Diagnostic Model for In-Hospital Bleeding in Patients with Acute ST-Segment Elevation Myocardial Infarction:
1Emergency and Critical Care Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
This study developed and validated a diagnostic model to predict in-hospital bleeding in patients with ST-segment elevation myocardial infarction (STEMI). Advanced age and Killip classification were key predictors, aiding risk assessment for better patient outcomes.
Area of Science:
- Cardiology
- Internal Medicine
- Clinical Diagnostics
Background:
- Bleeding complications in acute ST-segment elevation myocardial infarction (STEMI) are linked to adverse outcomes.
- Predicting and managing in-hospital bleeding is crucial for STEMI patient care.
Purpose of the Study:
- To develop a diagnostic model for predicting in-hospital bleeding in STEMI patients.
- To externally validate the developed diagnostic model using independent datasets.
Main Methods:
- Multivariate logistic regression analysis of a large cohort of hospitalized STEMI patients (n=4262 for development, n=6015 for validation).
- Identification of risk factors including age and Killip classification.
- Construction of a nomogram and assessment of model performance using discrimination, calibration, and decision curve analysis (DCA).
Main Results:
- In-hospital bleeding occurred in 2.6% of the development cohort and 1.9% of the validation cohort.
- Advanced age (OR 1.047) and high Killip classification (III: OR 3.265, IV: OR 5.133) were significant predictors.
- The diagnostic model demonstrated satisfactory performance with an AUC of 0.777 in the development set and 0.723 in the validation set.
Conclusions:
- A reliable diagnostic model for in-hospital bleeding in STEMI patients was successfully developed and validated.
- The model, incorporating age and Killip classification, aids in predicting bleeding risk.
- The validated model shows potential for improving clinical decision-making and patient management in STEMI.
Background:
Bleeding complications in patients with acute ST-segment elevation myocardial infarction (STEMI) have been associated with increased risk of subsequent adverse consequences.
Objective:
The objective of our study was to develop and externally validate a diagnostic model of in-hospital bleeding.
Methods:
We performed multivariate logistic regression of a cohort for hospitalized patients with acute STEMI in the emergency department of a university hospital. Participants: The model development data set was obtained from 4262 hospitalized patients with acute STEMI from January 2002 to December 2013. A set of 6015 hospitalized patients with acute STEMI from January 2014 to August 2019 were used for external validation. We used logistic regression analysis to analyze the risk factors of in-hospital bleeding in the development data set. We developed a diagnostic model of in-hospital bleeding and constructed a nomogram. We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA).
Results:
In-hospital bleeding occurred in 112 of 4262 participants (2.6%) in the development data set. The strongest predictors of in-hospital bleeding were advanced age and high Killip classification. Logistic regression analysis showed differences between the groups with and without in-hospital bleeding in age (odds ratio [OR] 1.047, 95% CI 1.029-1.066; P<.001), Killip III (OR 3.265, 95% CI 2.008-5.31; P<.001), and Killip IV (OR 5.133, 95% CI 3.196-8.242; P<.001). We developed a diagnostic model of in-hospital bleeding. The area under the receiver operating characteristic curve (AUC) was 0.777 (SD 0.021, 95% CI 0.73576-0.81823). We constructed a nomogram based on age and Killip classification. In-hospital bleeding occurred in 117 of 6015 participants (1.9%) in the validation data set. The AUC was 0.7234 (SD 0.0252, 95% CI 0.67392-0.77289).
Conclusions:
We developed and externally validated a diagnostic model of in-hospital bleeding in patients with acute STEMI. The discrimination, calibration, and DCA of the model were found to be satisfactory.
Trial Registration:
ChiCTR.org ChiCTR1900027578; http://www.chictr.org.cn/showprojen.aspx?proj=45926.
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