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Continuous diagnostic models for volume deficit in patients with acute diarrhea
BACKGROUND: Episodes of acute diarrhea lead to dehydration, and existing care algorithms base treatment around categorical estimates for fluid resuscitation. This study aims to develop models for the percentage dehydration (fluid deficit) in individuals with acute diarrhea, to better target treatmen...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8422628/ https://www.ncbi.nlm.nih.gov/pubmed/34488910 http://dx.doi.org/10.1186/s41182-021-00361-9 |
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author | Lee, J. Austin Qu, Kexin Gainey, Monique Kanekar, Samika S. Barry, Meagan A. Nasrin, Sabiha Alam, Nur H. Schmid, Christopher H. Levine, Adam C. |
author_facet | Lee, J. Austin Qu, Kexin Gainey, Monique Kanekar, Samika S. Barry, Meagan A. Nasrin, Sabiha Alam, Nur H. Schmid, Christopher H. Levine, Adam C. |
author_sort | Lee, J. Austin |
collection | PubMed |
description | BACKGROUND: Episodes of acute diarrhea lead to dehydration, and existing care algorithms base treatment around categorical estimates for fluid resuscitation. This study aims to develop models for the percentage dehydration (fluid deficit) in individuals with acute diarrhea, to better target treatment and avoid the potential sequelae of over or under resuscitation. METHODS: This study utilizes data from two prospective cohort studies of patients with acute diarrhea in Dhaka, Bangladesh. Data were collected on patient arrival, including weight, clinical signs and symptoms, and demographic information. Consecutive weights were obtained to determine the true volume deficit of each patient. Data were entered into two distinct forward stepwise regression logistic models (DHAKA for under 5 years and NIRUDAK for 5 years and over). RESULTS: A total of 782 patients were included in the final analysis of the DHAKA data set, and 2139 were included in the final analysis of the NIRUDAK data set. The best model for the DHAKA data achieved an R(2) of 0.27 and a root mean square error (RMSE) of 3.7 (compared to R(2) of 0.06 and RMSE of 5.5 with the World Health Organization child care algorithm) and selected 6 predictors. The best performance model for the NIRUDAK data achieved an R(2) of 0.28 and a RMSE of 2.6 (compared to R(2) of 0.08 and RMSE of 4.3 with the World Health Organization adolescent/adult care algorithm) and selected 7 predictors with 2 interactions. CONCLUSIONS: These are the first mathematical models for patients with acute diarrhea that allow for the calculation of a patient’s percentage dehydration (fluid deficit) and subsequent targeted treatment with fluid resuscitation. These findings are an improvement on existing World Health Organization care algorithms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41182-021-00361-9. |
format | Online Article Text |
id | pubmed-8422628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84226282021-09-09 Continuous diagnostic models for volume deficit in patients with acute diarrhea Lee, J. Austin Qu, Kexin Gainey, Monique Kanekar, Samika S. Barry, Meagan A. Nasrin, Sabiha Alam, Nur H. Schmid, Christopher H. Levine, Adam C. Trop Med Health Research BACKGROUND: Episodes of acute diarrhea lead to dehydration, and existing care algorithms base treatment around categorical estimates for fluid resuscitation. This study aims to develop models for the percentage dehydration (fluid deficit) in individuals with acute diarrhea, to better target treatment and avoid the potential sequelae of over or under resuscitation. METHODS: This study utilizes data from two prospective cohort studies of patients with acute diarrhea in Dhaka, Bangladesh. Data were collected on patient arrival, including weight, clinical signs and symptoms, and demographic information. Consecutive weights were obtained to determine the true volume deficit of each patient. Data were entered into two distinct forward stepwise regression logistic models (DHAKA for under 5 years and NIRUDAK for 5 years and over). RESULTS: A total of 782 patients were included in the final analysis of the DHAKA data set, and 2139 were included in the final analysis of the NIRUDAK data set. The best model for the DHAKA data achieved an R(2) of 0.27 and a root mean square error (RMSE) of 3.7 (compared to R(2) of 0.06 and RMSE of 5.5 with the World Health Organization child care algorithm) and selected 6 predictors. The best performance model for the NIRUDAK data achieved an R(2) of 0.28 and a RMSE of 2.6 (compared to R(2) of 0.08 and RMSE of 4.3 with the World Health Organization adolescent/adult care algorithm) and selected 7 predictors with 2 interactions. CONCLUSIONS: These are the first mathematical models for patients with acute diarrhea that allow for the calculation of a patient’s percentage dehydration (fluid deficit) and subsequent targeted treatment with fluid resuscitation. These findings are an improvement on existing World Health Organization care algorithms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41182-021-00361-9. BioMed Central 2021-09-06 /pmc/articles/PMC8422628/ /pubmed/34488910 http://dx.doi.org/10.1186/s41182-021-00361-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Lee, J. Austin Qu, Kexin Gainey, Monique Kanekar, Samika S. Barry, Meagan A. Nasrin, Sabiha Alam, Nur H. Schmid, Christopher H. Levine, Adam C. Continuous diagnostic models for volume deficit in patients with acute diarrhea |
title | Continuous diagnostic models for volume deficit in patients with acute diarrhea |
title_full | Continuous diagnostic models for volume deficit in patients with acute diarrhea |
title_fullStr | Continuous diagnostic models for volume deficit in patients with acute diarrhea |
title_full_unstemmed | Continuous diagnostic models for volume deficit in patients with acute diarrhea |
title_short | Continuous diagnostic models for volume deficit in patients with acute diarrhea |
title_sort | continuous diagnostic models for volume deficit in patients with acute diarrhea |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8422628/ https://www.ncbi.nlm.nih.gov/pubmed/34488910 http://dx.doi.org/10.1186/s41182-021-00361-9 |
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