Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images.

Background: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. Objectives: To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging risk biomarkers, and its association with CTRCD. Methods: Across a five-hospital U.S.-based health system (2013-2023), we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), and a control cohort receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (≥0.1, positive screen) and explored its association with i) global longitudinal strain (GLS) measured within 15 days (n=7,271 pairs); ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction [LVEF]<50%), and LVEF<40%. In the ICI cohort we correlated baseline AI-ECG-LVSD predictions with downstream myocarditis. Results: Higher AI-ECG LVSD predictions were associated with worse GLS (-18% [IQR:-20 to -17%] for predictions<0.1, to -12% [IQR:-15 to -9%] for ≥0.5 (p<0.001)). In 1,308 patients receiving AC/TZM (age 59 [IQR:49-67] years, 999 [76.4%] women, 80 [IQR:42-115] follow-up months) a positive baseline AI-ECG LVSD screen was associated with ~2-fold and ~4.8-fold increase in the incidence of the composite CTRCD endpoint (adj.HR 2.22 [95%CI:1.63-3.02]), and LVEF<40% (adj.HR 4.76 [95%CI:2.62-8.66]), respectively. Among 2,056 patients receiving ICI (age 65 [IQR:57-73] years, 913 [44.4%] women, follow-up 63 [IQR:28-99] months) AI-ECG predictions were not associated with ICI myocarditis (adj.HR 1.36 [95%CI:0.47-3.93]). Conclusion: AI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.

Use of Smart Devices to Track Cardiovascular Health Goals in the United States.

BACKGROUND: Smartphone-based health applications are increasingly popular, but their real-world use for cardiovascular risk management remains poorly understood. OBJECTIVES: The purpose of this study was to investigate the patterns of tracking health goals using smart devices, including smartphones and/or tablets, in the United States. METHODS: Using the nationally representative Health Information National Trends Survey for 2017 to 2020, we examined self-reported tracking of health-related goals (optimizing body weight, increasing physical activity, and/or quitting smoking) using smart devices among those with cardiovascular disease (CVD) or cardiovascular risk factors of hypertension, diabetes, obesity, and/or smoking. Survey analyses were used to obtain national estimates of use patterns and identify features associated with the use of these devices for tracking health goals. RESULTS: Of 16,092 Health Information National Trends Survey participants, 10,660 had CVD or cardiovascular risk factors, representing 154.2 million (95% CI: 149.2-159.3 million) U.S. adults. Among the general U.S. adult population, 46% (95% CI: 44%-47%) tracked their health goals using their smart devices, compared with 42% (95% CI: 40%-43%) of those with or at risk of CVD. Younger age, female, Black race, higher educational attainment, and greater income were independently associated with tracking of health goals using smart devices. CONCLUSIONS: Two in 5 U.S. adults with or at risk of CVD use their smart devices to track health goals. While representing a potential avenue to improve care, the lower use of smart devices among older and low-income individuals, who are at higher risk of adverse cardiovascular outcomes, requires that digital health interventions are designed so as not to exacerbate existing disparities.

Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020.

