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A nomogramic model for predicting the left ventricular ejection fraction of STEMI patients after
Shuai Liu1,2,3, Zhihui Jiang1,4,5, Yuanyuan Zhang6
1Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Insights
A new nomogram model accurately predicts early left ventricular ejection fraction (LVEF) in ST-segment elevation myocardial infarction (STEMI) patients after thrombolysis-transfer PCI (TTPCI). This tool aids clinicians in early cardiac function assessment and treatment optimization for STEMI patients.
Area of Science:
- Cardiovascular Medicine
- Interventional Cardiology
- Diagnostic Modeling
Background:
- Prognosis of ST-segment elevation myocardial infarction (STEMI) is strongly correlated with left ventricular ejection fraction (LVEF).
- Thrombolysis-transfer PCI (TTPCI) outcomes are more variable than primary PCI due to multiple influencing factors.
- Predicting early LVEF is crucial for managing STEMI patients undergoing TTPCI.
Purpose of the Study:
- To develop a predictive nomogram model for early LVEF in STEMI patients receiving TTPCI.
- To utilize routine admission indicators for predicting LVEF post-TTPCI.
- To enhance early cardiac function assessment and treatment strategies for STEMI patients.
Main Methods:
- Retrospective analysis of 288 STEMI patients who underwent TTPCI with door-to-balloon time > 120 minutes.
- Statistical analysis included Chi-squared tests, Fisher exact tests, t-tests, and Mann-Whitney U tests.
- A nomogram was developed using All-Subsets Regression and Logistic Regression, validated with ROC curve and Bootstrap methods.
Main Results:
- A nomogram model was developed based on six independent risk factors: age, heart rate (HR), hypertension, smoking history, Alanine aminotransferase (ALT), and Killip class.
- The model demonstrated excellent predictive performance with an Area Under the Curve (AUC) of 0.84.
- The model showed good calibration and discrimination for predicting LVEF ≥ 50% one week post-TTPCI.
Conclusions:
- The developed nomogram accurately predicts early LVEF (≥ 50%) in STEMI patients undergoing TTPCI.
- This tool enables clinicians to perform early cardiac function evaluations.
- Facilitates timely optimization of treatment strategies for improved patient outcomes.
Background:
The prognosis of ST-segment elevation myocardial infarction (STEMI) is closely linked to left ventricular ejection fraction (LVEF). In contrast to primary percutaneous coronary intervention (PPCI), thrombolysis-transfer PCI (TTPCI) is influenced by multiple factors that lead to heterogeneity in cardiac function and prognosis. The aim of this study is to develop a nomogram model for predicting early LVEF in STEMI patients with TTPCI, based on routine indicators at admission.
Method:
We retrospectively reviewed data from patients diagnosed with STEMI at five network hospitals of our PCI center who performed TTPCI as door-to-balloon time (the interval between arrival at the hospital and intracoronary balloon inflation) over 120 min, from February 2018 to April 2022. Categorical variables were analyzed using Pearson χ2 tests or Fisher exact tests, while Student's t-test or Mann-Whitney U-test was used to compare continuous variables. Subsequently, independent risk factors associated with reduced LVEF one week after TTPCI were identified through comprehensive analysis by combining All-Subsets Regression with Logistic Regression. Based on these indicators, a nomogram model was developed, and validated using the area under the receiver operating characteristic (ROC) curve and the Bootstrap method.
Results:
A total of 288 patients were analyzed, including 60 with LVEF < 50% and 228 with LVEF ≥ 50%. The nomogram model based on six independent risk factors including age, heart rate (HR), hypertension, smoking history, Alanine aminotransferase (ALT), and Killip class, demonstrated excellent discrimination with an AUC of 0.84 (95% CI: 0.78-0.89), predicted C-index of 0.84 and curve fit of 0.713.
Conclusions:
The nomogram model incorporating age, HR, hypertension, smoking history, ALT and Killip class could accurately predict the early LVEF ≥ 50% probability of STEMI patients undergoing TTPCI, and enable clinicians' early evaluation of cardiac function in STEMI patients with TTPCI and early optimization of treatment.
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