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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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). |
format | Online Article Text |
id | pubmed-9143218 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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