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Predictive Modeling and Analyses of National Emergency Department Data for Improving Patient Outcomes of Nephrolithiasis

Background: Nephrolithiasis, or kidney stones, imposes a significant burden of disease in the United States and comes with considerable costs, pain, and morbidity. The exact cause of stone formation is undefined, but the formation is a process. Risk factors include environmental, diabetes, obesity,...

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Autor principal: Stewart, Kori L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337510/
https://www.ncbi.nlm.nih.gov/pubmed/37448404
http://dx.doi.org/10.7759/cureus.40297
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author Stewart, Kori L
author_facet Stewart, Kori L
author_sort Stewart, Kori L
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description Background: Nephrolithiasis, or kidney stones, imposes a significant burden of disease in the United States and comes with considerable costs, pain, and morbidity. The exact cause of stone formation is undefined, but the formation is a process. Risk factors include environmental, diabetes, obesity, metabolic syndromes, low fluid intake, dehydration, diet, inflammatory bowel disorders, irritable bowel syndrome (IBS), and genetics. Laboratory testing and appropriate diagnostic imaging studies are two key components of assessment and prevention. Methods: This is a retrospective, quantitative study utilizing the Healthcare Cost and Utilization Project’s (HCUP) National Emergency Department Sample (NEDS)'s existing databases from 2012 to 2014 to classify outcomes for nephrolithiasis patients. International Classification of Diseases, Ninth Revision (ICD-9-CM) billing codes related to nephrolithiasis, relevant medical imaging exams, and procedural and surgical billing codes for interventions and procedures were selected. Descriptive statistical analyses as well as multiple regression models were used to analyze frequencies and percentages of variables and the relationship of the data, identify co-linearity among variables, and predict outcomes.  Results: The study sample includes a total of 509,192 emergency department (ED) visits for nephrolithiasis from 2012 to 2014 and reveals that IBS patients are two times more likely to require intervention. Stepwise regression models yield P-values of 0.004 for gender, 0.017 and 0.018 for minor diagnostic procedures, 0.006 and 0.001 for minor therapeutic procedures, and 0.000 and 0.001 for major therapeutic procedures when predicting total cost of care, and have a statistically significant impact on patient outcomes of nephrolithiasis. Conclusions: This research offers an investigation of the prevalence of nephrolithiasis based on age, gender, and co-morbidity, specifically IBS, and is the first to report on patient outcomes. This analysis also provides clinicians with recommendations to utilize for a comprehensive assessment of nephrolithiasis patients in the ED. 
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spelling pubmed-103375102023-07-13 Predictive Modeling and Analyses of National Emergency Department Data for Improving Patient Outcomes of Nephrolithiasis Stewart, Kori L Cureus Emergency Medicine Background: Nephrolithiasis, or kidney stones, imposes a significant burden of disease in the United States and comes with considerable costs, pain, and morbidity. The exact cause of stone formation is undefined, but the formation is a process. Risk factors include environmental, diabetes, obesity, metabolic syndromes, low fluid intake, dehydration, diet, inflammatory bowel disorders, irritable bowel syndrome (IBS), and genetics. Laboratory testing and appropriate diagnostic imaging studies are two key components of assessment and prevention. Methods: This is a retrospective, quantitative study utilizing the Healthcare Cost and Utilization Project’s (HCUP) National Emergency Department Sample (NEDS)'s existing databases from 2012 to 2014 to classify outcomes for nephrolithiasis patients. International Classification of Diseases, Ninth Revision (ICD-9-CM) billing codes related to nephrolithiasis, relevant medical imaging exams, and procedural and surgical billing codes for interventions and procedures were selected. Descriptive statistical analyses as well as multiple regression models were used to analyze frequencies and percentages of variables and the relationship of the data, identify co-linearity among variables, and predict outcomes.  Results: The study sample includes a total of 509,192 emergency department (ED) visits for nephrolithiasis from 2012 to 2014 and reveals that IBS patients are two times more likely to require intervention. Stepwise regression models yield P-values of 0.004 for gender, 0.017 and 0.018 for minor diagnostic procedures, 0.006 and 0.001 for minor therapeutic procedures, and 0.000 and 0.001 for major therapeutic procedures when predicting total cost of care, and have a statistically significant impact on patient outcomes of nephrolithiasis. Conclusions: This research offers an investigation of the prevalence of nephrolithiasis based on age, gender, and co-morbidity, specifically IBS, and is the first to report on patient outcomes. This analysis also provides clinicians with recommendations to utilize for a comprehensive assessment of nephrolithiasis patients in the ED.  Cureus 2023-06-12 /pmc/articles/PMC10337510/ /pubmed/37448404 http://dx.doi.org/10.7759/cureus.40297 Text en Copyright © 2023, Stewart et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Emergency Medicine
Stewart, Kori L
Predictive Modeling and Analyses of National Emergency Department Data for Improving Patient Outcomes of Nephrolithiasis
title Predictive Modeling and Analyses of National Emergency Department Data for Improving Patient Outcomes of Nephrolithiasis
title_full Predictive Modeling and Analyses of National Emergency Department Data for Improving Patient Outcomes of Nephrolithiasis
title_fullStr Predictive Modeling and Analyses of National Emergency Department Data for Improving Patient Outcomes of Nephrolithiasis
title_full_unstemmed Predictive Modeling and Analyses of National Emergency Department Data for Improving Patient Outcomes of Nephrolithiasis
title_short Predictive Modeling and Analyses of National Emergency Department Data for Improving Patient Outcomes of Nephrolithiasis
title_sort predictive modeling and analyses of national emergency department data for improving patient outcomes of nephrolithiasis
topic Emergency Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337510/
https://www.ncbi.nlm.nih.gov/pubmed/37448404
http://dx.doi.org/10.7759/cureus.40297
work_keys_str_mv AT stewartkoril predictivemodelingandanalysesofnationalemergencydepartmentdataforimprovingpatientoutcomesofnephrolithiasis