Influencing factors analysis of clinical effect of heart failure patients treated with ivabradine and metoprolol succinate and construction and validation of nomogram prediction model

TL;DR

This study identifies key factors affecting heart failure treatment with ivabradine and metoprolol succinate and builds a predictive model to guide clinical decisions.

Contribution

A novel nomogram prediction model is developed and validated for heart failure treatment outcomes using ivabradine and metoprolol succinate.

Findings

LVEF, LVEDD, 6 MWT, heart rate, and BNP level are independent risk factors for treatment outcomes.

The nomogram model showed high accuracy with ROC AUC of 0.862 in the training set and 0.819 in the testing set.

Calibration and DCA analyses confirmed the model's clinical applicability and consistency.

Abstract

To analyze the influencing factors of the clinical effect of ivabradine (Ivab) combined with metoprolol succinate (Met-S) in patients with heart failure (HF), and to construct and verify the nomogram prediction model, in order to provide reference for clinical treatment.

250 cases of HF patients from January 2021 to June 2023 were selected. The relevant factors affecting the therapeutic effect were screened out through univariate and multivariate analysis. The nomogram prediction model was constructed, and the model was verified and evaluated using receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA).

Single factor and multiple factor analyses showed that LVEF, LVEDD, 6 MWT, heart rate and BNP level were the independent risk factors for clinical effects (P < 0.05). In the training and testing sets, the area under the ROC curves were 0.862 (95% CI: 0.776–0.947) and 0.819 (95% CI: 0.704–0.934), respectively. The calibration curve showed good consistency, and DCA analysis indicated that the model had clinical application value.

LVEF, LVEDD, 6 MWT, heart rate and BNP level affect the clinical effect of Ivab combined with Met-S in patients with HF. The nomogram prediction model established has high accuracy and clinical application value.

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