Approximately 20% of men with metastatic castration-sensitive prostate cancer (mCSPC) progress within 1 year of treatment, and biomarkers to identify them up front are lacking. In a randomized phase III trial in men with mCSPC (SWOG S1216), higher baseline circulating tumor cells (CTCs) were prognostic of inferior outcomes. We aimed to validate these findings and interrogate corresponding tumor genomic profiles. Consecutively seen men with newly diagnosed mCSPC undergoing systemic therapy and baseline CTC enumeration by CellSearch assay were included. Gene alterations were determined by comprehensive genomic profiling of tumor tissue by Clinical Laboratory Improvement Amendments—certified lab. The relationship between categorized CTC counts and both progression-free survival (PFS) and overall survival (OS) was assessed in the context of Cox proportional hazards models, both unadjusted and adjusted for age, Gleason score, PSA at androgen-deprivation therapy initiation, disease volume, de novo status, treatment intensification, and number of altered genes. Overall, 103 patients were included in the analysis. On multivariate analysis high CTCs (≥ 5 vs. 0) were associated with poorer PFS [HR, 4.52; 95% confidence interval (CI), 1.84–11.11; P = 0.001) and OS (HR, 3.59; 95% CI, 0.95–13.57; P = 0.060). Patients with higher CTC counts had a greater number of altered genes and total number of alterations (all P < 0.02). In this article, for the first time, we externally validate the association of higher CTC counts with inferior survival outcomes in men with mCSPC and show a distinct associated tumor genomic landscape. These findings may improve prognostication, patient counseling, and treatment selection in men with mCSPC.

Background: Only 74% of infectious diseases (ID) training positions were filled in the 2022 fellowship match, indicating a need to find increasingly novel and creative solutions for ID recruitment and outreach. Methods: The websites of 2321 universities and 181 medical schools across the United States were manually searched for the presence of undergraduate clubs and interest groups, respectively, for multiple medical specialties and subspecialties, including ID. Geographic data were used to compare the proximity of ID fellowships to undergraduate institutions. Results: ID student groups were extremely rare among the categories studied throughout undergraduate institutions (6 out of 2048, or 0.29%). Only 58 of 163 (35.6%) medical schools nationwide included an ID student group. Geographic comparison found that every adult ID fellowship is in the same county and/or city as at least 1 undergraduate institution and 28.5% of adult ID fellowships are in the same zip code as at least 1 undergraduate institution. Conclusions: The relative paucity of ID student interest groups presents an opportunity for the ID community to begin outreach and recruitment at the undergraduate and medical student levels, specifically through student groups.

Introduction: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with coronary artery disease (CAD), also known as ischemic heart disease (IHD), responsible for approximately 13% of global deaths in 2021. Studies applying machine learning (ML) and deep learning (DL) to heart disease classification have demonstrated promising results in risk prediction and feature extraction. Background/Objectives: In this study, we develop an AI/ML framework to predict and classify ischemic heart disease risk using publicly available datasets, the Framingham Heart Study and the Cleveland subset of the UCI Heart Disease dataset, along with explanations for how predictions were made by a process called SHAP (SHapley Additive exPlanations). Methods: We implemented a leakage-controlled machine learning pipeline that included data cleaning, stratified 80/20 train-test splitting, training-fold-only feature scaling and class balancing, 5-fold hyperparameter tuning, SHAP interpretability, and Brier score-based calibration assessment. Logistic regression, random forest, K-nearest neighbors, XGBoost, and a feedforward neural network were evaluated on the Framingham dataset and the Cleveland subset of the UCI Heart Disease dataset. Performance was assessed using accuracy, precision, recall, F1-score, Matthews correlation coefficient, AUROC, and Brier score. Results: After leakage-controlled evaluation, Framingham performance was more modest than in the preliminary analysis. Logistic regression achieved the highest AUROC on the Framingham dataset (0.7234), while random forest achieved the lowest Brier score (0.1750), and the feedforward neural network achieved the highest accuracy (0.7719). On the Cleveland subset, logistic regression achieved the strongest threshold-based performance (accuracy 0.8667, precision 0.8571, recall 0.8571, F1-score 0.8571, MCC 0.7321), whereas K-nearest neighbors achieved the highest AUROC (0.9531) and lowest Brier score (0.0942). SHAP highlighted systolic blood pressure, smoking status, and hypertension as influential predictors (Framingham) and number of major vessels, chest pain type, thallium stress-test result (thal; normal, fixed defect, or reversible defect), and age (Cleveland) as top predictors. Conclusions: Optimal model performance is dataset-dependent, and SHAP enhances clinical interpretability. Broader access to high-quality, de-identified medical data could accelerate reproducible ML research in cardiology.

