Turkish validation and reliability of the symptoms of Lower Urinary Tract Dysfunction Research Network Symptom Index‐29 (LURN SI‐29) questionnaire in patients with lower urinary tract symptoms

Author(s):  
Meftun Culpan ◽  
Hazal C. Acar ◽  
David Cella ◽  
Ahmet Tahra ◽  
Mehmet C. Cakici ◽  
...  
2021 ◽  
Author(s):  
Victor P. Andreev ◽  
Margaret E. Helmuth ◽  
Gang Liu ◽  
Abigail R. Smith ◽  
Robert M. Merion ◽  
...  

ABSTRACTWe present a novel methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Identifying subtypes within this group would potentially lead to better diagnosis and treatment decision-making. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center prospective observational cohort study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five distinct clusters of women with LUTS were identified using the developed methodology. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. Rationale and thought process are described for selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.


2019 ◽  
Vol 38 (6) ◽  
pp. 1751-1759
Author(s):  
David Cella ◽  
Abigail R. Smith ◽  
James W. Griffith ◽  
Kathryn E. Flynn ◽  
Catherine S. Bradley ◽  
...  

2019 ◽  
Vol 201 (Supplement 4) ◽  
Author(s):  
David Cella ◽  
Abigail R. Smith ◽  
James W. Griffith* ◽  
Kathryn E. Flynn ◽  
Brenda W. Gillespie ◽  
...  

Author(s):  
Scott R Bauer ◽  
Stephanie L Harrison ◽  
Peggy M Cawthon ◽  
Angela Senders ◽  
Stacey A Kenfield ◽  
...  

Abstract Background Adiposity increases risk for male lower urinary tract symptoms (LUTS), although longitudinal studies have produced conflicting results. No prior studies have evaluated longitudinal associations of changes in adiposity with concurrent LUTS severity among older men. Methods We used repeated adiposity measurements from dual-energy x-ray absorptiometry (DXA), body mass index (BMI), and American Urological Association Symptom Index (AUASI) measured at four study visits over a 9-year period among 5949 men enrolled in the Osteoporotic Fractures in Men (MrOS) study. Linear mixed effect models adjusted for age, health-related behaviors, and comorbidities were created to evaluate the association between baseline and change in visceral adipose tissue (VAT) area, total fat mass, and BMI with change in LUTS severity measured by the AUASI. Results A non-linear association was observed between baseline VAT area and change in AUASI: men in baseline VAT tertile (T) 2 had a lower annual increase in AUASI score compared to men in T1 and T3 (T2 versus T1: β=-0.07; 95% CI -0.12, -0.03; P= 0.008; T3 versus T1: NS) but differences were small. No significant associations were observed between change in VAT area and change in AUASI score. Neither baseline tertiles nor change in total fat mass or BMI were associated with change in AUASI score. Conclusions Changes in VAT area, total fat mass, and BMI were not associated with change in LUTS severity in this cohort. Thus, despite other health benefits, interventions targeting adiposity alone are unlikely to be effective for preventing or treating LUTS among older men.


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