Importance: Wearable devices may be able to improve cardiovascular health, but the current adoption of these devices could be skewed in ways that could exacerbate disparities. Objective: To assess sociodemographic patterns of use of wearable devices among adults with or at risk for cardiovascular disease (CVD) in the US population in 2019 to 2020. Design, Setting, and Participants: This population-based cross-sectional study included a nationally representative sample of the US adults from the Health Information National Trends Survey (HINTS). Data were analyzed from June 1 to November 15, 2022. Exposures: Self-reported CVD (history of heart attack, angina, or congestive heart failure) and CVD risk factors (≥1 risk factor among hypertension, diabetes, obesity, or cigarette smoking). Main Outcomes and Measures: Self-reported access to wearable devices, frequency of use, and willingness to share health data with clinicians (referred to as health care providers in the survey). Results: Of the overall 9303 HINTS participants representing 247.3 million US adults (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) representing 20.3 million US adults had CVD (mean [SD] age, 62.2 [17.0] years; 43% [95% CI, 37%-49%] women), and 5185 (55.7%) representing 134.9 million US adults were at risk for CVD (mean [SD] age, 51.4 [16.9] years; 43% [95% CI, 37%-49%] women). In nationally weighted assessments, an estimated 3.6 million US adults with CVD (18% [95% CI, 14%-23%]) and 34.5 million at risk for CVD (26% [95% CI, 24%-28%]) used wearable devices compared with an estimated 29% (95% CI, 27%-30%) of the overall US adult population. After accounting for differences in demographic characteristics, cardiovascular risk factor profile, and socioeconomic features, older age (odds ratio [OR], 0.35 [95% CI, 0.26-0.48]), lower educational attainment (OR, 0.35 [95% CI, 0.24-0.52]), and lower household income (OR, 0.42 [95% CI, 0.29-0.60]) were independently associated with lower use of wearable devices in US adults at risk for CVD. Among wearable device users, a smaller proportion of adults with CVD reported using wearable devices every day (38% [95% CI, 26%-50%]) compared with overall (49% [95% CI, 45%-53%]) and at-risk (48% [95% CI, 43%-53%]) populations. Among wearable device users, an estimated 83% (95% CI, 70%-92%) of US adults with CVD and 81% (95% CI, 76%-85%) at risk for CVD favored sharing wearable device data with their clinicians to improve care. Conclusions and Relevance: Among individuals with or at risk for CVD, fewer than 1 in 4 use wearable devices, with only half of those reporting consistent daily use. As wearable devices emerge as tools that can improve cardiovascular health, the current use patterns could exacerbate disparities unless there are strategies to ensure equitable adoption.

Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials.

BACKGROUND: The cardiovascular benefits of intensive systolic blood pressure control vary across clinical populations tested in large randomised clinical trials. We aimed to evaluate the application of machine learning to clinical trials of patients without and with type 2 diabetes to define the personalised cardiovascular benefit of intensive control of systolic blood pressure. METHODS: In SPRINT, a trial of intensive (systolic blood pressure <120 mm Hg) versus standard (systolic blood pressure <140 mm Hg) systolic blood pressure control in patients without type 2 diabetes, we defined a phenotypic representation of the study population using 59 baseline variables. We extracted personalised treatment effect estimates for the primary outcome, time-to-first major adverse cardiovascular event (MACE; cardiovascular death, myocardial infarction or acute coronary syndrome, stroke, and acute decompensated heart failure), through iterative Cox regression analyses providing average hazard ratio (HR) estimates weighted for the phenotypic distance of each participant from the index patient of each iteration. Next, we trained an extreme gradient boosting algorithm (known as XGBoost) to predict the personalised effect of intensive systolic blood pressure control using features most consistently linked to increased personalised benefit, before evaluating its performance in the ACCORD BP trial of patients with type 2 diabetes randomly assigned to receive intensive versus standard systolic blood pressure control. We stratified patients based on their predicted treatment effect, and key demographic groups (age, sex, cardiovascular disease, and smoking). We assessed the presence of heterogeneity with an interaction test, and assessed the performance of the algorithm in a simulation analysis of SPRINT in the presence or absence of an artificially introduced heterogeneous treatment effect. FINDINGS: From SPRINT, we included all 9361 study participants (mean age 67·9 years [SD 9·4], 3332 [35·6%] female) who underwent randomisation to either intensive (n=4678) or standard (n=4683) treatment. The median individualised HR for MACE was 0·63 (IQR 0·53-0·78). An eight-feature tool built for this analysis to predict personalised benefit in SPRINT was externally tested in ACCORD BP (4733 participants (mean age 62·7 years [SD 6·7], 2258 [47·7%] female), wherein it successfully identified individuals with differential benefit from intensive versus standard systolic blood pressure control (adjusted HR for MACE of 0·70 [95% CI 0·55-0·90] in individuals with above-median MACE benefit versus 1·05 [95% CI 0·84-1·32] for below-median predicted benefit; pinteraction=0·0184). Subgroup analysis based on age (<65 years: HR 0·89 [95% CI 0·71-1·12]; ≥65 years: 0·85 [0·67-1·09]), sex (male: 0·89 [0·72-1·10]; female: 0·85 [0·65-1·10]), established cardiovascular disease (no: 0·89 [0·70-1·14]; yes: 0·84 [0·67-1·06]), or active smoking (no: 0·85 [0·71-1·02]; yes: 1·01 [0·64-1·60]) did not identify groups with heterogeneity of treatment effect. In a simulation analysis of SPRINT, the proposed algorithm detected groups with heterogeneous treatment effects in the presence, but not absence, of simulated subgroup differences. INTERPRETATION: By use of machine learning to define an individual's personalised benefit through phenotypic representations of clinical trials, we created a practical tool for individualising the selection of intensive versus standard systolic blood pressure control in patients without and with type 2 diabetes. FUNDING: National Heart, Lung, and Blood Institute of the US National Institutes of Health.

