Citations

Abstract Background: Cardiac toxicity (CT) in the form of left ventricular systolic dysfunction is a well-known complication of anti-HER2 therapy. Prior clinical trial data has suggested potential factors associated with increased risk for CT. We sought to develop a clinical risk prediction model from real world patients to better identify patients who may be at higher risk for CT. Methods: Patients treated with anti-HER2 agents at all Mayo Clinic sites from Jan 2008 to July 2015 were reviewed. Patient data including age, race, body mass index, comorbidities, smoking history, baseline ejection fraction (EF), presence of left ventricular hypertrophy (LVH), prior cancer treatments, number of anti-HER2 agents used, and cardiac medication use were recorded. Factors were compared in patients with and without development of CT. Cox regression models were used to identify univariate and multivariate predictors of CT. A risk score was then assigned based on the final multivariable model. Results: 604 patients were included in final analysis. Median follow-up 2.9 yrs; median age: 57 yr; baseline EF: 64%. One yr CT rate was 7%. Independent predictors of CT were age, baseline EF, LVH, and anthracycline use (C index=0.74). Based on model coefficient, scores of 1, 1, 2 and 3 were assigned to age>55, EF <60%, anthracycline exposure and LVH, respectively. Two random samples of 300 patients were created out of the original cohort for internal validation of the risk score model. In each, the risk score was found to be a highly significant predictor for CT with C index of 0.72 {HR 2.13 (95% CI 1.57-2.90), p <0.001} and 0.77 {HR 2.45 (95% CI 1.74-3.44), p <0.001}. Conclusions: Patients with risk factors of older age, lower baseline EF, anthracycline use and LVH are at increased risk for CT when treated with anti-HER2 agents. Using a risk prediction model at therapy initiation may enhance identification of patients who benefit from cardiology referral, closer cardiac monitoring, and/or prophylactic treatment while on anti-HER2 targeted therapy. Table 1. Multivariable Cox regression model predicting cardiac dysfunctionParameterParameter EstimateHR (95%CI)p valueScoreAge >550.909552.48(1.27, 4.84)0.00751EF<600.634361.89(1.01, 3.53)0.04751Anthracycline Exposure1.046132.85(1.48, 5.46)0.00162Baseline LVH1.633565.12(2.04,12.85)<0.0013 Citation Format: Pooja P Advani, Lauren Cornell, Zhuo Li, Jordan Ray, Catalina Sanchez, Armando Villanueva, Taryn Smith, Alvaro Moreno-Aspitia. A validated clinical risk prediction model for cardiac toxicity in patients treated with antiHER2 agents [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-14-14.

A 61-year-old male presented for an annual exam and received a transthoracic echocardiogram (TTE) which revealed a mobile mass arising from a subaortic membrane. Further investigations with a transesophageal echocardiogram (TEE) and cardiac computerized tomography angiography (CTA) confirmed the presence of a mobile 9 mm×3 mm mass on a subaortic membrane. Cardiothoracic surgery was performed with an open operation removing the mass and subaortic membrane. Upon visual inspection, the mass was likened to a sea anemone and immunohistochemical staining performed pathologically confirmed the diagnosis of cardiac papillary fibroelastoma. This case represents the first reported example of a cardiac papillary fibroelastoma (PFE) arising from a subaortic membrane. Although PFEs are benign cardiac tumors, proper identification and consideration for excision of these lesions may be indicated to prevent thromboembolic complications.

