BACKGROUND:Inadequate hydration in the elderly is associated with increased morbidity and mortality. However, few studies have addressed the knowledge of elderly individuals regarding hydration in health and disease. Gaps in health literacy have been identified as a critical component in health maintenance, and promoting health literacy should improve outcomes related to hydration associated illnesses in the elderly.METHODS:We administered an anonymous survey to community-dwelling elderly (n = 170) to gauge their hydration knowledge.RESULTS:About 56% of respondents reported consuming >6 glasses of fluid/day, whereas 9% reported drinking ≤3 glasses. About 60% of respondents overestimated the amount of fluid loss at which moderately severe dehydration symptoms occur, and 60% did not know fever can cause dehydration. Roughly 1/3 were not aware that fluid overload occurs in heart failure (35%) or kidney failure (32%). A majority of respondents were not aware that improper hydration or changes in hydration status can result in confusion, seizures, or death.CONCLUSIONS:Overall, our study demonstrated that there were significant deficiencies in hydration health literacy among elderly. Appropriate education and attention to hydration may improve quality of life, reduce hospitalizations and the economic burden related to hydration-associated morbidity and mortality.

Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

Background and Aims 5-aminosalicylates (5-ASA) are frequently used in the management of Crohn's disease (CD). We used a de-identified administrative claims database to compare patterns and outcomes of continuing versus stopping 5-ASA in patients with CD who escalated to anti-metabolite monotherapy. Methods Patients with CD on 5-ASA who were new users of anti-metabolite monotherapy and followed for at least 12 months from OptumLabs® Data Warehouse. Three patterns of 5-ASA use were identified: stopped 5-ASA, short-term 5-ASA (use for < 6 months after starting anti-metabolites), or persistent 5-ASA (use for > 6 months after starting anti-metabolites). Outcomes (need for corticosteroids, risk of CD-related hospitalization and/or surgery, treatment escalation to biologic therapy) were compared using Cox proportional hazard analysis adjusting for key covariates, with a 12-month immortal time period. Results Of 3036 patients with CD who were new-users of anti-metabolite monotherapy, 667 (21.9%), 626 (20.6%), and 1743 (57.4%) stopped 5-ASA, used 5-ASA transiently or persistently, respectively. Compared to patients who stopped 5-ASA after starting anti-metabolites, persistent 5-ASA use was associated with a higher risk of corticosteroid use (HR, 1.24 [1.08-1.42]), without an increase in risk of CD-related hospitalization (HR, 1.21 [0.98-1.49]), CD-related surgery (HR, 1.28 [0.90-1.80]) or treatment escalation (HR,). Sensitivity analyses using a 3-month window after initiation of anti-metabolites to classify patients as continuing vs. stopping 5-ASA showed similar results. Residual confounding by disease severity could not be excluded. Conclusion 5-ASAs are frequently continued long-term even after escalation to anti-metabolite therapy in patients with CD but offer no clinical benefit over stopping 5-ASA.

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