Multimodality Advanced Cardiovascular and Molecular Imaging for Early Detection and Monitoring of Cancer Therapy-Associated Cardiotoxicity and the Role of Artificial Intelligence and Big Data.

Cancer mortality has improved due to earlier detection via screening, as well as due to novel cancer therapies such as tyrosine kinase inhibitors and immune checkpoint inhibitions. However, similarly to older cancer therapies such as anthracyclines, these therapies have also been documented to cause cardiotoxic events including cardiomyopathy, myocardial infarction, myocarditis, arrhythmia, hypertension, and thrombosis. Imaging modalities such as echocardiography and magnetic resonance imaging (MRI) are critical in monitoring and evaluating for cardiotoxicity from these treatments, as well as in providing information for the assessment of function and wall motion abnormalities. MRI also allows for additional tissue characterization using T1, T2, extracellular volume (ECV), and delayed gadolinium enhancement (DGE) assessment. Furthermore, emerging technologies may be able to assist with these efforts. Nuclear imaging using targeted radiotracers, some of which are already clinically used, may have more specificity and help provide information on the mechanisms of cardiotoxicity, including in anthracycline mediated cardiomyopathy and checkpoint inhibitor myocarditis. Hyperpolarized MRI may be used to evaluate the effects of oncologic therapy on cardiac metabolism. Lastly, artificial intelligence and big data of imaging modalities may help predict and detect early signs of cardiotoxicity and response to cardioprotective medications as well as provide insights on the added value of molecular imaging and correlations with cardiovascular outcomes. In this review, the current imaging modalities used to assess for cardiotoxicity from cancer treatments are discussed, in addition to ongoing research on targeted molecular radiotracers, hyperpolarized MRI, as well as the role of artificial intelligence (AI) and big data in imaging that would help improve the detection and prognostication of cancer-treatment cardiotoxicity.

Biomarkers of Vascular Inflammation for Cardiovascular Risk Prognostication: A Meta-Analysis.

OBJECTIVES: The purpose of this study was to systematically explore the added value of biomarkers of vascular inflammation for cardiovascular prognostication on top of clinical risk factors. BACKGROUND: Measurement of biomarkers of vascular inflammation is advocated for the risk stratification for coronary heart disease (CHD). METHODS: We systematically explored published reports in MEDLINE for cohort studies on the prognostic value of common biomarkers of vascular inflammation in stable patients without known CHD. These included common circulating inflammatory biomarkers (ie, C-reactive protein, interleukin-6 and tumor necrosis factor-a, arterial positron emission tomography/computed tomography and coronary computed tomography angiography-derived biomarkers of vascular inflammation, including anatomical high-risk plaque features and perivascular fat imaging. The main endpoint was the difference in c-index (Δ[c-index]) with the use of inflammatory biomarkers for major adverse cardiovascular events (MACEs) and mortality. We calculated I2 to test heterogeneity. This study is registered with PROSPERO (CRD42020181158). RESULTS: A total of 104,826 relevant studies were screened and a final of 39 independent studies (175,778 individuals) were included in the quantitative synthesis. Biomarkers of vascular inflammation provided added prognostic value for the composite endpoint and for MACEs only (pooled estimate for Δ[c-index]% 2.9, 95% CI: 1.7-4.1 and 3.1, 95% CI: 1.8-4.5, respectively). Coronary computed tomography angiography-related biomarkers were associated with the highest added prognostic value for MACEs: high-risk plaques 5.8%, 95% CI: 0.6 to 11.0, and perivascular adipose tissue (on top of coronary atherosclerosis extent and high-risk plaques): 8.2%, 95% CI: 4.0 to 12.5). In meta-regression analysis, the prognostic value of inflammatory biomarkers was independent of other confounders including study size, length of follow-up, population event incidence, the performance of the baseline model, and the level of statistical adjustment. Limitations in the published literature include the lack of reporting of other metrics of improvement of risk stratification, the net clinical benefit, or the cost-effectiveness of such biomarkers in clinical practice. CONCLUSIONS: The use of biomarkers of vascular inflammation enhances risk discrimination for cardiovascular events.

