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Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis

When scheduling surgeries for urolithiasis, the lack of information about the complexity of procedures and required instruments can lead to mismanagement, cancellations of elective surgeries and financial risk for the hospital. The aim of this study was to develop, train, and test prediction models...

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Autores principales: Huettenbrink, Clemens, Hitzl, Wolfgang, Pahernik, Sascha, Kubitz, Jens, Popeneciu, Valentin, Ell, Jascha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143218/
https://www.ncbi.nlm.nih.gov/pubmed/35629205
http://dx.doi.org/10.3390/jpm12050784
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author Huettenbrink, Clemens
Hitzl, Wolfgang
Pahernik, Sascha
Kubitz, Jens
Popeneciu, Valentin
Ell, Jascha
author_facet Huettenbrink, Clemens
Hitzl, Wolfgang
Pahernik, Sascha
Kubitz, Jens
Popeneciu, Valentin
Ell, Jascha
author_sort Huettenbrink, Clemens
collection PubMed
description When scheduling surgeries for urolithiasis, the lack of information about the complexity of procedures and required instruments can lead to mismanagement, cancellations of elective surgeries and financial risk for the hospital. The aim of this study was to develop, train, and test prediction models for ureterorenoscopy. Routinely acquired Computer Tomography (CT) imaging data and patient data were used as data sources. Machine learning models were trained and tested to predict the need for laser lithotripsy and to forecast the expected duration of ureterorenoscopy on the bases of 474 patients over a period from May 2016 to December 2019. Negative predictive value for use of laser lithotripsy was 92%, and positive predictive value 91% before application of the reject option, increasing to 97% and 94% after application of the reject option. Similar results were found for duration of surgery at ≤30 min. This combined prediction is possible for 54% of patients. Factors influencing prediction of laser application and duration ≤30 min are age, sex, height, weight, Body Mass Index (BMI), stone size, stone volume, stone density, and presence of a ureteral stent. Neuronal networks for prediction help to identify patients with an operative time ≤30 min who did not require laser lithotripsy. Thus, surgical planning and resource allocation can be optimised to increase efficiency in the Operating Room (OR).
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spelling pubmed-91432182022-05-29 Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis Huettenbrink, Clemens Hitzl, Wolfgang Pahernik, Sascha Kubitz, Jens Popeneciu, Valentin Ell, Jascha J Pers Med Article When scheduling surgeries for urolithiasis, the lack of information about the complexity of procedures and required instruments can lead to mismanagement, cancellations of elective surgeries and financial risk for the hospital. The aim of this study was to develop, train, and test prediction models for ureterorenoscopy. Routinely acquired Computer Tomography (CT) imaging data and patient data were used as data sources. Machine learning models were trained and tested to predict the need for laser lithotripsy and to forecast the expected duration of ureterorenoscopy on the bases of 474 patients over a period from May 2016 to December 2019. Negative predictive value for use of laser lithotripsy was 92%, and positive predictive value 91% before application of the reject option, increasing to 97% and 94% after application of the reject option. Similar results were found for duration of surgery at ≤30 min. This combined prediction is possible for 54% of patients. Factors influencing prediction of laser application and duration ≤30 min are age, sex, height, weight, Body Mass Index (BMI), stone size, stone volume, stone density, and presence of a ureteral stent. Neuronal networks for prediction help to identify patients with an operative time ≤30 min who did not require laser lithotripsy. Thus, surgical planning and resource allocation can be optimised to increase efficiency in the Operating Room (OR). MDPI 2022-05-12 /pmc/articles/PMC9143218/ /pubmed/35629205 http://dx.doi.org/10.3390/jpm12050784 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huettenbrink, Clemens
Hitzl, Wolfgang
Pahernik, Sascha
Kubitz, Jens
Popeneciu, Valentin
Ell, Jascha
Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis
title Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis
title_full Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis
title_fullStr Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis
title_full_unstemmed Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis
title_short Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis
title_sort neural networks modeling for prediction of required resources for personalized endourologic treatment of urolithiasis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143218/
https://www.ncbi.nlm.nih.gov/pubmed/35629205
http://dx.doi.org/10.3390/jpm12050784
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