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Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters
OBJECTIVE: We investigated the predictive value of a logistic model utilizing hematological parameters in diagnosing occupational lead poisoning. METHODS: This retrospective study (September 2020–December 2022) included patients with occupational lead poisoning. Differences in hematological paramete...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666822/ https://www.ncbi.nlm.nih.gov/pubmed/37994031 http://dx.doi.org/10.1177/03000605231213221 |
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author | Sun, Guokang Xiang, Pinpin Chen, Yiping Li, Zheng Wu, Bo Rao, Yanping Zhu, Zheng |
author_facet | Sun, Guokang Xiang, Pinpin Chen, Yiping Li, Zheng Wu, Bo Rao, Yanping Zhu, Zheng |
author_sort | Sun, Guokang |
collection | PubMed |
description | OBJECTIVE: We investigated the predictive value of a logistic model utilizing hematological parameters in diagnosing occupational lead poisoning. METHODS: This retrospective study (September 2020–December 2022) included patients with occupational lead poisoning. Differences in hematological parameters were compared between individuals with occupational blood lead poisoning and healthy individuals. We used logistic regression analysis to develop a diagnostic prediction model for occupational blood lead poisoning. Receiver operating characteristic (ROC) curves and corresponding area under the ROC curve values were used to assess the diagnostic value of hematological parameters and logistic models. RESULTS: Compared with controls, several indicators were significantly higher in the group with blood lead poisoning, but others were significantly lower. Logistic regression analysis showed that the red blood cell distribution width coefficient of variation (RDW-CV), neutrophil/lymphocyte ratio (NLR), and percentage of small red blood cells (Micro%) were independent factors in diagnosing occupational blood lead poisoning. The logistic regression model constructed based on these three parameters had sensitivity 78.7% and specificity 83.8% for diagnosing occupational lead poisoning. CONCLUSION: We identified RDW-CV, NLR, and Micro% as independent predictors in the diagnosis of occupational lead poisoning. A logistic regression model that includes these may contribute to better detection of occupational lead poisoning. |
format | Online Article Text |
id | pubmed-10666822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-106668222023-11-22 Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters Sun, Guokang Xiang, Pinpin Chen, Yiping Li, Zheng Wu, Bo Rao, Yanping Zhu, Zheng J Int Med Res Observational Study OBJECTIVE: We investigated the predictive value of a logistic model utilizing hematological parameters in diagnosing occupational lead poisoning. METHODS: This retrospective study (September 2020–December 2022) included patients with occupational lead poisoning. Differences in hematological parameters were compared between individuals with occupational blood lead poisoning and healthy individuals. We used logistic regression analysis to develop a diagnostic prediction model for occupational blood lead poisoning. Receiver operating characteristic (ROC) curves and corresponding area under the ROC curve values were used to assess the diagnostic value of hematological parameters and logistic models. RESULTS: Compared with controls, several indicators were significantly higher in the group with blood lead poisoning, but others were significantly lower. Logistic regression analysis showed that the red blood cell distribution width coefficient of variation (RDW-CV), neutrophil/lymphocyte ratio (NLR), and percentage of small red blood cells (Micro%) were independent factors in diagnosing occupational blood lead poisoning. The logistic regression model constructed based on these three parameters had sensitivity 78.7% and specificity 83.8% for diagnosing occupational lead poisoning. CONCLUSION: We identified RDW-CV, NLR, and Micro% as independent predictors in the diagnosis of occupational lead poisoning. A logistic regression model that includes these may contribute to better detection of occupational lead poisoning. SAGE Publications 2023-11-22 /pmc/articles/PMC10666822/ /pubmed/37994031 http://dx.doi.org/10.1177/03000605231213221 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Observational Study Sun, Guokang Xiang, Pinpin Chen, Yiping Li, Zheng Wu, Bo Rao, Yanping Zhu, Zheng Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters |
title | Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters |
title_full | Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters |
title_fullStr | Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters |
title_full_unstemmed | Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters |
title_short | Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters |
title_sort | diagnostic value of a logistic model of occupational lead poisoning using hematological parameters |
topic | Observational Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666822/ https://www.ncbi.nlm.nih.gov/pubmed/37994031 http://dx.doi.org/10.1177/03000605231213221 |
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