Effects of canagliflozin on human myocardial redox signalling: clinical implications.

AIMS: Recent clinical trials indicate that sodium-glucose cotransporter 2 (SGLT2) inhibitors improve cardiovascular outcomes in heart failure patients, but the underlying mechanisms remain unknown. We explored the direct effects of canagliflozin, an SGLT2 inhibitor with mild SGLT1 inhibitory effects, on myocardial redox signalling in humans. METHODS AND RESULTS: Study 1 included 364 patients undergoing cardiac surgery. Right atrial appendage biopsies were harvested to quantify superoxide (O2.-) sources and the expression of inflammation, fibrosis, and myocardial stretch genes. In Study 2, atrial tissue from 51 patients was used ex vivo to study the direct effects of canagliflozin on NADPH oxidase activity and nitric oxide synthase (NOS) uncoupling. Differentiated H9C2 and primary human cardiomyocytes (hCM) were used to further characterize the underlying mechanisms (Study 3). SGLT1 was abundantly expressed in human atrial tissue and hCM, contrary to SGLT2. Myocardial SGLT1 expression was positively associated with O2.- production and pro-fibrotic, pro-inflammatory, and wall stretch gene expression. Canagliflozin reduced NADPH oxidase activity via AMP kinase (AMPK)/Rac1signalling and improved NOS coupling via increased tetrahydrobiopterin bioavailability ex vivo and in vitro. These were attenuated by knocking down SGLT1 in hCM. Canagliflozin had striking ex vivo transcriptomic effects on myocardial redox signalling, suppressing apoptotic and inflammatory pathways in hCM. CONCLUSIONS: We demonstrate for the first time that canagliflozin suppresses myocardial NADPH oxidase activity and improves NOS coupling via SGLT1/AMPK/Rac1 signalling, leading to global anti-inflammatory and anti-apoptotic effects in the human myocardium. These findings reveal a novel mechanism contributing to the beneficial cardiac effects of canagliflozin.

Insulin-induced vascular redox dysregulation in human atherosclerosis is ameliorated by dipeptidyl peptidase 4 inhibition.

Recent clinical trials have revealed that aggressive insulin treatment has a neutral effect on cardiovascular risk in patients with diabetes despite improved glycemic control, which may suggest confounding direct effects of insulin on the human vasculature. We studied 580 patients with coronary atherosclerosis undergoing coronary artery bypass surgery (CABG), finding that high endogenous insulin was associated with reduced nitric oxide (NO) bioavailability ex vivo in vessels obtained during surgery. Ex vivo experiments with human internal mammary arteries and saphenous veins obtained from 94 patients undergoing CABG revealed that both long-acting insulin analogs and human insulin triggered abnormal responses of post-insulin receptor substrate 1 downstream signaling ex vivo, independently of systemic insulin resistance status. These abnormal responses led to reduced NO bioavailability, activation of NADPH oxidases, and uncoupling of endothelial NO synthase. Treatment with an oral dipeptidyl peptidase 4 inhibitor (DPP4i) in vivo or DPP4i administered to vessels ex vivo restored physiological insulin signaling, reversed vascular insulin responses, reduced vascular oxidative stress, and improved endothelial function in humans. The detrimental effects of insulin on vascular redox state and endothelial function as well as the insulin-sensitizing effect of DPP4i were also validated in high-fat diet-fed ApoE-/- mice treated with DPP4i. High plasma DPP4 activity and high insulin were additively related with higher cardiac mortality in patients with coronary atherosclerosis undergoing CABG. These findings may explain the inability of aggressive insulin treatment to improve cardiovascular outcomes, raising the question whether vascular insulin sensitization with DPP4i should precede initiation of insulin treatment and continue as part of a long-term combination therapy.

Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.

ABSTRACT: Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.

Impact of Cancer Therapy-Related Cardiac Dysfunction on Risk of Heart Failure in Pregnancy.

