Impact of Incident Cancer on Short-Term Coronary Artery Disease-Related Healthcare Expenditures Among Medicare Beneficiaries

Subclinical Atherosclerosis and Cardiovascular Events Among Patients With Colorectal Cancer

BACKGROUND

Prior studies have documented that patients with colorectal cancer (CRC) are at an increased risk of cardiovascular disease (CVD).

OBJECTIVES

To examine coronary artery calcium (CAC) as a marker of subclinical atherosclerosis and its association with major adverse cardiovascular events (MACE) in patients with CRC across the cancer treatment trajectory.

METHODS

Adults with newly diagnosed CRC were enrolled in the prospective ColoCare study from 2017 to 2024. CAC was measured from routine diagnostic computed tomography (CT) and positron emission tomography-CT scans at CRC diagnosis until 5 years post-diagnosis. Atherosclerosis was defined as the presence of CAC. We used multivariable-adjusted Fine and Gray models to assess the association between CAC and MACE risk, accounting for competing risks.

RESULTS

Among 300 CRC patients, the most common CVD risk factors at cancer diagnosis were hypertension (37%), hyperlipidemia (24%), and diabetes (14%). During follow-up (median = 5.3 years), 75 (25%) individuals experienced MACE: stroke (3%), new/worsening HF (9%), HF exacerbation requiring hospitalization (2%), coronary revascularization (3%), and death (19%). Among individuals with imaging at baseline (n = 101), 37 (36.6%) had CAC, and statins were not prescribed in 11 (55.0%) patients with moderate/high CAC. For those with serial imaging (n = 61), 31.1% showed worsening CAC and 3% developed new CAC. Baseline CAC conferred a higher risk of MACE (HR = 4.79; 95% CI: 1.05-21.75, p = 0.04) after accounting for cancer-related deaths as a competing risk.

CONCLUSIONS

Subclinical atherosclerosis and MACE are common among patients with CRC. Integrating CAC from routine cancer imaging can identify patients who may benefit from cardio-preventive treatment.

Immune checkpoint inhibitor use, multimorbidity and healthcare expenditures among older adults with late-stage melanoma

Background: The objective of this study is to assess the impact of immune checkpoint inhibitors (ICIs) and multimorbidity on healthcare expenditures among older patients with late-stage melanoma. Materials & methods: A retrospective longitudinal cohort study using Surveillance, Epidemiology and End Results linked with Medicare claims was conducted. Generalized linear mixed models were used to analyze adjusted relationships of ICI, multimorbidity and ICI-multimorbidity interaction on average healthcare expenditures. Results: Patients who received ICI and those who had multimorbidity had significantly higher average total healthcare expenditures compared with ICI nonusers and no multimorbidity. In the fully adjusted model using ICI-multimorbidity interaction, no excess cost was added by multimorbidity. Conclusion: Use of ICIs, regardless of multimorbidity, is associated with increased healthcare expenditures.

Digital Analysis in Breast Imaging

Breast imaging is a multimodal approach that plays an essential role in the diagnosis of breast cancer. Mammography, sonography, magnetic resonance, and image-guided biopsy are imaging techniques used to search for malignant changes in the breast or precursors of malignant changes in, e.g., screening programs or follow-ups after breast cancer treatment. However, these methods still have some disadvantages such as interobserver variability and the mammography sensitivity in women with radiologically dense breasts. In order to overcome these difficulties and decrease the number of false positive findings, improvements in imaging analysis with the help of artificial intelligence are constantly being developed and tested. In addition, the extraction and correlation of imaging features with special tumor characteristics and genetics of the patients in order to get more information about treatment response, prognosis, and also cancer risk are coming more and more in focus. The aim of this review is to address recent developments in digital analysis of images and demonstrate their potential value in multimodal breast imaging.