Background Contemporary therapies improve breast cancer (BC) outcomes. Yet, many of these therapies have been increasingly linked with serious cardiotoxicity, including reports of profound hypertension. Yet, the incidence, predictors, and impacts of these events are largely unknown. Methods Leveraging two large U.S.‐based registries, the National Inpatient Sample (NIS) and the Food and Drug Administration Adverse Event Reporting System (FAERS) databases, we assessed the incidence, factors, and outcomes of hypertensive events among BC patients from 2007 to 2015. Differences in baseline characteristics, hypertension‐related discharges, and complications were examined over time. Further, we performed a disproportionality analysis using reporting‐odds‐ratios (ROR) to determine the association between individual BC drugs and hypertensive events. Utilizing an ROR cutoff of >1.0, we quantified associations by drug‐class, and individual drugs with the likelihood of excess hypertension. Results Overall, there were 5,464,401 BC‐admissions, of which 46,989 (0.8%) presented with hypertension. Hypertensive BC patients were older, and saw initially increased in‐hospital mortality, which equilibrated over time. The mean incidence of hypertension‐related admissions was 732 per 100,000 among BC patients, versus 96 per 100,000 among non‐cancer patients (RR 7.71, p < 0.001). Moreover, in FAERS, those with hypertension versus other BC‐treatment side‐effects were more frequently hospitalized (40.1% vs. 36.7%, p < 0.001), and were most commonly associated with chemotherapy (45.9%). Outside of Eribulin (ROR 3.36; 95% CI 1.37–8.22), no specific drug was associated with a higher reporting of hypertension; however, collectively BC drugs were associated with a higher odds of hypertension (ROR 1.66; 95% CI 1.09–2.53). Conclusions BC therapies are associated with a substantial increase in limiting hypertension.

Objective: Utilization of pharmacogenomics data in clinical practice is a critical step towards individual and precision medicine. This is a cross-sectional study conducted by incorporating several variables as outlined in the survey report to assess and analyze the reasons or behaviors that could influence clinicians to use or not use pharmacogenomics. Methods: In this study, we conducted a cross-sectional quantitative survey among primary physicians practicing in Kettering Health Network facilities. 1,201 invitations were sent out and 135 Physicians participated in the survey. Physicians were requested by email to participate in a survey containing 14 multiple choice questions regarding their understanding and beliefs regarding pharmacogenomics, as well as questions about specific professional details which were intended to explore how physician characteristics affected familiarity, and comfort and confidence in using pharmacogenomics data in patient care. Statistical Package for the Social Sciences (standard version 25) was used for statistical analysis and consent was obtained from all study participants through the survey link. Results: The ratings of the familiarly, comfort, and confidence with pharmacogenetics were highly intercorrelated (r = 0.81-0.87).  Accordingly, we summed the three ratings to form a composite score of the three items; hereafter referred to as “scale scores”.  Possible scores ranged from 5 to 15, whereas actual scores ranged from 3 to 15 (Mean = 6.32, SD = 3.12). Scale scores were not statistically significantly correlated with age (r = 0.12, p < 0.17) or number of years in practice (r = 0.11, p < 0.22), and were only weakly (inversely) correlated with number of hours spent in patient care each week (r = -0.17, p < 0.05). Conclusion: In our study, physicians who had some education in the field of pharmacogenomics were more likely to use pharmacogenomics data in clinical practice. We have further characterized that continuing medical education (CME), more than medical education or residency training significantly predicts familiarity, confidence, or comfort in using pharmacogenomics data. Therefore, pharmacogenomics should be integrated in the CME for practicing clinicians as well as graduate medical education.

Background 25% of all breast cancer patients have HER-2 overexpression. Breast Cancer patients with HER-2 overexpression are typically treated with HER-2 inhibitors such as Trastuzumab. Trastuzumab is known to cause a decrease in left ventricular ejection fraction. The aim of this study is to create a cardiac risk prediction tool among women with Her-2 positive breast cancer to predict cardiotoxicity. Method Using a split sample design, we created a risk prediction tool using patient level data from electronic medical records. The study included women 18 years of age and older diagnosed with HER-2 positive breast cancer who received Trastuzumab. Outcome measure was defined as a drop in LVEF by more than 10% to less than 53% at any time in the 1-year study period. Logistic regression was used to test predictors. Results The cumulative incidence of cardiac dysfunction in our study was 9.4%. The sensitivity and specificity of the model are 46% and 84%, respectively. Given a cumulative incidence of cardiotoxicity of 9%, the negative predictive value of the test was 94%. This suggests that in a low-risk population, the interval of screening for cardiotoxicity may be performed less frequently. Conclusion Cardiac risk prediction tool can be used to identify Her-2 positive breast cancer patients at risk of developing cardiac dysfunction. Also, test characteristics in addition to disease prevalence may inform a rational strategy in performing cardiac ultrasound in Her-2 breast cancer patients. We have developed a cardiac risk prediction model with high NPV in a low-risk population which has an appealing cost-effectiveness profile.

Introduction: The third-generation tyrosine kinase inhibitor ponatinib approved for treating chronic myeloid leukemia (CML) rendered drug resistant with a threonine-315-isoleucine (T315I) mutation is associated with increased incidence of vascular adverse events (VAEs). This case series sought to elucidate the mechanism of these VAEs. Methods: Two patients with CML and two patients with Philadelphia chromosome positive acute lymphocytic leukemia (Ph+ ALL) on at least one year of ponatinib therapy were imaged with optical coherence tomography (OCT) after presenting with angina. An additional four patients from an institutional OCT database were matched across clinical parameters (age, sex, diabetes, prior coronary artery disease (CAD) history, prior congestive heart failure (CHF) history, hypertension, and family history of CAD) to serve as controls. Plaque composition was assessed using operator-assisted virtual histology OCT (vOCT). Results: Clinical presentations included 2 cases of NSTEMI, 1 case of unstable angina, and 1 case of stable angina pectoris among the 4 ponatinib patients. Atherosclerosis was composed primarily of calcium and fibrous tissue with minimal lipid presence (0 ± 0°) compared to the matched controls. The matched controls had a mean non-zero lipid content of 48.43 ± 18.36°, ( P < 0.05) (Table 1). Three of the four patients had cardiovascular risk factors. Conclusions: Ponatinib causes VAEs through non-lipid plaque formation driving a symptomatology consistent with an oxygen supply-demand mismatch. This mechanism is distinct from traditional pathways of excess lipid and plaque instability causing coronary artery disease and merits further investigation.

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