BACKGROUND: Cancer treatment can lead to left ventricular (LV) dysfunction in female cancer survivors of reproductive age, and pregnancy-related hemodynamic stress may result in LV dysfunction or heart failure (HF). OBJECTIVES: We performed a systematic review and meta-analysis to determine the incidence of LV systolic dysfunction or HF during or soon after pregnancy in cancer survivors and evaluated the impact of history of cancer therapeutics-related cardiac dysfunction (CTRCD). METHODS: We systematically searched electronic databases (MEDLINE and EMBASE) from inception to January 2020 to identify cohort studies that examined cardiac disease in pregnant cancer survivors. Meta-analysis was performed using the inverse-variance fixed effects method. Potential sources of heterogeneity were explored using subgroup analyses and meta-regression. RESULTS: Of 13,782 identified articles, 6 studies consisting of 2,016 pregnancies, predominantly in childhood cancer survivors, were included. Overall, there were 33 cardiac events. The total weighted incidence of LV dysfunction or HF with pregnancy was 1.7% (95% confidence interval [CI]: 0.9% to 2.7%) overall; 28.4% (95% CI: 14.6% to 43.9%) in those with a history of CTRCD and 0.24% (95% CI: 0% to 0.81%) in those without, translating into an odds ratio of 47.4 (95% CI: 17.9 to 125.8). Interstudy heterogeneity was low (I2 = 17.5%). Metaregression did not reveal significant sources of heterogeneity. CONCLUSIONS: The incidence of LV dysfunction or HF during pregnancy in cancer survivors was low. Although risk estimates are limited by the small number of events, women with a history of CTRCD compared to those without had a 47.4-fold higher odds of experiencing pregnancy-related LV dysfunction or HF.

Development of a risk score for early saphenous vein graft failure: An individual patient data meta-analysis.

OBJECTIVES: Early saphenous vein graft (SVG) occlusion is typically attributed to technical factors. We aimed at exploring clinical, anatomical, and operative factors associated with the risk of early SVG occlusion (within 12 months postsurgery). METHODS: Published literature in MEDLINE was searched for studies reporting the incidence of early SVG occlusion. Individual patient data (IPD) on early SVG occlusion were used from the SAFINOUS-CABG Consortium. A derivation (n = 1492 patients) and validation (n = 372 patients) cohort were used for model training (with 10-fold cross-validation) and external validation respectively. RESULTS: In aggregate data meta-analysis (48 studies, 41,530 SVGs) the pooled estimate for early SVG occlusion was 11%. The developed IPD model for early SVG occlusion, which included clinical, anatomical, and operative characteristics (age, sex, dyslipidemia, diabetes mellitus, smoking, serum creatinine, endoscopic vein harvesting, use of complex grafts, grafted target vessel, and number of SVGs), had good performance in the derivation (c-index = 0.744; 95% confidence interval [CI], 0.701-0.774) and validation cohort (c-index = 0.734; 95% CI, 0.659-0.809). Based on this model. we constructed a simplified 12-variable risk score system (SAFINOUS score) with good performance for early SVG occlusion (c-index = 0.700, 95% CI, 0.684-0.716). CONCLUSIONS: From a large international IPD collaboration, we developed a novel risk score to assess the individualized risk for early SVG occlusion. The SAFINOUS risk score could be used to identify patients that are more likely to benefit from aggressive treatment strategies.

A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography.

BACKGROUND: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. METHODS AND RESULTS: We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62-0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI. CONCLUSION: The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art.

Assessment of Prognostic Value of Left Ventricular Global Longitudinal Strain for Early Prediction of Chemotherapy-Induced Cardiotoxicity: A Systematic Review and Meta-analysis.

