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A predictive model for adverse events in left heart failure patients based on cardiopulmonary ultrasound parameters
Yanfen Zhong1, Ji Wu2, Hongyu Zheng1
1Department of Ultrasound, Guangxi Academy of Medical Sciences & The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Background:
Left heart failure (LHF) is the most common in clinical setting and is more likely to experience adverse cardiovascular events. This study aims to establish a predictive nomogram of adverse events based on cardiopulmonary ultrasound (CPUS) parameters and clinical predictors for LHF patients. And the predictive performance of the nomogram was assessed.
Methods:
Potential risk factors associated with the occurrence of adverse events in LHF were analyzed using one-way analysis of variance (ANOVA) or univariate logistic regression. A multivariate logistic regression model was subsequently constructed, and the results were visualized with a nomogram. Then the predictive ability and clinical application value of the nomogram were verified by the concordance index (C-index), calibration curve and decision curve.
Results:
A total of 329 heart failure patients including 278 males and 51 females, with an average age of (53.62±15.85) years were enrolled in this study. Independent influencing factors of adverse events were identified through multivariate logistic regression. Finally, four indicators were included, namely, systolic pulmonary artery pressure (sPAP) [odds ratio (OR) =15.53, 95% confidence interval (CI): 1.53-157.19, P=0.02], left atrial transverse diameter (LAD) (OR =1.190, 95% CI: 1.031-1.373, P=0.02), E/e' ratio of left ventricle (LV-E/e') (OR =1.241, 95% CI: 1.101-1.399, P<0.001), and the number of B lines (OR =1.591, 95% CI: 1.123-2.254, P=0.009). The C-index of the nomogram model for predicting the risk of adverse events in LHF patients were 0.888 (95% CI: 0.826-0.950). Furthermore, the calibration curve demonstrated great consistency between the predicted probabilities and the observed outcomes, and the decision curve analysis confirmed the important clinical advantage of the model in LHF patients.
Conclusions:
CPUS may provide valuable prognostic information in patients with LHF. The constructed prediction model for adverse events, incorporating CPUS parameters including sPAP, LAD, LV-E/e', and number of B lines, demonstrated favorable discriminative ability and accuracy within the study cohort.
