Cardio-oncology最新文献

Background: Childhood cancer survivors (CCS) often develop late complications after their primary disease is cured. Cardiovascular disease is one of the most frequent and serious complications that significantly affects prognosis and quality of life. Early detection and appropriate intervention are expected to improve their prognosis. However, the risk factors for late cardiotoxicity in CCS are not well defined, and biomarkers that can detect cardiac dysfunction prior to the development of heart failure have not yet been established.

Methods: Medical records of childhood hematologic cancer survivors referred to our department for transitional care between January 2016 and October 2023 were reviewed for this cross-sectional study. The relationships between the most recent cardiac function at the review and history of cancer treatment were analyzed.

Results: This study included 34 patients and the median elapsed time since cancer diagnosis was 16.5 years (range, 5-30 years). None of the patients had symptomatic cardiac complications. The E/e' ratio was significantly higher in survivors with a history of hematopoietic stem cell transplantation (HSCT) than in those who did not undergo HSCT (median, 8.4% vs. 6.25%, P = 0.040), while no intergroup differences were observed in ejection fraction (EF), global longitudinal strain (GLS), or the brain natriuretic protein (BNP) level. In addition, the E/e' ratio was positively correlated with years elapsed since cancer diagnosis (ρ = 0.38, P = 0.034). While there was no clear correlation between years since cancer diagnosis and the BNP level in the overall cohort, a strong correlation was found in patients with a history of HSCT (ρ = 0.73; P < 0.01). No significant differences were observed in EF, E/e' ratio, GLS, and BNP level by cumulative anthracycline dose or history of chest irradiation.

Conclusions: In this study, no patient had late symptomatic cardiac complications. However, in those who had survived for a long time since their cancer diagnosis, particularly those with a history of HSCT, there were significant elevations in the E/e' ratio and the BNP level. Continuous follow-up is required to determine whether these abnormalities lead to symptomatic cardiotoxicity and whether they serve as useful markers for the early detection of cardiac complications.

Background: Breast cancer survivors treated with radiotherapy (RT) face elevated long-term risk of cardiovascular complications. Double product (DP), the product of systolic blood pressure and heart rate, reflects myocardial workload and may serve as a simple prognostic marker. We evaluated whether peak DP during exercise or dobutamine stress echocardiography predicts major adverse cardiovascular events (MACE) and all-cause mortality (ACM).

Methods: We retrospectively studied patients who had breast cancer treated with RT and underwent follow-up testing with ESE or DSE from 2000 through 2022. Univariate and multivariate Cox regression models were used to evaluate the association between MACE/ACM and risk factors in each group. Kaplan-Meier analysis was used to compare ACM and MACE-free survival among the different DP quartiles.

Results: Our study included 696 patients. ESE (n = 425) or DSE (n = 271) was performed at a median of 4.4 (IQR, 2.2-7.5) years after RT. For the ESE group, the rate of MACE was higher in the lowest quartile. Patients in the 3rd and 4th quartile had a decreased risk of MACE and ACM (hazard ratio [HR] per 1,000-mm Hg/min increase, 0.92 [95% CI, 0.88-0.97]; P < .001 and HR, 0.91 [95% CI, 0.87-0.96]; P < .001) respectively. Advanced age (> 60 years) was also seen to be a predictor of MACE in this group (HR, 5.22 [95% CI, 2.13-12.75]; P = < .001). Additionally, treatment with doxorubicin-based chemotherapy was associated with an increased risk of ACM but not MACE (HR, 1.78 [95% CI, 1.04-3.05]; P = .03). For the DSE group, rate of MACE was similar among DP quartiles, indicating that this is not a prognostic indicator. However, a history of diabetes and dyslipidemia were shown to be predictors of MACE (HR, 1.58 [95% CI, 1.05-2.37]; P = .02; and HR, 1.77 [95% CI, 1.12-2.79]; P = .01).

Conclusions: DP achieved during ESE but not DSE is an excellent tool for the prediction of MACE and ACM in this patient population; therefore, ESE should be recommended as the test of choice when possible. A history of atrial fibrillation, diabetes and dyslipidemia were identified as significant predictors of adverse cardiovascular outcomes in the DSE group, highlighting individualized monitoring and early intervention for patients at higher risk.

Background: Anthracycline-induced cardiotoxicity (ACT) is a significant concern for cancer survivors, while genetic basis of ACT remains unclear. This study employs a murine genetic reference population (GRP) of BXD recombinant inbred strains, derived from DBA/2J (D2) and C57BL/6J (B6) crosses, to map quantitative trait loci (QTLs) linked to doxorubicin (DOX)-induced phenotypes through systems genetics approaches.