Importance: Echocardiographic left ventricular global longitudinal strain (GLS) detects early subclinical ventricular dysfunction and can be used in patients receiving potentially cardiotoxic chemotherapy. A meta-analysis of the prognostic value of GLS for cancer therapy-related cardiac dysfunction (CTRCD) has not been performed, to our knowledge. Objective: To explore the prognostic value of GLS for the prediction of CTRCD. Data Sources: Systematic search of the MEDLINE, Embase, Scopus, and the Cochrane Library databases from database inception to June 1, 2018. Study Selection: Cohort studies assessing the prognostic or discriminatory performance of GLS before or during chemotherapy for subsequent CTRCD. Data Extraction and Synthesis: Random-effects meta-analysis and hierarchical summary receiver operating characteristic curves (HSROCs) were used to summarize the prognostic and discriminatory performance of different GLS indices. Publication bias was assessed using the Egger test, and meta-regression was performed to assess sources of heterogeneity. Main Outcomes and Measures: The primary outcome was CTRCD, defined as a clinically significant change in left ventricular ejection fraction with or without new-onset heart failure symptoms. Results: Analysis included 21 studies comprising 1782 patients with cancer, including breast cancer, hematologic malignancies, or sarcomas, treated with anthracyclines with or without trastuzumab. The incidence of CTRCD ranged from 9.3% to 43.8% over a mean follow-up of 4.2 to 23.0 months (pooled incidence, 21.0%). For active treatment absolute GLS (9 studies), the high-risk cutoff values ranged from -21.0% to -13.8%, with worse GLS associated with a higher CTRCD risk (odds ratio, 12.27; 95% CI, 7.73-19.47; area under the HSROC, 0.86; 95% CI, 0.83-0.89). For relative changes vs a baseline value (9 studies), cutoff values ranged from 2.3% to 15.9%, with a greater decrease linked to a 16-fold higher risk of CTRCD (odds ratio, 15.82; 95% CI, 5.84-42.85; area under the HSROC, 0.86; 95% CI, 0.83-0.89). Both indices showed significant publication bias. Meta-regression identified differences in sample size and CTRCD definition but not GLS cutoff value as significant sources of interstudy heterogeneity. Conclusions and Relevance: In this meta-analysis, measurement of GLS after initiation of potentially cardiotoxic chemotherapy with anthracyclines with or without trastuzumab had good prognostic performance for subsequent CTRCD. However, risk of bias in the original studies, publication bias, and limited data on the incremental value of GLS and its optimal cutoff values highlight the need for larger prospective multicenter studies.

Association of Biologic Therapy With Coronary Inflammation in Patients With Psoriasis as Assessed by Perivascular Fat Attenuation Index.

Importance: Psoriasis is a chronic inflammatory skin disease associated with increased coronary plaque burden and cardiovascular events. Biologic therapy for psoriasis has been found to be favorably associated with luminal coronary plaque, but it is unclear whether these associations are attributable to direct anti-inflammatory effects on the coronary arteries. Objective: To investigate the association of biologic therapy with coronary inflammation in patients with psoriasis using the perivascular fat attenuation index (FAI), a novel imaging biomarker that assesses coronary inflammation by mapping spatial changes of perivascular fat composition via coronary computed tomography angiography (CCTA). Design, Setting, and Participants: This prospective cohort study performed from January 1, 2013, through March 31, 2019, analyzed changes in FAI in patients with moderate to severe psoriasis who underwent CCTA at baseline and at 1 year and were not receiving biologic psoriasis therapy at baseline. Exposures: Biologic therapy for psoriasis. Main Outcomes and Measures: Perivascular FAI mapping was performed based on an established method by a reader blinded to patient demographics, visit, and treatment status. Results: Of the 134 patients (mean [SD] age, 51.1 [12.1] years; 84 [62.5%] male), most had low cardiovascular risk by traditional risk scores (median 10-year Framingham Risk Score, 3% [interquartile range, 1%-7%]) and moderate to severe skin disease. Of these patients, 82 received biologic psoriasis therapy (anti-tumor necrosis factor α, anti-interleukin [IL] 12/23, or anti-IL-17) for 1 year, and 52 did not receive any biologic therapy and were given topical or light therapy (control group). At baseline, 46 patients (27 in the treated group and 19 in the untreated group) had a focal coronary atherosclerotic plaque. Biologic therapy was associated with a significant decrease in FAI at 1 year (median FAI -71.22 HU [interquartile range (IQR), -75.85 to -68.11 HU] at baseline vs -76.09 HU [IQR, -80.08 to -70.37 HU] at 1 year; P < .001) concurrent with skin disease improvement (median PASI, 7.7 [IQR, 3.2-12.5] at baseline vs 3.2 [IQR, 1.8-5.7] at 1 year; P < .001), whereas no change in FAI was noted in those not receiving biologic therapy (median FAI, -71.98 [IQR, -77.36 to -65.64] at baseline vs -72.66 [IQR, -78.21 to -67.44] at 1 year; P = .39). The associations with FAI were independent of the presence of coronary plaque and were consistent among patients receiving different biologic agents, including anti-tumor necrosis factor α (median FAI, -71.25 [IQR, -75.86 to -66.89] at baseline vs -75.49 [IQR, -79.12 to -68.58] at 1 year; P < .001) and anti-IL-12/23 or anti-IL-17 therapy (median FAI, -71.18 [IQR, -75.85 to -68.80] at baseline vs -76.92 [IQR, -81.16 to -71.67] at 1 year; P < .001). Conclusions and Relevance: In this study, biologic therapy for moderate to severe psoriasis was associated with reduced coronary inflammation assessed by perivascular FAI. This finding suggests that perivascular FAI measured by CCTA may be used to track response to interventions for coronary artery disease.

