Translation and validation of the Italian version of the Wisconsin Stone Quality of Life Questionnaire (I-WISQOL) for assessing quality of life in patients with urolithiasis

Salciccia, Stefano; Maggi, Martina; Frisenda, Marco; Guacci, Lucia Finistauri; Hoxha, Sanie; Licari, Leslie C; Viscuso, Pietro; Gentilucci, Alessandro; Giudice, Francesco Del; Berardinis, Ettore De; Cattarino, Susanna; Mariotti, Gianna; Tufano, Antonella; Dominicis, Mauro DE; Ricciuti, Gian Piero; Sciarra, Alessandro; Penniston, Kristina L.; Moriconi, Martina

doi:10.23736/s2724-6051.23.04882-6

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…The recurrent progression of kidney stones is significantly influenced by clinical risk factors and lithogenic environments, including metabolic changes with diet and medication [4,6]. Kidney stones are a chronic condition that negatively effects the quality of life of patients in various ways, primarily due to hospitalizations and renal colic's [7]. Kidney stone patients experience severe recurrent pain during renal colic's, which can sometimes require surgical interventions.…”

Section: Introductionmentioning

confidence: 99%

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

Aiumtrakul,

Thongprayoon,

Arayangkool

et al. 2024

JPM

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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.

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Section: Introductionmentioning

confidence: 99%

“…These factors disrupt their normal life activities, work and family time, and sleep patterns. Moreover, patients suffer from psychological distress, such as fear of recurrence or worsening of their condition, and social well-being, including feelings of isolation, as well as problems with intimacy [7,8].…”

Section: Introductionmentioning

confidence: 99%

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

Aiumtrakul,

Thongprayoon,

Arayangkool

et al. 2024

JPM

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“…As kidney stones become more prevalent, the deleterious impact on global health and quality of life increases. Patients with kidney stones suffer from recurrent severe renal colic pain and frequent medical visits that disrupt daily life, work, family time, and sleep [ 28 , 29 ]. Moreover, nephrolithiasis is linked to chronic kidney disease (CKD), with the mechanism primarily attributed to obstructive uropathy or pyelonephritis [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

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

Aiumtrakul,

Thongprayoon,

Suppadungsuk

et al. 2024

Clinics and Practice

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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.

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Unveiling the impact of stone disease: enhancing quality of life through comprehensive care

ESPERTO,

PIETROPAOLO,

EMILIANI

et al. 2023

Minerva Urol Nephrol

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Translation and validation of the Italian version of the Wisconsin Stone Quality of Life Questionnaire (I-WISQOL) for assessing quality of life in patients with urolithiasis

Cited by 3 publications

References 28 publications

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

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

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

Unveiling the impact of stone disease: enhancing quality of life through comprehensive care

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