Clinical trial NCT04979624: Construction and Validation of an In-hospital Mortality Risk Prediction Model for Acute Ischemic Stroke Patients
Official title: Construction and Validation of an In-hospital Mortality Risk Prediction Model for Acute Ischemic Stroke Patients
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Acute Ischemic Stroke
Keywords: In-hospital mortality
Lead sponsor: Shenzhen Second People's Hospital
Sponsor class: OTHER
First posted: 2021-07-28
Enrollment: 234
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Clinical data of acute ischemic stroke patients hospitalized in Shenzhen Second People's Hospital and Shenzhen Longhua District People's Hospital

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

1. ≥18 years old;
2. It meets the diagnostic criteria of China Guidelines for the Diagnosis and Treatment of Acute Ischemic Stroke 2018, and bleeding is confirmed by head MRI or excluded by CT after admission;
3. Admission within 72 hours of onset. -

Exclusion Criteria:

1. Non-vascular causes and transient ischemic attack;
2. with severe hepatic and renal dysfunction;
3. Central nervous system infection, recent history of severe trauma, and malignant tumors affecting survival time;
4. Incomplete main clinical data. -

Primary outcomes:
measure: In-hospital mortality
timeFrame: 7-day in-hospital mortality rates
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
References: