Background: Anticholinergic exposure is associated with dementia risk; however, the mechanisms for this association remain unclear. The objective of this study was to examine the association between anticholinergic exposure and white matter hyperintensity (WMH) burden.
Methods: This was a retrospective analysis of data from the Adult Changes in Thought (ACT) study, a prospective cohort study among adults aged ≥65 years on dementia risk factors.
Purpose: Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software.
View Article and Find Full Text PDFObjective: The aim of this study was to determine if magnetic resonance-guided focused ultrasound (MRgFUS) is cost-effective compared with medication, for refractory pain from bone metastases in the United States.
Methods: We constructed a Markov state transition model using TreeAge Pro software (TreeAge Software, Inc., Williamstown, MA, USA) to model costs, outcomes, and the cost-effectiveness of a treatment strategy using MRgFUS for palliative treatment of painful bone metastases compared with a Medication Only strategy (Figure 1).
Objective: Cost-effectiveness analyses (CEAs) contribute to informed decision making, at both the practitioner and societal levels; therefore, understanding CEAs is valuable for radiologists. In light of the recently published National Lung Cancer Screening Trial (NLST) CEA, we aim to explain the terminology, methods, and heterogeneity of CEAs.
Conclusion: We compared the NLST results to two example lung cancer screening CEAs (which do not rely on NLST data).