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Development of a multivariate prediction model for nocturia, based on urinary tract etiologies

PURPOSE: The main objective of our study was to determine which combination of modifiable and non‐modifiable parameters that could discriminate patients with nocturia from those without nocturia. This was a post‐hoc analysis of 3 prospective, observational studies conducted in Ghent University. Part...

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Autores principales: Olesen, Tine Kold, Denys, Marie‐Astrid, Goessaert, An‐sophie, Bruneel, Elke, Decalf, Veerle, Helleputte, Thibault, Paul, Jerome, Gramme, Pierre, Everaert, Karel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767697/
https://www.ncbi.nlm.nih.gov/pubmed/30556626
http://dx.doi.org/10.1111/ijcp.13306
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author Olesen, Tine Kold
Denys, Marie‐Astrid
Goessaert, An‐sophie
Bruneel, Elke
Decalf, Veerle
Helleputte, Thibault
Paul, Jerome
Gramme, Pierre
Everaert, Karel
author_facet Olesen, Tine Kold
Denys, Marie‐Astrid
Goessaert, An‐sophie
Bruneel, Elke
Decalf, Veerle
Helleputte, Thibault
Paul, Jerome
Gramme, Pierre
Everaert, Karel
author_sort Olesen, Tine Kold
collection PubMed
description PURPOSE: The main objective of our study was to determine which combination of modifiable and non‐modifiable parameters that could discriminate patients with nocturia from those without nocturia. This was a post‐hoc analysis of 3 prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC) to compare characteristics between patients with and without nocturia. METHOD: This was a post hoc analysis of three prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC Study 1: adults with and without nocturia (n = 148); Study 2: patients ≥65 years with and without nocturnal LUTS (n = 54); Study 3: menopausal women before and after hormone replacement therapy (n = 43). All eligible patients (n = 183) completed a FVC during 24 hours (n = 13), 48 hours (n = 30) or 72 hours (n = 140). The combination of algorithms and number of determinants obtaining the best average area under the receiver operating curve (AUC‐ROC) led to the final model. Differences between groups were assessed using the AUC‐ROC and Mann‐ Whitney‐Wilcoxon tests. Holm corrections were applied for multiple statistical testing. Also, the stability of the feature selection was evaluated. RESULTS: The best discrimination was obtained when 13 determinants were included. However, a logistic regression model based on seven determinants selected with random forest had comparable discrimination including an optimal signature stability. It was able to discriminate almost perfectly between nights with and without nocturia. CONCLUSION: Relevant information to accomplish the excellent predictability of the model is; functional bladder capacity, 24 hours urine output, nocturnal output, age, BMI. The multivariate model used in this analysis provides new insights into combination therapy as it allows simulating the effect of different available treatment modalities and its combinations.
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spelling pubmed-67676972019-10-03 Development of a multivariate prediction model for nocturia, based on urinary tract etiologies Olesen, Tine Kold Denys, Marie‐Astrid Goessaert, An‐sophie Bruneel, Elke Decalf, Veerle Helleputte, Thibault Paul, Jerome Gramme, Pierre Everaert, Karel Int J Clin Pract Urology PURPOSE: The main objective of our study was to determine which combination of modifiable and non‐modifiable parameters that could discriminate patients with nocturia from those without nocturia. This was a post‐hoc analysis of 3 prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC) to compare characteristics between patients with and without nocturia. METHOD: This was a post hoc analysis of three prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC Study 1: adults with and without nocturia (n = 148); Study 2: patients ≥65 years with and without nocturnal LUTS (n = 54); Study 3: menopausal women before and after hormone replacement therapy (n = 43). All eligible patients (n = 183) completed a FVC during 24 hours (n = 13), 48 hours (n = 30) or 72 hours (n = 140). The combination of algorithms and number of determinants obtaining the best average area under the receiver operating curve (AUC‐ROC) led to the final model. Differences between groups were assessed using the AUC‐ROC and Mann‐ Whitney‐Wilcoxon tests. Holm corrections were applied for multiple statistical testing. Also, the stability of the feature selection was evaluated. RESULTS: The best discrimination was obtained when 13 determinants were included. However, a logistic regression model based on seven determinants selected with random forest had comparable discrimination including an optimal signature stability. It was able to discriminate almost perfectly between nights with and without nocturia. CONCLUSION: Relevant information to accomplish the excellent predictability of the model is; functional bladder capacity, 24 hours urine output, nocturnal output, age, BMI. The multivariate model used in this analysis provides new insights into combination therapy as it allows simulating the effect of different available treatment modalities and its combinations. John Wiley and Sons Inc. 2019-02-28 2019-08 /pmc/articles/PMC6767697/ /pubmed/30556626 http://dx.doi.org/10.1111/ijcp.13306 Text en © 2018 The Authors. International Journal of Clinical Practice Published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Urology
Olesen, Tine Kold
Denys, Marie‐Astrid
Goessaert, An‐sophie
Bruneel, Elke
Decalf, Veerle
Helleputte, Thibault
Paul, Jerome
Gramme, Pierre
Everaert, Karel
Development of a multivariate prediction model for nocturia, based on urinary tract etiologies
title Development of a multivariate prediction model for nocturia, based on urinary tract etiologies
title_full Development of a multivariate prediction model for nocturia, based on urinary tract etiologies
title_fullStr Development of a multivariate prediction model for nocturia, based on urinary tract etiologies
title_full_unstemmed Development of a multivariate prediction model for nocturia, based on urinary tract etiologies
title_short Development of a multivariate prediction model for nocturia, based on urinary tract etiologies
title_sort development of a multivariate prediction model for nocturia, based on urinary tract etiologies
topic Urology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767697/
https://www.ncbi.nlm.nih.gov/pubmed/30556626
http://dx.doi.org/10.1111/ijcp.13306
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