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Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study

INTRODUCTION: Postoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SI...

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Autores principales: Li, Xiaoyue, Lu, Yaxin, Chen, Chaojin, Luo, Tongsen, Chen, Jingjing, Zhang, Qi, Zhou, Shaoli, Hei, Ziqing, Liu, Zifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150121/
https://www.ncbi.nlm.nih.gov/pubmed/37139371
http://dx.doi.org/10.3389/fpubh.2023.1145013
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author Li, Xiaoyue
Lu, Yaxin
Chen, Chaojin
Luo, Tongsen
Chen, Jingjing
Zhang, Qi
Zhou, Shaoli
Hei, Ziqing
Liu, Zifeng
author_facet Li, Xiaoyue
Lu, Yaxin
Chen, Chaojin
Luo, Tongsen
Chen, Jingjing
Zhang, Qi
Zhou, Shaoli
Hei, Ziqing
Liu, Zifeng
author_sort Li, Xiaoyue
collection PubMed
description INTRODUCTION: Postoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients. METHODS: Patients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort. RESULTS: A total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application. CONCLUSION: We developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients.
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spelling pubmed-101501212023-05-02 Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study Li, Xiaoyue Lu, Yaxin Chen, Chaojin Luo, Tongsen Chen, Jingjing Zhang, Qi Zhou, Shaoli Hei, Ziqing Liu, Zifeng Front Public Health Public Health INTRODUCTION: Postoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients. METHODS: Patients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort. RESULTS: A total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application. CONCLUSION: We developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients. Frontiers Media S.A. 2023-04-17 /pmc/articles/PMC10150121/ /pubmed/37139371 http://dx.doi.org/10.3389/fpubh.2023.1145013 Text en Copyright © 2023 Li, Lu, Chen, Luo, Chen, Zhang, Zhou, Hei and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Li, Xiaoyue
Lu, Yaxin
Chen, Chaojin
Luo, Tongsen
Chen, Jingjing
Zhang, Qi
Zhou, Shaoli
Hei, Ziqing
Liu, Zifeng
Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study
title Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study
title_full Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study
title_fullStr Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study
title_full_unstemmed Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study
title_short Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study
title_sort development and validation of a patient-specific model to predict postoperative sirs in older patients: a two-center study
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150121/
https://www.ncbi.nlm.nih.gov/pubmed/37139371
http://dx.doi.org/10.3389/fpubh.2023.1145013
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