Complications of Corticosteroid Therapy: A Comprehensive Literature Review.

Relevance to Patient Care and Clinical Practice: Corticosteroids are among the most prescribed medications, particularly during the COVID-19 era. The literature has clearly highlighted the dangers of prolonged, high-dose corticosteroid use, which is important for clinicians to consider before treating patients in their clinical practices. Objective: The objective of this article is to review the literature on complications of corticosteroid use, review corticosteroid pharmacokinetics, and provide an updated reference on risks associated with corticosteroid therapy, especially at higher doses. Data Sources: A conventional literature search of PubMed was conducted without restrictions on publication date. Search terms included "corticosteroids," "avascular necrosis," "gastrointestinal bleeding," and "complications." Study Selection and Data Extraction: Pertinent systematic review/meta-analyses and randomized controlled trials were reviewed for study inclusion. Data Synthesis: Corticosteroids were associated with complications including avascular necrosis, gastrointestinal bleeding, myocardial infarction, heart failure, cerebrovascular events, diabetes mellitus, psychiatric syndromes, ophthalmic complications, tuberculosis reactivation, and bacterial sepsis. Increased daily and cumulative doses were associated with increased excess risk of complications. Cumulative doses greater than 430 mg prednisone equivalent were shown to increase the excess risk of avascular necrosis, with progressively higher rates with higher doses. Risk of gastrointestinal bleeding was significantly increased with corticosteroid usage in the in-patient but not out-patient setting. Conclusion: Since corticosteroids have been associated with the aforementioned severe complications and frequent medicolegal malpractice claims, counseling and informed consent should be performed when prescribing moderate-high dosages of corticosteroids. Further research is needed to characterize the long-term effects of corticosteroid usage in COVID-19 patients.

Can the Timed Up & Go Test and Montreal Cognitive Assessment predict outcomes in patients waitlisted for renal transplant?

PURPOSE: Frail patients who undergo renal transplantation (RT) have more complications; however, little is known if these patients can sustain the wait to RT. We used the Timed Up and Go Test (TUGT) and Montreal Cognitive Assessment (MoCA) to determine outcomes of RT candidates. METHODS: In this retrospective study, 526 RT candidates underwent TUGT and MoCA (2015-2019) and were divided into "favorable" (transplanted or remained on the list) or "unfavorable" (not listed, removed from list, or died) outcome. Demographics, education, language, comorbidities, dialysis type, use of a walking device, TUGT, and MoCA were compared by outcome. RESULTS: Overall, 230 patients (43.7%) passed TUG, 268 (51%) passed MoCA, 133 (25.3%) passed both, and 161 (30.6%) failed both tests. Multivariate analysis demonstrated age ≥ 65 (OR 1.58, CI 1.03-2.43), cardiac disease (OR 3.09, CI 2.02-4.72), ≥36 months on dialysis (OR 1.80, CI 1.24-2.69), EPTS < 20% at time of MoCA (OR 0.26, CI 0.07-0.98), and failing TUGT (OR 2.14, CI 1.43-3.19) were associated with unfavorable outcome. Failing MoCA was not associated with outcome. CONCLUSIONS: MoCA test results were not associated with RT waitlist outcomes; however, passing the TUGT was associated with receiving RT or remaining on the list. Additional studies are needed to validate this and determine outcome after RT.

Global Trends in Kidney Stone Awareness: A Time Series Analysis from 2004-2023.

BACKGROUND: Despite the prevalence and incidence of kidney stones progressively increasing worldwide, public awareness of this condition remains unclear. Understanding trends of awareness can assist healthcare professionals and policymakers in planning and implementing targeted health interventions. This study investigated online search interest in "kidney stone" by analyzing Google Trends, focusing on stationarity of the trends and predicting future trends. METHODS: We performed time series analysis on worldwide Google monthly search data from January 2004 to November 2023. The Augmented Dickey-Fuller (ADF) test was used to assess the stationarity of the data, with a p-value below 0.05 indicating stationarity. Time series forecasting was performed using the autoregressive integrated moving average to predict future trends. RESULTS: The highest search interest for "kidney stone" (score 100) was in August 2022, while the lowest was in December 2007 (score 36). As of November 2023, search interest remained high, at 92. The ADF test was significant (p = 0.023), confirming data stationarity. The time series forecasting projected continued high public interest, likely reflecting ongoing concern and awareness. Notably, diverse regions such as Iran, the Philippines, Ecuador, the United States, and Nepal showed significant interest, suggesting widespread awareness of nephrolithiasis. CONCLUSION: This study highlighted that "kidney stone" is a consistently relevant health issue globally. The increase and stationarity of search trends, the forecasted sustained interest, and diverse regional interest emphasize the need for collaborative research and educational initiatives. This study's analysis serves as a valuable tool for shaping future healthcare policies and research directions in addressing nephrolithiasis related health challenges.

Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention.

Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5-8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.