Objectives: Pulmonary arterial hypertension (PAH) is a deadly disease without any effective cure. Identification of the PAH-associated gut microbiome may shift conceptual paradigms of the disease that could lead to new therapeutic advancement. We tested the hypothesis that PAH patients have distinct gut microbiome composition contributing to PAH that predicts the disease. Methods: A total of 71 participants were recruited. Fecal microbiomes of 18 type 1 PAH patients (mean pulmonary arterial pressure [mPAP] 57.4±16.7 mmHg) and 13 reference subjects were compared by shotgun metagenomics methods. Plasma from 22 PAH patients (mPAP 48.3± 18.4 mmHg) and 18 reference subjects was used in untargeted metabolomic analysis. Results: Significant taxonomic and functional changes in the microbiome of the PAH cohort were observed. PAH patients had increased gut barrier dysfunction and inflammation as evidenced by plasma markers and metagenomics. A random forest model predicted PAH from gut microbiome composition with 82.6% accuracy. Changes in the gut microbiome were associated with purine metabolism, arginine biosynthesis, trimethylamine production and tryptophan biosynthesis, all previously associated with PAH. Area under the curve of the multi-variate receiver operating characteristics (ROC) plot for metabolites of these pathways had a predictive accuracy of 0.965. Conclusions: The gut microbiome/metabolites of PAH patients predict disease in random forest models and ROC analyses. The unique PAH gut microbiome influences carnitine, arginine and purine metabolisms contributing to pathophysiology. These highlight previously unknown roles of gut bacteria and their metabolites in PAH that could lead to new therapeutic or management paradigms.

Objectives: Pulmonary arterial hypertension (PAH) is a cardiopulmonary disease without effective cure. The objective of this study is to test the hypothesis that PAH patients have a distinct gut microbiome composition acting as a driving force for the increased PAH metabolic biomarkers, acyl carnitines and urate. Methods: Fecal samples from 18 type 1 PAH patients (mean pulmonary arterial pressure [mPAP] 57.4±16.7 mmHg) and 13 reference subjects in Brazil were collected for comparison of microbiome using shotgun metagenomics. Plasma samples from 33 type I PAH patients (mPAP 51.7± 13.5 mmHg) and 19 reference subjects in the USA were used for untargeted metabolomic analysis. Results: More than 80 bacterial taxa were differentially enriched in the microbiomes of the PAH cohort. Random forest machine-learning algorithm predicted PAH patients with 82.4% accuracy from their gut microbiome composition. The most increased taxa in the PAH microbiome were trimethylamine (TMA) producing bacteria like Collinsella (P=0.0037). The PAH metabolome also showed increases of gut microbiome-derived short chain acyl carnitines like isubutyrylcarnitine (P=0.010) and valerylcarnitine (P=0.030), which are the precursors of TMA. In addition, increased urate (P=0.0004) and allantoin (P=0.027) in PAH plasma correlated with significantly increased enzymes producing urate in the PAH microbiome (purine metabolism (P=0.017) and xanthine oxidase (P<0.05)). Conclusion: These findings suggest that the random forest modeling of the gut microbiome composition can be used for prediction of PAH and that the unique gut microbiome of PAH influences acyl carnitines and urate metabolism to contribute to pathophysiology.

Introduction: Our study assesses the utility of telemetry in identifying decompensation in patients with documented cardiopulmonary arrest. Methods: A retrospective review of inpatients who experienced a cardiopulmonary arrest from May 1, 2008, until June 30, 2014, was performed. Telemetry records 24 hours prior to and immediately preceding cardiopulmonary arrest were reviewed. Patient subanalyses based on clinical demographics were made as well as analyses of survival comparing patients with identifiable rhythm changes in telemetry to those without. Results: Of 242 patients included in the study, 75 (31.0%) and 110 (45.5%) experienced telemetry changes at the 24-hour and immediately preceding time periods, respectively. Of the telemetry changes, the majority were classified as nonmalignant (n = 50, 66.7% and n = 66, 55.5% at 24 hours prior and immediately preceding, respectively). There was no difference in telemetry changes between intensive care unit (ICU) and non-ICU patients and among patients stratified according to the American Heart Association telemetry indications. There was no difference in survival when comparing patients with telemetry changes immediately preceding and at 24 hours prior to an event (n = 30, 27.3% and n = 15, 20.0%) to those without telemetry changes during the same periods (n = 27, 20.5% and n = 42, 25.2%; P = .22 and .39). Conclusion: Telemetry has limited utility in predicting clinical decompensation in the inpatient setting.