Methods: To model variability in ACT, 58 BXD strains and parental B6 and D2 mice (n ≥ 4 mice/sex/strain, 3-4-month-old) underwent an intraperitoneal injection of DOX (20 mg/kg). Survival and body weight (BW) were monitored for 10 days. Echocardiography was performed before treatment and on Day 5 post-treatment, followed by genetic mapping and Mendelian randomization analyses for identifying QTLs and candidate genes associated with DOX-induced traits and severity.

Results: Parental B6 strain had 60% survival, whereas 24% of D2 mice survived on Day 10. Among BXD strains, median survival varied, with BXD77 showing the lowest at Day 4. Echocardiography revealed cardiac dysfunction and a small-heart phenotype resembling ACT patients. Significant QTLs on Chromosome 10 (86-94 Mb), Chromosome 19 (52.5-54.2 Mb) and on Chromosome 14 (103-120 Mb) were associated with the survival, mean BW loss, and left ventricular (LV) volumes and ejection fraction (EF%), respectively. MR analysis identified significant causal associations between the genes implicated in BW loss (ADD3, HSPA12 A, SLC18 A2, PDZD8, DUSP5, CASP7) as well as EF% and LV volumes (GPC6, UGGT2, SLAIN1, POU4 F1, MBNL2) in BXD mice post-DOX and heart failure outcomes in humans. Most of the top candidates showed cardiomyocyte specific expression based on scRNA-seq data.

Conclusions: Survival, BW loss, and echocardiography parameters considerably varied among DOX-treated BXDs, suggesting significant influence of genetic background on expression of those traits. Several candidate genes that may modulate ACT susceptibility and heart failure were identified, providing a foundation for genetic-based risk stratification and therapeutics in cardio-oncology.

Introduction: Non-cancer deaths are now becoming a significant threat to the health of cancer patients. Death from atherosclerosis is linked to cancer due to the side effects of treatment and its pathogenesis. However, guidelines for identifying cancer patients at the highest risk of death from atherosclerosis remain unclear. In this study, we aimed to identify the correlation between various outcomes and the risk of death from atherosclerosis as well as to determine which cancer subtypes are linked to a higher risk of mortality from atherosclerosis.

Methods: Data of all patients diagnosed with cancer between 2000 and 2021 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Data regarding the causes of death and clinicopathological features such as sex, age, race, marital status, SEER stage, and treatment procedures were extracted. We calculated standardized mortality ratios (SMRs) using the SEER*Stat software V8.4.3.

Results: Of the 6,891,191 cancer patients, 3,900 (0.057%) died of atherosclerosis, a rate higher than that in the general population (SMR = 1.18, 95% CI [1.15-1.22]). Atherosclerosis-related deaths decreased over time from 1,882 deaths between 2000 and 2004 to 279 deaths between 2015 and 2019. Among the 3,900 atherosclerotic deaths, the highest numbers were observed in patients with digestive cancers (n = 768, 19.7%), particularly colon and rectal cancer (n = 544, 13.9%), prostate cancer (n = 742, 19%), and breast cancer (n = 544, 13.9%). Patients with brain cancer (SMR = 4.96, 95% CI [3.07-7.59]), liver and intrahepatic bile duct cancers (SMR = 3.20, 95% CI [2.24-4.43]), and pancreatic cancer (SMR = 2.69, 95% CI [1.97-3.59]) had a significantly higher rate of death from atherosclerosis than the general population.

Conclusion: Our study revealed a higher atherosclerosis mortality risk among patients with cancer in the United States, emphasizing the need for integrated care that addresses cancer and cardiovascular risks to improve overall patient outcomes. However, our conclusions are restricted to the aggregated data provided by SEER, and we encourage future studies to explore more detailed datasets.

Introduction: Aromatase inhibitors (AIs) have been linked to increased atrial fibrillation(AF) risk due to estrogen depletion however tamoxifen's effect on AF remains conflicting. This study investigates the risk of AF associated with AI use compared to tamoxifen in breast cancer patients.

Methods: A retrospective cohort analysis was conducted using the TriNetX database from 2015 to 2024. Breast cancer patients were categorized into two groups: AI users (anastrozole, exemestane, or letrozole) and tamoxifen users. A propensity score matching (1:1) adjusted for demographics, comorbidities, concurrent therapies, and lab values. The incidence of AF was assessed at 1, 5, and 10-years post-treatment initiation.

Results: The study included 220,552 AI users and 73,388 tamoxifen users before matching, with 54,175 patients in each group after matching. At 1 year, AI users had a higher risk of AF (0.5% vs. 0.4%, RR: 1.36, p = 0.001). At 5 years, AF incidence remained higher in the AI group (1.2% vs. 1.1%, RR: 1.13, p = 0.035).However at 10 years, the difference in AF risk between the two groups was no longer significant (1.6% vs. 1.5%, RR: 1.05, p = 0.295).

