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Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis

BACKGROUND: Severe acute pancreatitis (SAP) can progress to lung and kidney dysfunction, and blood clotting within 48 hours of its onset, and is associated with a high mortality rate. The aim of this study was to establish a reliable diagnostic prediction model for the early stage of severe pancreat...

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Autores principales: Li, Bo, Wu, Weiqing, Liu, Aijun, Feng, Lifeng, Li, Bin, Mei, Yong, Tan, Li, Zhang, Chaoyang, Tian, Yangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337691/
https://www.ncbi.nlm.nih.gov/pubmed/37449283
http://dx.doi.org/10.2147/JIR.S416411
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author Li, Bo
Wu, Weiqing
Liu, Aijun
Feng, Lifeng
Li, Bin
Mei, Yong
Tan, Li
Zhang, Chaoyang
Tian, Yangtao
author_facet Li, Bo
Wu, Weiqing
Liu, Aijun
Feng, Lifeng
Li, Bin
Mei, Yong
Tan, Li
Zhang, Chaoyang
Tian, Yangtao
author_sort Li, Bo
collection PubMed
description BACKGROUND: Severe acute pancreatitis (SAP) can progress to lung and kidney dysfunction, and blood clotting within 48 hours of its onset, and is associated with a high mortality rate. The aim of this study was to establish a reliable diagnostic prediction model for the early stage of severe pancreatitis. METHODS: The clinical data of patients diagnosed with acute pancreatitis from October 2017 to June 2022 at the Shangluo Central Hospital were collected. The risk factors were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. A novel nomogram model was then established by multivariable logistic regression analysis. RESULTS: The data of 436 patients with acute pancreatitis, 45 (10.3%) patients had progressed to SAP. Through univariate and LASSO regression analyses, the neutrophils (P <0.001), albumin (P < 0.001), blood glucose (P < 0.001), serum calcium (P < 0.001), serum creatinine (P < 0.001), blood urea nitrogen (P < 0.001) and procalcitonin (P = 0.005) were identified as independent predictive factors for SAP. The nomogram built on the basis of these factors predicted SAP with sensitivity of 0.733, specificity of 0.9, positive predictive value of 0.458 and negative predictive value of 0.967. Furthermore, the concordance index of the nomogram reached 0.889 (95% CI, 0.837–0.941), and the area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis was significantly higher than that of the APACHEII and ABISAP scoring systems. The established model was validated by plotting the clinical decision curve analysis (DCA) and clinical impact curve (CIC). CONCLUSION: We established a nomogram to predict the progression of early acute pancreatitis to SAP with high discrimination and accuracy.
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spelling pubmed-103376912023-07-13 Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis Li, Bo Wu, Weiqing Liu, Aijun Feng, Lifeng Li, Bin Mei, Yong Tan, Li Zhang, Chaoyang Tian, Yangtao J Inflamm Res Original Research BACKGROUND: Severe acute pancreatitis (SAP) can progress to lung and kidney dysfunction, and blood clotting within 48 hours of its onset, and is associated with a high mortality rate. The aim of this study was to establish a reliable diagnostic prediction model for the early stage of severe pancreatitis. METHODS: The clinical data of patients diagnosed with acute pancreatitis from October 2017 to June 2022 at the Shangluo Central Hospital were collected. The risk factors were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. A novel nomogram model was then established by multivariable logistic regression analysis. RESULTS: The data of 436 patients with acute pancreatitis, 45 (10.3%) patients had progressed to SAP. Through univariate and LASSO regression analyses, the neutrophils (P <0.001), albumin (P < 0.001), blood glucose (P < 0.001), serum calcium (P < 0.001), serum creatinine (P < 0.001), blood urea nitrogen (P < 0.001) and procalcitonin (P = 0.005) were identified as independent predictive factors for SAP. The nomogram built on the basis of these factors predicted SAP with sensitivity of 0.733, specificity of 0.9, positive predictive value of 0.458 and negative predictive value of 0.967. Furthermore, the concordance index of the nomogram reached 0.889 (95% CI, 0.837–0.941), and the area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis was significantly higher than that of the APACHEII and ABISAP scoring systems. The established model was validated by plotting the clinical decision curve analysis (DCA) and clinical impact curve (CIC). CONCLUSION: We established a nomogram to predict the progression of early acute pancreatitis to SAP with high discrimination and accuracy. Dove 2023-07-08 /pmc/articles/PMC10337691/ /pubmed/37449283 http://dx.doi.org/10.2147/JIR.S416411 Text en © 2023 Li et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Li, Bo
Wu, Weiqing
Liu, Aijun
Feng, Lifeng
Li, Bin
Mei, Yong
Tan, Li
Zhang, Chaoyang
Tian, Yangtao
Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis
title Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis
title_full Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis
title_fullStr Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis
title_full_unstemmed Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis
title_short Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis
title_sort establishment and validation of a nomogram prediction model for the severe acute pancreatitis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337691/
https://www.ncbi.nlm.nih.gov/pubmed/37449283
http://dx.doi.org/10.2147/JIR.S416411
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