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Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study

BACKGROUND: This study was performed to construct and validate an early risk warning model of urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD). METHODS: Eligible patients with NLUTD admitted to Shenzhen Longcheng hospital from January 2017 to June 2021 were...

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Autores principales: Zhou, Liqiong, Liang, Surui, Shuai, Qin, Fan, Chunhua, Gao, Linghong, Cai, Wenzhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9080428/
https://www.ncbi.nlm.nih.gov/pubmed/35539015
http://dx.doi.org/10.7717/peerj.13388
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author Zhou, Liqiong
Liang, Surui
Shuai, Qin
Fan, Chunhua
Gao, Linghong
Cai, Wenzhi
author_facet Zhou, Liqiong
Liang, Surui
Shuai, Qin
Fan, Chunhua
Gao, Linghong
Cai, Wenzhi
author_sort Zhou, Liqiong
collection PubMed
description BACKGROUND: This study was performed to construct and validate an early risk warning model of urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD). METHODS: Eligible patients with NLUTD admitted to Shenzhen Longcheng hospital from January 2017 to June 2021 were recruited for model construction, internal validation and external validation. The first time point of data collection was within half a month of patients first diagnosed with NLUTD. The second time point was at the 6-month follow-up. The early warning model was constructed by logistic regression. The model prediction effects were validated using the area under the Receiver Operating Characteristic curve, the Boostrap experiment and the calibration plot of the combined data. The model was externally validated using sensitivity, specificity and accuracy. RESULTS: Six predictors were identified in the model, namely patients ≥65 years old (OR = 2.478, 95%CI [1.215– 5.050]), female (OR = 2.552, 95%CI [1.286–5.065]), diabetes (OR = 2.364, 95%CI) [1.182–4.731]), combined with urinary calculi (OR = 2.948, 95%CI [1.387–6.265]), indwelling catheterization (OR = 1.988, 95%CI [1.003 –3.940]) and bladder behavior training intervention time ≥2 weeks (OR = 2.489, 95%CI [1.233–5.022]); and the early warning model formula was Y = 0.907 ×  age+ 0.937 × sex + 0.860 × diabetes +1.081 × combined with urinary calculi+ 0.687 × indwelling catheterization+ 0.912 × bladder behavior training intervention time-2.570. The results show that the area under the ROC curve is 0.832, which is close to that of 1,000 Bootstrap internal validation (0.828). The calibration plot shows that the early warning model has good discrimination ability and consistency. The external validation shows the sensitivity is 62.5%, the specificity is 100%, and the accuracy is 90%. CONCLUSION: The early warning model for urinary tract infection in patients with NLUTD is suitable for clinical practice, which can provide targeted guidance for the evaluation of urinary tract infection in patients with NLUTD.
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spelling pubmed-90804282022-05-09 Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study Zhou, Liqiong Liang, Surui Shuai, Qin Fan, Chunhua Gao, Linghong Cai, Wenzhi PeerJ Epidemiology BACKGROUND: This study was performed to construct and validate an early risk warning model of urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD). METHODS: Eligible patients with NLUTD admitted to Shenzhen Longcheng hospital from January 2017 to June 2021 were recruited for model construction, internal validation and external validation. The first time point of data collection was within half a month of patients first diagnosed with NLUTD. The second time point was at the 6-month follow-up. The early warning model was constructed by logistic regression. The model prediction effects were validated using the area under the Receiver Operating Characteristic curve, the Boostrap experiment and the calibration plot of the combined data. The model was externally validated using sensitivity, specificity and accuracy. RESULTS: Six predictors were identified in the model, namely patients ≥65 years old (OR = 2.478, 95%CI [1.215– 5.050]), female (OR = 2.552, 95%CI [1.286–5.065]), diabetes (OR = 2.364, 95%CI) [1.182–4.731]), combined with urinary calculi (OR = 2.948, 95%CI [1.387–6.265]), indwelling catheterization (OR = 1.988, 95%CI [1.003 –3.940]) and bladder behavior training intervention time ≥2 weeks (OR = 2.489, 95%CI [1.233–5.022]); and the early warning model formula was Y = 0.907 ×  age+ 0.937 × sex + 0.860 × diabetes +1.081 × combined with urinary calculi+ 0.687 × indwelling catheterization+ 0.912 × bladder behavior training intervention time-2.570. The results show that the area under the ROC curve is 0.832, which is close to that of 1,000 Bootstrap internal validation (0.828). The calibration plot shows that the early warning model has good discrimination ability and consistency. The external validation shows the sensitivity is 62.5%, the specificity is 100%, and the accuracy is 90%. CONCLUSION: The early warning model for urinary tract infection in patients with NLUTD is suitable for clinical practice, which can provide targeted guidance for the evaluation of urinary tract infection in patients with NLUTD. PeerJ Inc. 2022-05-05 /pmc/articles/PMC9080428/ /pubmed/35539015 http://dx.doi.org/10.7717/peerj.13388 Text en ©2022 Zhou et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Epidemiology
Zhou, Liqiong
Liang, Surui
Shuai, Qin
Fan, Chunhua
Gao, Linghong
Cai, Wenzhi
Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study
title Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study
title_full Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study
title_fullStr Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study
title_full_unstemmed Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study
title_short Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study
title_sort early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (nlutd): a retrospective study
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9080428/
https://www.ncbi.nlm.nih.gov/pubmed/35539015
http://dx.doi.org/10.7717/peerj.13388
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