Kunlun He
Chinese PLA General Hospital, China
Title: Prediction model of in-hospital adverse cardiac events in patients with heart failure
Biography
Biography: Kunlun He
Abstract
Objectives: Early prediction and identification of the onset of adverse cardiac events in high-risk patients are of great significance for preemptive treatment and a better prognosis. We sought to establish a risk evaluation model to predict the adverse cardiac events during hospitalization in patients with heart failure (HF). Methods: In-hospital, patients with HF was randomly selected from intensive care units of Chinese PLA General Hospital. Patients were then allocated into model derivation group and validation group, respectively. In the derivation group, independent risk factors for adverse cardiac events were evaluated by multivariate logistic regression. We established a prediction score system using the independent risk factors. In the validation group, receiver operator characteristic curve (ROC) and C-statistic testing were utilized to assess the performance of the constructed model in comparison with a previous published Modified Early Warning Score (MEWS) model. Results: The binary logistic regression analysis revealed that the level of heart rate, left ventricular ejection fraction, pH value, renal dysfunction and NT-pro BNP are independent risk factors of adverse cardiac events during hospitalization for HF patients. The effectiveness of our risk prediction score system (PSS) is better than modified early warning score (MEWS) system. Conclusions: Through data analysis of patients with heart failure, we found heart rate, left ventricular ejection fraction, pH value, renal dysfunction and NT-pro BNP were closely associated with adverse cardiac events during hospitalization. It has important significance for the precision risk stratification of in-hospital patients with heart failure.