中华心血管病杂志最新文献

Objective: To preliminarily explore the relationship between intraventricular pressure gradients (IVPG) measured by ultrasound hemodynamic analysis and left ventricular cardiotoxicity after anthracycline chemotherapy. Methods: This was a retrospective cohort study. Patients with diffuse large B-cell lymphoma (DLBCL) who completed 6 cycles of R-CHOP chemotherapy at Fudan University Shanghai Cancer Center from 2014 to 2015 were included. Echocardiography was performed at baseline (T0), after 2 cycles of chemotherapy (T1), after 4 cycles of chemotherapy (T2), and after all chemotherapy cycles (T3). Left ventricular global longitudinal strain (LVGLS), left ventricular global circumferential strain (LVGCS), and left ventricular ejection fraction (LVEF) were analyzed using speckle-tracking imaging technology, and IVPG was measured using hemodynamic analysis technology, including IVPG of long-axis (IVPG-LA) and IVPG of short-axis. The change rate of each index from T0 to T2 was marked as Δ. Left ventricular cardiotoxicity was defined as a decrease in LVEF of ≥10% from the baseline level or LVEF ≤50%. Univariate logistic regression analysis was used to explore the related factors of left ventricular myocardial toxicity, and the receiver operating characteristic curve was drawn to analyze their evaluation efficiency for left ventricular myocardial toxicity. Results: A total of 55 patients were included, including 28 males (51%), aged (46.5±11.7) years. Twelve patients (22%) developed left ventricular cardiotoxicity. Compared with T0, IVPG-LA decreased at T1 ((10.73±2.51)% vs. (11.52±3.62)%, P=0.037); while LVGLS, LVGCS, and LVEF only decreased at T3 (all P<0.05). Univariate logistic regression analysis showed that ΔIVPG-LA and ΔLVGLS were related factors for left ventricular myocardial toxicity in patients with DLBCL receiving chemotherapy (all P<0.05). The receiver operating characteristic curve showed that the area under the curve of ΔLVGLS was 0.702, with an optimal cut-off value of 13.15% (sensitivity 66.7%, specificity 62.8%); the area under the curve of ΔIVPG-LA was 0.812, with an optimal cut-off value of 20.74% (sensitivity 75.0%, specificity 90.7%). Conclusions: Hemodynamic analysis technology shows promise clinical application value in evaluating subclinical changes in left ventricular function in tumor patients after anthracycline chemotherapy; the change rate of IVPG-LA could be used as an early indicator of left ventricular toxicity after anthracycline chemotherapy.

Objective: To explore the predictive efficacy of the HFA-ICOS score for cancer therapy-related cardiac dysfunction (CTRCD) in Chinese patients with breast cancer and lymphoma. Methods: This study was a single-center retrospective cohort study which included patients with breast cancer and lymphoma who were treated with anthracyclines from February 2018 to February 2025 at the First Affiliated Hospital of Dalian Medical University. Patients were evaluated at baseline with cardiac biomarkers and echocardiography, including left ventricular ejection fraction and global longitudinal strain of the left ventricle. After anthracycline therapy, they were followed up at 1, 3, 6, and 12 months. Data involved biomarkers and echocardiography were collected to determine whether CTRCD had occurred. The patients were categorized into low-risk, intermediate-risk, high-risk, and very-high-risk groups using the HFA-ICOS scoring model. The cumulative probability of CTRCD under different HFA-ICOS risk stratification was analyzed using Kaplan-Meier survival curves. The effect of HFA-ICOS risk stratification on CTRCD was assessed using an univariate Cox proportional hazards regression model. The predictive efficacy of the HFA-ICOS model and its utility in clinical decision-making were assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curves at each time point. Results: A total of 286 patients, aged 55 (44, 61) years, were enrolled, of whom 33 (11.5%) cases were male. And 113 (39.5%) patients developed CTRCD during a median follow-up time of 111 (70, 210) days. HFA-ICOS risk stratification showed that 228 (79.7%) were low-risk, 49 (17.1%) were intermediate-risk, and a total of 9 (3.1%) were high-risk and very high-risk. The difference in the occurrence of CTRCD over time between patients with different HFA-ICOS risk stratification was statistically significant (P_log-rank<0.001). Cox proportional regression hazards analysis showed an increased risk of CTRCD development in intermediate-risk (HR=1.95, 95%CI 1.22-3.00, P=0.006) and high-risk and very high-risk patients (HR=4.12, 95%CI 1.66-8.54, P=0.004) compared with low-risk patients. The ROC curves showed that the area under the curve of the HFA-ICOS model predicting CTRCD was 0.532, 0.597, 0.600 and 0.577 at 1, 3, 6 and 12 months, respectively. The calibration curves indicated Brier scores of 0.041 (95%CI 0.013-0.067), 0.144 (95%CI 0.115-0.173), 0.232 (95%CI 0.215-0.249) and 0.236 (95%CI 0.220-0.251) at 1, 3, 6 and 12 months, correspondingly. The clinical decision curve suggested that clinical intervention may have a net benefit when the risk threshold is between 0.15 and 0.18 at 1 month, between 0.10 and 0.50 at 3 months, and between 0.30 and 0.70 at 6 and 12 months. Conclusion: The HFA-ICOS score could predict the occurrence of CTRCD in patients with br