Conclusion: AI use is associated with a higher risk of AF than tamoxifen in the first 5 years of treatment, but the risk equalizes at 10 years. Long-term hormonal therapy has an increased risk of AF, highlighting the need for ongoing monitoring and management of risk factors.

Background: The effects of sodium-glucose cotransporter-2 (SGLT2) inhibitors on reducing cardiovascular events in different subgroups of diabetic patients are under investigation. The current systematic review and meta-analysis investigated the effects of SGLT2 inhibitors on preventing cardiovascular events and mortality and their adverse events in patients with active cancer and diabetes undergoing cardiotoxic cancer treatment.

Methods: We searched PubMed, Embase, Web of Science, and Scopus to find studies investigating the effects of SGLT2 inhibitors on patients with diabetes and confirmed cancer until 19 August 2024. Meta-analyses were conducted using the random-effects model to compare all-cause mortality, cancer-associated mortality, heart failure (HF) hospitalization, arrhythmia, and adverse event rates such as ketoacidosis, hypoglycemia, urinary tract infection, and sepsis between patients with or without SGLT2 inhibitors use. Risk ratios (RRs) with 95% confidence intervals (CI) were used to compare outcomes between SGLT2 inhibitors and non-SGLT2 inhibitors groups.

Results: Eleven studies were included with 88,096 patients with confirmed cancer (49% male). Among the total population, 20,538 received SGLT2 inhibitors (age 61.68 ± 10.71), while 67,558 did not receive SGLT2 inhibitors (age 68.24 ± 9.48). The meta-analysis found that the patients who received SGLT2 inhibitors had a significantly lower mortality rate than those who did not receive SGLT2 inhibitors (RR 0.46, 95% CI 0.34 to 0.63, p-value < 0.0001). Similarly, the cancer-associated mortality rate was also lower (RR 0.29, 95% CI 0.27 to 0.30, p-value < 0.0001). Further analysis found that the SGLT2 inhibitor group had a lower rate of HF hospitalization, compared to controls (RR 0.44, 95% CI 0.27 to 0.70, p-value = 0.0007). Moreover, patients receiving SGLT2 inhibitors had a statistically lower rate of arrhythmia (RR 0.38, 95% CI 0.26 to 0.56, p-value < 0.0001). Finally, patients in the SGLT2 inhibitors group had a lower rate of adverse events (RR 0.51, 95% CI 0.42 to 0.62, p-value < 0.0001).

Conclusions: SGLT2 inhibitors are effective in reducing mortality (all-cause and cancer-associated), HF hospitalization, arrhythmia, and drug adverse events in patients with cancer. If confirmed in future studies, these agents could be a potentially ideal candidate to prevent cardiotoxicity of cancer therapies.

Introduction: Global longitudinal strain (GLS) is an important prognostic indicator for predicting heart failure and cancer therapy-related cardiac dysfunction (CTRCD). Although access to GLS measurement has increased across institutions, its actual use in clinical practice remains limited due to practical barriers such as limited time and insufficient training. If reduced GLS could be predicted from conventional echocardiographic parameters, it could help identify patients who would most benefit from direct GLS assessment. Therefore, in this study, we tested the hypothesis that reduced GLS can be predicted from conventional echocardiography via a machine learning (ML) approach.

Methods: This single-center cross-sectional study included patients who visited the Tokyo Metropolitan Tama Medical Center Hospital and underwent echocardiography with GLS before or after anticancer chemotherapy. Low-GLS was defined as a GLS < 16; otherwise, it was defined as Normal-GLS. Patients with EF < 50% were excluded. We developed ML models that predict Low-GLS from conventional echocardiography measurements. Sixteen ML models were constructed including various boosting and tree-based methods. We assessed the models by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, Positive predictive value (PPV), Negative predictive value (NPV), and F1 score. The Shapley Additive exPlanations (SHAP) method was employed to evaluate the essential predictors.

Results: A total of 1,484 patients (64 ± 13 years old, 69% female) were enrolled for ML model development, including 406 patients with Low-GLS and 1,078 with Normal-GLS. The best model for the test dataset was the CatBoost classifier (AUC, 0.748; accuracy, 0.734). Diastolic dysfunction indices [such as septal/lateral mitral annular early diastolic velocity (e') and E-wave to atrial contraction filling velocity (E/A)] and peak velocity‑related parameters [aortic valve peak velocity (AV-Vmax) and left ventricular outflow tract velocity maximum (LVOT-Vmax)] played essential roles in the Low-GLS prediction model.