133 Background: In the PEACE-1 trial (Fizazi, ESMO 2021), in pts with dn-hv-mCSPC, the addition of abiraterone to ADT + docetaxel (triplet therapy arm) resulted in a median OS of 61 months (OS on ADT + docetaxel arm was 42 months). However, independent contribution of docetaxel to efficacy of ADT + abiraterone in the triplet therapy arm of PEACE-1 trial is not clear. Furthermore, the efficacy of an novel hormonal therapy (NHT) specifically in dn-hv-mCSPC population has not been reported (LATITUDE trial’s eligibility did not include volume status). Herein, our objective was to assess PFS and OS in a cohort of dn-hv-mCSPC pts undergoing intensified ADT with either NHT (i.e. novel androgen receptor or androgen synthesis inhibitors) or docetaxel. Methods: In this IRB-approved study, patient-level data were collected retrospectively. Eligibility: presence of dn-hv-mCSPC (hv per CHAARTED criteria) undergoing intensified ADT with either NHT or docetaxel started within 3 months of diagnosis. Study endpoints: PFS was calculated per PCWG-2 - defined PSA progression or radiographic progression or clinical progression whichever occurred first; OS was defined as start of therapy to date of death or censored at last follow-up. A multivariate analysis using the Cox proportional hazards (Cox-ph) model was used; the relationship between treatment intensification with both PFS and OS was assessed and adjusted for age at diagnosis, Gleason score, presence of visceral metastases, and PSA at baseline. Results: 85 pts with dn-hv-mCSPC were included: 45 received ADT + NHT; 40 received ADT + docetaxel. In the NHT vs docetaxel arms: median PFS were 23 vs 14 months; and median OS were 63 vs 36 months respectively. See the table for Cox-ph and adjusted co-variate results. Conclusions: We for the first-time report the real-world survival outcomes with ADT + NHT specifically in pts with dn-hv-mCSPC. In addition, in these hypothesis-generating data, the OS of pts with dn-hv-mCSPC undergoing ADT + NHT or ADT + docetaxel were similar to those in the triplet therapy arm or ADT + docetaxel arm of PEACE-1 trial respectively. A randomized trial in pts with dn-hv-mCSPC comparing ADT + NHT vs triplet therapy may better characterize the benefit of the docetaxel component in the triplet therapy.[Table: see text]

BACKGROUND: Static cold storage of cardiac allografts at 4–8 °C or 10 °C has yielded promising heart transplant outcomes compared with ice storage. However, direct comparisons between these 2 preservation temperatures are lacking. This dual-center study is the first to compare adult heart transplant outcomes using allografts preserved at 10 °C versus 4–8 °C static cold storage. METHODS: All single-organ, donation-after-brain-death adult heart transplants performed at 2 high-volume centers between January 2020 and April 2025 were retrospectively analyzed. Multiorgan, adult congenital, and donation-after-circulatory-death cases were excluded. A 3:1 nearest-neighbor propensity score matching was applied using a standardized mean difference <20% to create balanced cohorts. Firth logistic regression and quantile regression were used to evaluate categorical and continuous outcomes in unmatched and matched cohorts. RESULTS: Among 365 recipients, 113 received allografts preserved at 10 °C and 252 at 4–8 °C. The 10 °C group had higher donor and recipient risk profiles, including older donors (37 [28–43] versus 31 [24–39] years; P =0.004), greater donor-recipient sex mismatch, and more frequent donor undersizing by predicted heart mass ratio. Recipients in this cohort were older and had more re-sternotomies and higher serum creatinine levels at transplant. Patients in the 4–8 °C group were more often listed as Status 2. After matching, 10 °C preservation was associated with less primary graft dysfunction (2 [2.9%] versus 19 [14.6%]; P =0.008), less left and right ventricular dysfunction, reduced new intraaortic balloon pump use, and improved 1-year survival (95.7% versus 86.2%; P =0.02). Other outcomes, including cardiac indices and posttransplant length of stay, were not significantly different between groups. CONCLUSIONS: Preservation of cardiac allografts at 10 °C may yield superior early graft function compared with 4–8 °C static cold storage. However, further prospective studies are required to delineate the optimal donor heart preservation temperature.