Background: Cardiovascular disease (CVD) is a leading cause of death in cancer survivors. Predicting CVD risk in this population remains challenging. Risk prediction models that use machine learning algorithms could provide an objective method for accurate risk prediction to facilitate the prevention and management of CVD in cancer survivors. We evaluated previously tested machine learning algorithms and logistic regression (regularized by machine learning methods), in addition to testing and validating newer, more complex machine learning algorithms, for CVD prediction in cancer survivors. Methods: This multicenter study used a database of 3835 multiracial cancer survivors with 89 clinical, laboratory, and echocardiographic features over 20 years. Models were trained using repeated random and time‐split samples and tested on a separate cohort of 329 patients. Model performance was assessed using the area under the receiver operating characteristic curve. Results: Regularized logistic regression achieved an area under the receiver operating characteristic curve of 0.845 (heart failure), 0.783 (atrial fibrillation), 0.792 (coronary artery disease), and 0.806 (composite CVD). These are comparable to 0.837 and 0.848 for heart failure, using Bayesian additive regression tree and random forest as more advanced machine learning models, respectively. De novo composite CVD (post‐cancer diagnosis) was also predicted with an area under the receiver operating characteristic curve of 0.826 using regularized logistic regression, compared with 0.735 and 0.802 using decision tree and random forest, respectively. Conclusions: Regularized logistic regression and advanced machine learning models demonstrated similar predictive performance, with institutional transferability. These tools may support risk stratification and prevention strategies in cardio‐oncology using longitudinal data. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05377320.

Background: Peripheral artery disease (PAD) is a progressive atherosclerotic condition associated with significant morbidity and mortality. While PAD prevalence is comparable between sexes, women tend to have worse clinical outcomes, higher rates of disability, and are underdiagnosed and undertreated compared to men. This study examines sex differences in PAD presentation, diagnosis, and treatment outcomes using the Medical Information Mart for Intensive Care (MIMIC)-IV v3.1 database. Methods: A retrospective cohort study was conducted using electronic health records from the MIMIC-IV v3.1 database, identifying patients diagnosed with PAD between 2008 and 2022. Patient selection was based on International Classification of Diseases (ICD)-9 and ICD-10 codes. The following two datasets were constructed: an admission-level dataset (6468 admissions, 3913 unique patients) and a patient-level dataset aggregating multiple admissions per individual. Key variables included demographics, hospitalization details, procedure rates, and clinical outcomes. Sex-based comparisons were performed to assess disparities in disease burden, intervention rates, and mortality. Results: The study cohort comprised 3913 PAD patients. Women were significantly older than men at time of admission (mean 70.78 vs. 68.97 years, p < 0.05) and had lower rates of procedural intervention across all categories, including angioplasty (12.85% vs. 15.39%) and bypass grafting (14.74% vs. 16.98%). Despite similar Intensive Care Unit (ICU) admission rates (30.56% in females vs. 31.73% in males), women experienced greater delays in PAD diagnosis and treatment initiation. The in-hospital mortality rate was comparable between sexes (6.62% vs. 6.92%). Women presented more frequently with atypical or asymptomatic PAD, leading to delays in diagnosis and specialist referrals. Conclusions: This study highlights significant sex disparities in PAD diagnosis and management. Women with PAD are older at diagnosis, receive fewer procedural interventions, and experience delayed clinical recognition, contributing to a higher cumulative disease burden. These findings underscore the need for sex-specific diagnostic criteria, improved clinical awareness, and equitable treatment strategies to optimize PAD outcomes in women.

Venous occlusion is a complication of transvenous lead implantation and can lead to development of collateral circulation. Reopening of a previously closed left superior vena cava (SVC) has not been recorded in this context. A 71-year-old woman presented with worsening heart failure symptoms. Three years earlier, she had received a biventricular implantable cardioverter defibrillator for cardiac resynchronization therapy. During revision of a dislodged left ventricular lead, venography revealed occlusion of the right SVC and reopening of a left SVC. Despite anatomical challenges, left ventricular lead placement via the left SVC was achieved. This case highlights how obliterated venous structures can reopen after occlusion. Whereas a right-sided approach is recommended for lead placement, this case highlights a successful left SVC approach. Venous occlusion is a common complication of transvenous lead implantation and can lead to collateral circulation, including left SVC reopening. Although complex, lead implantation is possible.