Conclusion: This study indicated the possibility that Low-GLS might be predicted by machine learning models from conventional echocardiography measurements in cancer patients.

Background: Fluoropyrimidines (FP) are the third most used chemotherapeutic drugs administered in solid tumors but have cardiotoxic side effects. We aimed to determine whether pre-chemotherapeutic cardiological assessment and management of cardiovascular risk factors could prevent FP-induced cardiotoxicity and if the coronary artery calcium (CAC) score was predictive of chest pain.

Methods: This was a randomized, controlled, single center trial of patients with various cancer types who were treated with FP and had no known ischemic heart disease. All patients had CAC score obtained by cardiac CT scan. Patients were randomized to pre-chemotherapeutic cardiological management or standard care. Cardiological management included risk reduction based on electro- and echocardiographic evaluation and blood samples. Primary composite endpoint included hospital admission for chest pain, acute coronary syndrome, coronary angiography intervention, or all-cause mortality. Secondary outcome was chest pain. Follow-up was 6 months. Data were analyzed using Kaplan-Meier survival function with log-rank test and ROC-analyses.

Results: Of the 192 patients included, the primary endpoint occurred in 9/95 (9.5%) patients in the intervention group and 15/97 (15.5%) patients in the control group (log-rank p = 0.19) with an incidence rate ratio (IRR) of 0.57 (95% CI [0.22 - 1.39]). Chest pain occurred in 6/95 (6.3%) patients in the intervention group and 13/97 (13.4%) in the control group, yielding an IRR of 0.44 (95% CI [0.14 - 1.23]). CAC score did not predict chest pain occurrence.

Conclusions: Cardiological management of cardiovascular risk factors prior to treatment with fluoropyrimidines resulted in half as many cardiotoxic events but the study did not reach statistical significance. Further studies are needed to investigate the optimal strategies to prevent fluoropyrimidine-induced cardiotoxicity in cancer patients.

Trial registration: ClinicalTrials.gov Identifyer NCT03486340.

Background: Anthracycline-induced cardiomyopathy is a well-established adverse consequence in childhood cancer survivors. Altered mRNA expression in the peripheral blood has been found at the level of genes and pathways among anthracycline-exposed childhood cancer survivors with and without cardiomyopathy. However, the role of aberrant alternative splicing in anthracycline-induced cardiomyopathy remains unexplored. The present study examined if transcript-specific events, due to alternative splicing occur in anthracycline-exposed childhood cancer survivors with and without cardiomyopathy.

Methods: Participants were anthracycline-exposed childhood cancer survivors with cardiomyopathy (cases) matched with anthracycline-exposed childhood cancer survivors without cardiomyopathy (controls; matched on primary cancer diagnosis, year of diagnosis, and race/ethnicity). mRNA sequencing was performed on total RNA from peripheral blood in 32 cases and 32 matched controls. Event-level splicing tool, rMATS (replicate Multivariate Analysis of Transcript Splicing) was used for quantitative profiling of alternative splicing events.

Results: A total of 45 alternative splicing events in 36 genes were identified. Using a prioritization strategy to filter the alternative splicing events, intron retention in RPS24 and skipped exon of PFND5 showed differential expression of altered transcripts.

Conclusions: We identified specific alternative splicing events in anthracycline-exposed childhood cancer survivors with and without cardiomyopathy. Our findings suggest that differential alternative splicing events can provide additional insight into the peripheral blood transcriptomic landscape of anthracycline-induced cardiomyopathy.

Background: Cardiac involvement is the primary driver of death in systemic light chain (AL) amyloidosis. However, the early prediction of cardiac death risk in AL amyloidosis remains insufficient.

Objectives: We aimed to develop a novel prediction model and prognostic scoring system that enables early identification of these high-risk individuals.

Methods: This study enrolled 235 patients with confirmed AL cardiac amyloidosis from three hospitals. Patients from the first hospital were randomly assigned to the training and internal validation sets in an 8:2 ratio, while the external validation set comprised patients from the other two hospitals. Participants were categorized into a cardiac death group and a non-cardiac death group (including survivors and those who died from other causes). Five different machine learning models were used to train model, and model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.

Results: All five models showed excellent performance on the training and internal validation sets. In external validation, both the Logistic Regression (LR) and Random Forest models achieved an area under the ROC curve of 0.873 and 0.877, respectively, and exhibited superior calibration and decision curve analysis. Considering the comprehensive performance and clinical applicability, the LR model was selected as the final prediction model. The visualization results are ultimately presented in a nomogram. Further analyses were performed on the newly identified predictors.

Conclusions: This prediction model enables early identification and risk assessment of cardiac death in patients with AL amyloidosis, exhibiting considerable predictive ability.