Attention-deficit hyperactivity disorder medication use is associated with testosterone hypofunction-results from a national claims database analysis.

Male hypogonadism is not a risk associated with attention-deficit hyperactivity disorder (ADHD) stimulant medications, but recent studies have explored this connection. Though the pathophysiologic connection remains unclear, we predicted that long-term use of ADHD stimulant medications could increase the risk of hypogonadism in post-pubertal males. Utilizing TriNetX, LLC Research Network data from January 2000 through December 2019, men older than 18 with ADHD receiving long-term stimulant medication (>36 monthly prescriptions) were selected for the study population. Two control groups were constructed: individuals with ADHD but no stimulant medication use, and individuals without ADHD or stimulant medication use. A diagnosis of testicular hypofunction (ICD-10: E29.1) within five years of long-term ADHD stimulant medication use was the chosen primary outcome. After propensity score matching, 17,224 men were analyzed in each group. Of the men with long-term ADHD stimulant medication use, 1.20% were subsequently diagnosed with testicular hypofunction compared to 0.67% of individuals with ADHD without stimulant medication use (RR: 1.78, 95% CI: 1.42-2.23) and 0.68% in men without ADHD or stimulant medication use (RR: 1.75, 95% CI: 1.39-2.19). Therefore, chronic ADHD stimulant medication use was found to be significantly associated with a subsequent diagnosis of testicular hypofunction.

Compute Tomography Radiomics Analysis on Whole Pancreas Between Healthy Individual and Pancreatic Ductal Adenocarcinoma Patients: Uncertainty Analysis and Predictive Modeling.

Radiomics is a rapidly growing field that quantitatively extracts image features in a high-throughput manner from medical imaging. In this study, we analyzed the radiomics features of the whole pancreas between healthy individuals and pancreatic cancer patients, and we established a predictive model that can distinguish cancer patients from healthy individuals based on these radiomics features. Methods: We retrospectively collected venous-phase scans of contrast-enhanced computed tomography (CT) images from 181 control subjects and 85 cancer case subjects for radiomics analysis and predictive modeling. An attending radiation oncologist delineated the pancreas for all the subjects in the Varian Eclipse system, and we extracted 924 radiomics features using PyRadiomics. We established a feature selection pipeline to exclude redundant or unstable features. We randomly selected 189 cases (60 cancer and 129 control) as the training set. The remaining 77 subjects (25 cancer and 52 control) as a test set. We trained a Random Forest model utilizing the stable features to distinguish the cancer patients from the healthy individuals on the training dataset. We analyzed the performance of our best model by running 5-fold cross-validations on the training dataset and applied our best model to the test set. Results: We identified that 91 radiomics features are stable against various uncertainty sources, including bin width, resampling, image transformation, image noise, and segmentation uncertainty. Eight of the 91 features are nonredundant. Our final predictive model, using these 8 features, has achieved a mean area under the receiver operating characteristic curve (AUC) of 0.99 ± 0.01 on the training dataset (189 subjects) by cross-validation. The model achieved an AUC of 0.910 on the independent test set (77 subjects) and an accuracy of 0.935. Conclusion: CT-based radiomics analysis based on the whole pancreas can distinguish cancer patients from healthy individuals, and it could potentially become an early detection tool for pancreatic cancer.