Myocarditis Surveillance in Patients with Advanced Melanoma on Combination Immune Checkpoint Inhibitor Therapy: The Memorial Sloan Kettering Cancer Center Experience.

This letter to the editor describes myocarditis screening among patients undergoing combination immune checkpoint inhibitor therapy, in light of the consensus document from the Checkpoint Inhibitor Safety Working Group.

Heart transplantation versus left ventricular assist devices as destination therapy or bridge to transplantation for 1-year mortality: a systematic review and meta-analysis.

BACKGROUND: The optimal treatment for advanced heart failure (HF) patients with regards to mortality remains unknown. Heart transplantation (HTx) and left ventricular assist devices (LVAD) used either as a bridge to transplant (BTT) or destination therapy (DT) have been compared in a number of studies, without definite conclusions with regards to mortality benefit. We sought to systematically review the pertinent literature and perform a meta-analysis of all the available studies presenting head-to-head comparisons between HTx and LVAD BTT or LVAD DT for late (>6 months) all-cause mortality. METHODS: We performed a systematic search of Medline and Cochrane Central databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conducted a meta-analysis of late mortality comparing HTx vs. BTT LVAD and HTx vs. DT LVAD using a random effects model. RESULTS: Eight studies were included in our meta-analysis, reporting data on 7,957 patients in total. Although the available studies are of high quality [8 stars in Newcastle-Ottawa Scale (NOS) on average], there is paucity of mortality data. Specifically, seven studies compared HTx with BTT and five studies compared HTx with DT for 1-year mortality. Our pooled estimates showed that there was no difference in late mortality among these strategies. CONCLUSIONS: Our meta-analysis highlights the small number and the heterogeneity of available studies referring to the optimal invasive management of advanced HF, and shows that there are no differences between HTx and LVAD for these patients with regards to late mortality.

Development and Validation of a Clinical Score for Cardiovascular Risk Stratification of Long-Term Childhood Cancer Survivors.

BACKGROUND: Long-term childhood cancer survivors (CCS) are at increased risk of adverse cardiovascular events; however, there is a paucity of risk-stratification tools to identify those at higher-than-normal risk. SUBJECTS, MATERIALS, AND METHODS: This was a population-based study using data from the Surveillance, Epidemiology, and End Results Program (1973-2013). Long-term CCS (age at diagnosis ≤19 years, survival ≥5 years) were followed up over a median time period of 12.3 (5-40.9) years. Independent predictors of cardiovascular mortality (CVM) were combined into a risk score, which was developed in a derivation set (n = 22,374), and validated in separate patient registries (n = 6,437). RESULTS: In the derivation registries, older age at diagnosis (≥10 years vs. reference group of 1-5 years), male sex, non-white race, a history of lymphoma, and a history of radiation were independently associated with an increased risk of CVM among long-term CCS (p < .05). A risk score derived from this model (Childhood and Adolescence Cancer Survivor CardioVascular score [CHACS-CV], range: 0-8) showed good discrimination for CVM (Harrell's C-index [95% confidence interval (CI)]: 0.73 [0.68-0.78], p < .001) and identified a high-risk group (CHACS-CV ≥6), with cumulative CVM incidence over 30 years of 6.0% (95% CI: 4.3%-8.1%) versus 2.6% (95% CI: 1.8%-3.7%), and 0.7% (95% CI: 0.5%-1.0%) in the mid- (CHACS-CV = 4-5) and low-risk groups (CHACS-CV ≤3), respectively (plog-rank < .001). In the validation set, the respective cumulative incidence rates were 4.7%, 3.1%, and 0.8% (plog-rank < .001). CONCLUSION: We propose a simple risk score that can be applied in everyday clinical practice to identify long-term CCS at increased cardiovascular risk, who may benefit from early cardiovascular screening, and risk-reduction strategies. IMPLICATIONS FOR PRACTICE: Childhood cancer survivors (CCS) are known to be at increased cardiovascular risk. Currently available prognostic tools focus on treatment-related adverse events and late development of congestive heart failure, but there is no prognostic model to date to estimate the risk of cardiovascular mortality among long-term CCS. A simple clinical tool is proposed for cardiovascular risk stratification of long-term CCS based on easily obtainable information from their medical history. This scoring system may be used as a first-line screening tool to assist health care providers in identifying those who may benefit from closer follow-up and enable timely deployment of preventive strategies.

