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Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm

BACKGROUND: Patients with colorectal cancer (CRC) are prone to stress ulcer after laparoscopic surgery. The analysis of risk factors for stress ulcer (SU) in patients with CRC is important to reduce mortality and improve patient prognosis. AIM: To identify risk factors for SU after laparoscopic surg...

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Autores principales: Yu, Dong-Mei, Wu, Chun-Xiao, Sun, Jun-Yi, Xue, Hui, Yuwen, Zhe, Feng, Jiang-Xue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600766/
https://www.ncbi.nlm.nih.gov/pubmed/37901722
http://dx.doi.org/10.4240/wjgs.v15.i9.1978
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author Yu, Dong-Mei
Wu, Chun-Xiao
Sun, Jun-Yi
Xue, Hui
Yuwen, Zhe
Feng, Jiang-Xue
author_facet Yu, Dong-Mei
Wu, Chun-Xiao
Sun, Jun-Yi
Xue, Hui
Yuwen, Zhe
Feng, Jiang-Xue
author_sort Yu, Dong-Mei
collection PubMed
description BACKGROUND: Patients with colorectal cancer (CRC) are prone to stress ulcer after laparoscopic surgery. The analysis of risk factors for stress ulcer (SU) in patients with CRC is important to reduce mortality and improve patient prognosis. AIM: To identify risk factors for SU after laparoscopic surgery for CRC, and develop a nomogram model to predict the risk of SU in these patients. METHODS The clinical data of 135 patients with CRC who underwent laparoscopic surgery between November 2021 and June 2022 were reviewed retrospectively. They were divided into two categories depending on the presence of SUs: The SU group (n = 23) and the non-SU group (n = 112). Univariate analysis and multivariate logistic regression analysis were used to screen for factors associated with postoperative SU in patients undergoing laparoscopic surgery, and a risk factor-based nomogram model was built based on these risk factors. By plotting the model's receiver operating characteristic (ROC) curve and calibration curve, a Hosmer-Lemeshow goodness of fit test was performed. RESULTS: Among the 135 patients with CRC, 23 patients had postoperative SU, with an incidence of 17.04%. The SU group had higher levels of heat shock protein (HSP) 70, HSP90, and gastrin (GAS) than the non-SU group. Age, lymph node metastasis, HSP70, HSP90, and GAS levels were statistically different between the two groups, but other indicators were not statistically different. Logistic regression analysis showed that age ≥ 65 years, lymph node metastasis, and increased levels of HSP70, HSP90 and GAS were all risk factors for postoperative SU in patients with CRC (P < 0.05). According to these five risk factors, the area under the ROC curve for the nomogram model was 0.988 (95%CI: 0.971-1.0); the calibration curve demonstrated excellent agreement between predicted and actual probabilities, and the Hosmer-Lemeshow goodness of fit test revealed that the difference was not statistically significant (χ(2) = 0.753, P = 0.999), suggesting that the nomogram model had good discrimination, calibration, and stability. CONCLUSION: Patients with CRC aged ≥ 65 years, with lymph node metastasis and elevated HSP70, HSP90, GAS levels, are prone to post-laparoscopic surgery SU. Our nomogram model shows good predictive value.
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spelling pubmed-106007662023-10-27 Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm Yu, Dong-Mei Wu, Chun-Xiao Sun, Jun-Yi Xue, Hui Yuwen, Zhe Feng, Jiang-Xue World J Gastrointest Surg Retrospective Study BACKGROUND: Patients with colorectal cancer (CRC) are prone to stress ulcer after laparoscopic surgery. The analysis of risk factors for stress ulcer (SU) in patients with CRC is important to reduce mortality and improve patient prognosis. AIM: To identify risk factors for SU after laparoscopic surgery for CRC, and develop a nomogram model to predict the risk of SU in these patients. METHODS The clinical data of 135 patients with CRC who underwent laparoscopic surgery between November 2021 and June 2022 were reviewed retrospectively. They were divided into two categories depending on the presence of SUs: The SU group (n = 23) and the non-SU group (n = 112). Univariate analysis and multivariate logistic regression analysis were used to screen for factors associated with postoperative SU in patients undergoing laparoscopic surgery, and a risk factor-based nomogram model was built based on these risk factors. By plotting the model's receiver operating characteristic (ROC) curve and calibration curve, a Hosmer-Lemeshow goodness of fit test was performed. RESULTS: Among the 135 patients with CRC, 23 patients had postoperative SU, with an incidence of 17.04%. The SU group had higher levels of heat shock protein (HSP) 70, HSP90, and gastrin (GAS) than the non-SU group. Age, lymph node metastasis, HSP70, HSP90, and GAS levels were statistically different between the two groups, but other indicators were not statistically different. Logistic regression analysis showed that age ≥ 65 years, lymph node metastasis, and increased levels of HSP70, HSP90 and GAS were all risk factors for postoperative SU in patients with CRC (P < 0.05). According to these five risk factors, the area under the ROC curve for the nomogram model was 0.988 (95%CI: 0.971-1.0); the calibration curve demonstrated excellent agreement between predicted and actual probabilities, and the Hosmer-Lemeshow goodness of fit test revealed that the difference was not statistically significant (χ(2) = 0.753, P = 0.999), suggesting that the nomogram model had good discrimination, calibration, and stability. CONCLUSION: Patients with CRC aged ≥ 65 years, with lymph node metastasis and elevated HSP70, HSP90, GAS levels, are prone to post-laparoscopic surgery SU. Our nomogram model shows good predictive value. Baishideng Publishing Group Inc 2023-09-27 2023-09-27 /pmc/articles/PMC10600766/ /pubmed/37901722 http://dx.doi.org/10.4240/wjgs.v15.i9.1978 Text en ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
spellingShingle Retrospective Study
Yu, Dong-Mei
Wu, Chun-Xiao
Sun, Jun-Yi
Xue, Hui
Yuwen, Zhe
Feng, Jiang-Xue
Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm
title Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm
title_full Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm
title_fullStr Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm
title_full_unstemmed Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm
title_short Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm
title_sort prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm
topic Retrospective Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600766/
https://www.ncbi.nlm.nih.gov/pubmed/37901722
http://dx.doi.org/10.4240/wjgs.v15.i9.1978
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