Objective: To explore the application value of machine learning algorithms in constructing a predictive model for cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy. Methods: This study was a retrospective cohort study. The female patients with breast cancer who received anthracyclines in the Affiliated Cancer Hospital of Xinjiang Medical University from January 2020 to December 2023 were enrolled. The endpoint event was abnormal electrocardiogram (ECG). According to whether the patients had ECG abnormalities during chemotherapy, they were divided into the ECG abnormal group and the ECG normal group. The dataset was divided into the training set and the test set at a ratio of 8∶2, and logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP) were used to construct a risk prediction model for cardiovascular toxicity in breast cancer patients, and the receiver operating characteristic curve, calibration curve and clinical decision curve were used to evaluate the model. Results: A total of 731 female patients with breast cancer, aged (51.6±9.4) years, were enrolled. The follow-up time was (130.3±37.1) days. There were 333 cases in the ECG abnormal group and 398 cases in the ECG normal group. Seven factors influencing cardiovascular toxicity were identified, including age, menstrual history, diabetes, combination therapy with trastuzumab, combination therapy with dexrazoxane, creatine kinase isoenzymes, and α-hydroxybutyrate dehydrogenase. In the training set, the area under the curve (AUC) for the logistic regression, random forest, XGBoost, SVM, and MLP models was 0.712, 0.863, 0.774, 0.813, and 0.733, respectively. In the test set, the AUC was 0.671, 0.778, 0.746, 0.771, and 0.705, respectively. Calibration curves and clinical decision curves showed that the random forest model performed the best. Conclusion: Models constructed with machine learning algorithms show promise in predicting cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy, with the random forest prediction model performing the best.

Objective: To explore the advantages of internet-based smart healthcare for full-cycle transcatheter edge-to-edge repair (TEER) management in reducing postoperative adverse events rate, improving cardiac function, and enhancing quality of life. Methods: This retrospective study enrolled patients with mitral regurgitation who underwent transcatheter TEER at Beijing Anzhen Hospital Valve Intervention Center between June 2021 and September 2023. Patients were classified into degeneration mitral regurgitation (DMR) and functional mitral regurgitation (FMR) according to etiology, with further stratification by enrollment period into usual care group (June 2021 to October 2022) and full-cycle management group (November 2022 to September 2023). The 1-year postoperative follow-up data were collected and compared between subgroups with the same etiology. Kaplan-Meier survival curves were plotted, and log-rank tests were used to compare the differences in major endpoint event-free survival rates between the two groups. Univariate and multivariate Cox regression and logistic regression analyses were performed to evaluate the impact of the full-cycle management system on patients' outcomes. Results: A total of 130 patients were included, aged (72.0±8.6) years, including 82 (63%) males. DMR was identified in 84 cases (40 in the usual care group and 44 in the full-cycle management group), while FMR was observed in 46 cases (27 in the usual care group and 19 in the full-cycle management group). Kaplan-Meier analysis demonstrated higher 1-year major endpoint event-free survival rates in the full-cycle management group compared to the usual care group, though the difference was not statistically significant (log-rank P>0.05). Compared to the usual care group, the full-cycle management group showed significantly higher proportions of New York Heart Association classification Ⅰ-Ⅱ patients (DMR: 67% vs. 52%, P=0.031; FMR: 68% vs. 52%, P=0.021), greater 6-minute walking distances (DMR: (346.39±70.41) m vs. (294.11±60.47) m, P=0.012; FMR: (356.60±54.68) m vs. (318.55±39.02) m, P=0.004), and superior Kansas City Cardiomyopathy Questionnaire scores (DMR: 81.50 (74.50, 85.00) points vs. 71.00 (66.00, 82.25) points, P=0.014; FMR: 83.00 (76.00, 85.00) points vs. 74.00 (70.75, 80.00) points, P=0.030). Multivariate logistic regression confirmed the full-cycle management system as an independent predictor for the above improved outcomes (all P<0.05). Conclusion: Smart healthcare-based full-cycle management improves cardiac function and quality of life in mitral regurgitation patients after TEER, demonstrating lower rates of major endpoint events compared to usual care.