an improved HT prediction model based on the AIC principle; (2) Continuous collection of AIS patients who received intravenous thrombolysis in two local hospitals from January 2021 to December 2022 for internal and external verification.

Expected results: (1) Evaluate the clinical application value of the existing prediction model in local AIS patients with intravenous thrombolysis; (2) Develop a modified risk assessment model suitable for hemorrhage transformation after intravenous thrombolysis in AIS patients in Guangdong area, and evaluate the risk early Provide guarantee for clinical diagnosis and treatment.

Clinical Trial Description

This study has two main parts. The first part is to verify and optimize the clinical application effect of the existing prediction model. The clinical data of the acute ischemic stroke intravenous thrombolytic population is collected retrospectively, mainly including baseline indicators and 7 days after thrombolysis Internal bleeding, based on the existing prediction models (HAT, SIT-sICH, THRIVE), calculate the prediction probability, and compare it with the actual bleeding situation, evaluate the clinical application effect of the prediction model, use ROC curve, calibration curve, sensitivity and Evaluation of indicators such as specificity. Using retrospective data, using multivariate logistic regression to analyze the predictive value of baseline clinical indicators, screening risk factors, and optimizing the HAT, SIT-sICH, and THRIVE prediction models. The logistic regression model is used to construct an improved HT prediction model based on the AIC principle; the method of model comparison is used to combine the clinical significance of the indicators to complete the construction of the prediction model. The second part is to evaluate the clinical application effect of the improved prediction model, and prospectively collect clinical data of AIS patients undergoing intravenous thrombolysis in Shenzhen Second People's Hospital, Shenzhen Longhua District People's Hospital, including general demographic data and laboratory tests Baseline indicators such as imaging examinations, bleeding within 7 days after thrombolysis, etc., were used to verify the improved HT prediction model using ROC curve, calibration curve, sensitivity and specificity, and external verification was performed to evaluate the prediction effect of the model. ;

Related Conditions & MeSH terms

• Hemorrhage
• Stroke
• Stroke, Acute

• NCT number: NCT04745052
• Study type: Observational
• Source: Shenzhen Second People's Hospital
• Contact: Xie Xiaohua, master
• Phone: +86 13560779836
• Email: 13560779836@163.com
• Status: Not yet recruiting
• Start date: March 1, 2021
• Completion date: July 1, 2023