Citations

Abstract Background/Introduction Tricuspid valve regurgitation (TR) is a common valvular disease associated with increased mortality. There is a need for tools to assess the interaction between the pulmonary artery (PA) circulation and the right ventricle (RV) in patients with TR and to investigate their association with outcomes. Various echo parameters have emerged that assess RV-PA coupling, including the ratio of TAPSE to RV systolic pressure (RVSP) and the PA pulsatility index (PAPi). The effective PA elastance (PA Ea) is a composite measure of RV afterload that refers to the measure of the pulmonary vascular load that the RV must overcome to eject blood into the PA. PA Ea is simple to estimate by echo and found to be an independent haemodynamic risk measure in various left heart and pulmonary hypertension (PH) diseases. Whether PA Ea discriminates risk in unselected patients with significant TR and how it compares to other measures of RV-PA coupling is untested. Purpose To ascertain the prognostic value of PA Ea in patients with TR. Methods In consecutive patients with a first diagnosis of ≥ moderate TR (regardless of aetiology), we compared all-cause mortality at five years based on the PA Ea (defined as the RVSP/stroke volume as measured by transthoracic echocardiography). As stroke volume was measured by Doppler at the left ventricular (LV) outflow tract, patients with > moderate aortic valve regurgitation were excluded. The Tricuspid Regurgitation Impact on Outcomes (TRIO) score was calculated as previously described. PA Ea was separated into 4 groups for comparison based on the separation of quartiles of PA Ea rounded to one decimal place. Results In the 13,608 patients studied, PA Ea could be calculated in 12,682 patients (94.4%). The median PA Ea was 0.68 mmHg/mL (IQR 0.50, 0.92). Increasing levels of PA Ea were associated with clinical comorbidities including heart failure, liver and kidney disease and clinical markers of risk such as the TRIO score. Increasing levels of PA Ea were also associated with larger RVs, worse RV systolic function, higher NT-pro BNP levels, and greater degrees of right heart failure (Table). Each quartile of PA Ea was associated with a significant increase in all-cause mortality (Figure). After adjusting for age and sex, PA Ea was associated with higher risk of death (HR 2.41 (2.28-2.56); p<.0001). While worsening degrees of PA Ea correlated with both declining levels of TAPSE/PASP and PAPi (Table), PA Ea was independently predictive of outcome in a combined model: PA Ea HR 1.65 (1.51-1.80, Chi-squ 120, p<.0001); PAPi HR 0.84 (0.80-0.89, Chi-squ 48, p<.0001) and TAPSE/PASP HR 0.32 (017-0.60, Chi-squ 12, p=.0004). Conclusion(s) In patients with significant TR, PA Ea is highly feasible and higher PA Ea is progressively associated with RV dysfunction, right heart failure, and worse survival. Incorporating PA Ea into the routine echo assessment may help stratify disease severity in patients with significant TR. Table Assoc. of PA Ea with mortality in TR

Objective Tricuspid regurgitation (TR) is a prevalent valve disease associated with significant morbidity and mortality. We aimed to apply machine learning (ML) to assess risk stratification in patients with ≥moderate TR. Methods Patients with ≥moderate TR on echocardiogram between January 2005 and December 2016 were retrospectively included. We used 70% of data to train ML-based survival models including 27 clinical and echocardiographic features to predict mortality over a 3-year period on an independent test set (30%). To account for differences in baseline comorbidities, prediction was performed in groups stratified by increasing Charlson Comorbidity Index (CCI). Permutation feature importance was calculated using the best-performing model separately in these groups. Results Of 13 312 patients, mean age 72 ± 13 years and 7406 (55%) women, 7409 (56%) had moderate, 2646 (20%) had moderate–severe and 3257 (24%) had severe TR. The overall performance for 1-year mortality by 3 ML models was good, c-statistic 0.74–0.75. Interestingly, performance varied between CCI groups, (c-statistic = 0.774 in lowest CCI group and 0.661 in highest CCI group). The performance decreased over 3-year follow-up (average c-index 0.78). Furthermore, the top 10 features contributing to these predictions varied slightly with the CCI group, the top features included heart rate, right ventricular systolic pressure, blood pressure, diuretic use and age. Conclusions Machine learning of common clinical and echocardiographic features can evaluate mortality risk in patients with TR. Further refinement of models and validation in prospective studies are needed before incorporation into the clinical practice.

The NCCN Guidelines for Prostate Cancer include recommendations regarding diagnosis, risk stratification and workup, treatment options for localized disease, and management of recurrent and advanced disease for clinicians who treat patients with prostate cancer. The portions of the guidelines included herein focus on the roles of germline and somatic genetic testing, risk stratification with nomograms and tumor multigene molecular testing, androgen deprivation therapy, secondary hormonal therapy, chemotherapy, and immunotherapy in patients with prostate cancer.

5000 Background: It is clinically challenging to integrate genomic classifier results that report a continuous numerical risk of recurrence into treatment decisions for prostate cancer (PCa). We aimed to develop a novel clinical-genomic risk system that can readily be incorporated into treatment guidelines for localized PCa. Methods: Four multi-center cohorts (n = 6928 men; 5937 prospective samples and 991 retrospective samples with long-term follow-up) were utilized to identify and validate our clinical-genomic risk system in radical prostatectomy (RP) samples and subsequently in pre-treatment biopsy samples. All patients’ FFPE tissue underwent microarray analysis, and the expression values for 22 prespecified biomarkers that constitute Decipher were extracted. Cumulative incidence curves were constructed to estimate metastasis risk. C-indices were calculated to compare NCCN and CAPRA score to our clinical-genomic system. Results: With a median follow-up of 8 years for men in our RP cohort, the 10-year distant metastasis rates for NCCN low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.8%, 9.4%, 40.1%, and 41.4%, respectively. Our 3-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.7%, 30.7%, and 57.7%, for low, intermediate, and high-risk, which were validated in our pre-treatment biopsy cohort with 10-year rate of distant metastasis of 0%, 30.3%, and 63.2%, respectively. C-indices for the clinical-genomic system (0.84, 95%CI 0.62-0.92) were significantly improved over NCCN (0.71, 95%CI 0.59-0.84) and CAPRA (0.71, 95%CI 0.60-0.81) score. A total of 33.4% of men would be reclassified by the clinical-genomic system, and specifically 17.1%, 41.3%, and 19.4% of men in NCCN low, intermediate and high risk groups would be reclassified by our new system. Conclusions: The use of a readily available genomic classifier in combination with clinicopathologic variables can generate a simple to use 3-tier clinical-genomic risk system that is highly prognostic for distant metastasis, is more accurate than clinical risk, and can be easily incorporated into NCCN guidelines to inform treatment decisions.