Model of Chronic Itch in Aged Mice: Beneficial Effects of Drugs Affecting Descending Modulatory Systems.

Pruritus in the elderly, particularly those cases without skin dryness or other identifiable causes, makes treatment challenging due to the lack of evidence regarding the therapeutic effects of antipruritics. This study proposes an age-related alloknesis mouse model for an evaluation system for such cases, and aimed to investigate the effectiveness and mechanisms of action of several drugs commonly used as antipruritics in Japan, utilizing this model. Mice 69-80 weeks old were used as aged mice, and the level of mechanical alloknesis was counted as the number of scratching behaviours in response to innocuous stimuli. Bepotastine, neurotropin, pregabalin, baricitinib, and abrocitinib were used as antipruritics, and yohimbine and methysergide as inhibitors of the descending inhibitory pathway. The findings suggest that mechanical alloknesis in aged mice is a suitable animal model for assessing pruritus in the elderly without xerosis, and pregabalin, neurotropin, baricitinib, and abrocitinib may be effective antipruritics in the elderly through activating both the noradrenergic and serotonergic descending inhibitory pathways. These findings may be useful for the selection of antipruritics for pruritus in the elderly without skin lesions or dryness.

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission. METHODS: We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system. RESULTS: Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital. CONCLUSIONS: Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.