Clinical Trial Details
— Status: Not yet recruiting
Administrative data
NCT number |
NCT04979624 |
Other study ID # |
20213357006 |
Secondary ID |
|
Status |
Not yet recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
July 1, 2022 |
Est. completion date |
December 31, 2023 |
Study information
Verified date |
April 2022 |
Source |
Shenzhen Second People's Hospital |
Contact |
Gao Yan, Ph.D |
Phone |
13660367430 |
Email |
gaoyanluoyang163[@].com |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Firstly, the application effect of the existing predictive models, SOAR and GWTG-Stroke, was
verified in Guangdong acute ischemic Stroke population, and the clinical application effect
of the existing predictive models was verified.
Secondly, the predictive value of clinical indicators was analyzed, SOAR and GWTG-Stroke
scores were optimized, and an improved prediction Model (New Model) was constructed.
The third is to apply the New Model to clinical practice, collect clinical data and evaluate
the prediction effect of the Model, and evaluate the prediction efficiency of the improved
prediction Model.
Description:
This research is mainly divided into two parts. The first part is to verify and optimize the
existing prediction model. Through continuous collection of clinical data of acute ischemic
Stroke patients hospitalized in Shenzhen Second People's Hospital from January 2017 to
December 2021, including baseline indicators and end point events, based on the existing
prediction model (SOAR, GWTG-Stroke), The predictive probability was calculated and compared
with the actual mortality during hospitalization. The ROC curve, calibration curve and
decision curve were used to evaluate the model's differentiation, calibration and clinical
application value.
Using retrospective data, multivariate logistic regression was used to analyze the predictive
value of baseline clinical indicators, screen risk factors, and optimize the prediction model
of SOAR and GWTG-Stroke.
Extreme Gradient Boosting (XGBOOST) was used to select variables, and logistic regression
model was used based on Akaike Information Criterion.
AIC) was used to construct an improved mortality risk prediction Model (New Model). Decision
curves were used to compare the models. Combined with the clinical significance of the
indicators, the construction of the prediction Model was improved.
The model was validated internally by resampling with computer simulation. The second part is
to evaluate the clinical application effect of the improved prediction Model. The clinical
data of acute ischemic stroke patients hospitalized in Shenzhen Second People's Hospital and
Shenzhen Longhua District People's Hospital from January 2022 to December 2023 are collected
continuously. The New Model is applied in the clinic, and the New Model is validated in the
external time and space.
Evaluate prediction effectiveness and extrapolation.