Efficacy of Oral Chinese Medicine in Regulating the Intestinal Flora of Patients with Chronic Heart Failure: A Meta-Analysis

Figures

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  Fig. 1. Development, tuning, internal validation, and external validation sets generation and ECG labeling of left ventricular mass index. Schematic of the data set creation and analysis strategy, which was devised to assure a robust and reliable data set for training, validating, and testing of the network. Once a patient’s data were placed in one of the data sets, that individual’s data were used only in that set, avoiding ‘cross-contamination’ among the training, validation, and test data sets. The details of the flow chart and how each of the data sets was used are described in the methods

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  Fig. 2. Scatter plots of predicted left ventricular mass index (ECG-LVMI) via ECG only compared to the actual LVMI. The x-axis indicates the actual LVMI, and the y-axis presents the ECG-LVMI. Red points represent the highest density, followed by yellow, green light blue, and dark blue. We presented the mean difference, Pearson correlation coefficients (COR), and mean absolute errors (MAE) to demonstrate the accuracy of DLM. The black lines with 95% conference intervals are fitted via simple linear regression

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  Fig. 3. The ROC curve of DLM predictions based on ECG to detect minor and severe left ventricular hypertrophy (LVH). The minor LVH cases are defined as a left ventricular mass index (LVMI) of > 115 in male and > 95 in female, respectively. The severe LVH cases are defined as a LVMI of > 149 in male and > 122 in female, respectively. The operating point was selected based on the maximum of Youden’s index in tuning set and presented using a circle mark, and the area under ROC curve (AUC), sensitivity (Sens.), specificity (Spec.), positive predictive value (PPV), and negative predictive value (NPV) were calculated based on it. The green triangle was the diagnosis based on Philips ECG automatic analysis system

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  Fig. 4. Interpretability and feature attribution of the DL model for LVH prediction. Using the class activation mapping and attention mechanism to explain the AI-ECG prediction, we used white-to-red gradient bars to indicate the importance of each lead (with contribution percentages), and the darker-to-light gradient to indicate the contribution of each temporal position in the prediction of ECG-LVH. In this representative case, the model’s attention is primarily focused on the QRS complexes and ST-T segments, particularly within the lateral precordial leads (V5 and V6) and lead II. This localization aligns with the established clinical criteria for left ventricular hypertrophy, emphasizing the biological plausibility of the model’s diagnostic performance

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  Fig. 5. Long-term incidence of developing a new-onset minor and severe left ventricular hypertrophy (LVH) in patients with an initially normal left ventricular mass index (LVMI) stratified by AI-ECG prediction. Patients in this analysis had an initial LVMI of ≤ 115 in male and ≤ 95 in female, respectively. The AI-ECG predictions were classified as without ECG-LVH, minor ECG-LVH, and severe ECG-LVH based on the operating points as the same with previous ROC curve analysis. The outcomes included new-onset LVH (> 115 in male and > 95 in female) and new-onset severe LVH (> 149 in male and > 122 in female), respectively. The C-index is calculated based on the continuous value combined with sex and age. The analyses are conducted both in internal and external validation sets. The table shows the at-risk population and cumulative risk for the given time intervals in each risk stratification

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  Fig. 6. Kaplan–Meier curves for each severity of electrocardiogram based left ventricular hypertrophy (ECG-LVH) and cardioechogram based LVH (ECHO-LVH) on new-onset complications. This analysis used the same operating points in male and female described previously, and combined male and female into one analysis. We excluded patients with present corresponding disease histories to analyze the new-onset events. The C-index is calculated based on the continuous value combined with sex and age. The analyses are conducted both in internal and external validation sets. The table shows the at-risk population and cumulative risk for the given time intervals in each risk stratification