Vascular inflammation and metabolic activity in hematopoietic organs and liver in familial combined hyperlipidemia and heterozygous familial hypercholesterolemia.

BACKGROUND: Familial dyslipidemias of either heterozygous (heFH) or combined (FCH) type lead to accelerated atherogenesis and increased cardiovascular risk. OBJECTIVE: The aim of this study was to investigate in statin-naïve adult patients with familial dyslipidemias whether inflammatory activation and liver, spleen and bone marrow metabolic activity differ compared with normolipidemic subjects and between dyslipidemic groups. METHODS: Fourteen patients with FCH, 14 with heFH, and 14 normolipidemic individuals were enrolled. Serum lipids, high-sensitivity C-reactive protein, and fibrinogen levels were measured, followed by 18F-fluorodeoxyglucose positron-emission tomography/computed tomography imaging. Radiotracer uptake in the aortic wall, spleen, bone marrow, and liver was quantified as tissue-to-background ratio (TBR). RESULTS: Patients with heFH had significantly higher low-density lipoprotein levels compared with those with FCH and controls (P < .001). However, aortic TBRs were higher in FCH compared with heFH patients and controls (P = .02 and P < .001, respectively). FCH patients exhibited higher FDG uptake in the spleen compared with controls (P = .05). In addition, FCH exhibited higher bone marrow FDG uptake compared with heFH patients and controls (P = .03 and P = .02, respectively). FCH had higher liver uptake compared with heFH patients and controls (P < .001 for both). Significant correlations were observed between inflammatory biomarkers and imaging indices as well as between aortic TBR and FDG uptake of hematopoietic organs and liver. CONCLUSIONS: Systemic, as well as vascular inflammation and spleen, bone marrow, and hepatic metabolic activity are increased in patients with FCH despite lower levels of low-density lipoprotein.

Stem Cell Therapy for Congenital Heart Disease: A Systematic Review.

BACKGROUND: Congenital heart disease (CHD) constitutes the most prevalent and heterogeneous group of congenital anomalies. Although surgery remains the gold standard treatment modality, stem cell therapy has been gaining ground as a complimentary or alternative treatment option in certain types of CHD. The aim of this study was to present the existing published evidence and ongoing research efforts on the implementation of stem cell-based therapeutic strategies in CHD. METHODS: A systematic review was conducted by searching Medline, ClinicalTrials.gov, and the Cochrane library, along with reference lists of the included studies through April 23, 2017. RESULTS: Nineteen studies were included in this review (8 preclinical, 6 clinical, and 5 ongoing trials). Various routes of cardiac stem cell delivery have been reported, including intracoronary, intramyocardial, intravenous, and epicardial. Depending on their origin and level of differentiation at which they are harvested, stem cells may exhibit different properties. Preclinical studies have mostly focused on modeling right ventricle dysfunction or failure and pulmonary artery hypertension by using pressure or volume overload in vitro or in vivo. Only a limited number of clinical trials on patients with CHD exist, and these primarily focus on hypoplastic left heart syndrome. Cell-based tissue engineering has recently been introduced, and research currently is focusing on developing cell-seeded grafts and patches that could potentially grow in parallel with whole body growth once implanted in the heart. CONCLUSIONS: It seems that stem cell delivery to the diseased heart as an adjunct to surgical palliation may provide some benefits over surgery alone in terms of cardiac function, somatic growth, and quality of life. Despite encouraging preliminary results, stem cell therapies for patients with CHD should only be considered in the setting of well-designed clinical trials. More wet laboratory research experience is needed, and translation of promising findings to large clinical studies is warranted to clearly define the efficacy and safety profile of this alternative and potentially groundbreaking therapeutic approach.