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Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis
OBJECTIVE: The purpose of this study was to establish a predictive model of postoperative fever in children with acute appendicitis through retrospective analysis, and the prediction ability of the model is demonstrated by model evaluation and external validation. METHODS: Medical records informatio...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445266/ https://www.ncbi.nlm.nih.gov/pubmed/36081635 http://dx.doi.org/10.3389/fped.2022.982614 |
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author | Chen, Yang Ren, Feng Xiao, Dong Guan, Ai-hui Zhu, Le-dao Ma, Xiao-peng Wang, Zhi-yong |
author_facet | Chen, Yang Ren, Feng Xiao, Dong Guan, Ai-hui Zhu, Le-dao Ma, Xiao-peng Wang, Zhi-yong |
author_sort | Chen, Yang |
collection | PubMed |
description | OBJECTIVE: The purpose of this study was to establish a predictive model of postoperative fever in children with acute appendicitis through retrospective analysis, and the prediction ability of the model is demonstrated by model evaluation and external validation. METHODS: Medical records information on children undergoing surgery for acute appendicitis within 2 years were retrospectively collected, prospective collection was performed for external validation in the next 3 months. The patients were divided into two groups according to whether the postoperative body temperature exceeded 38.5°C. Multivariate logistic regression analysis was used to determine independent risk factors and develop regression equations and nomogram. ROC curve, calibration curve and decision curve were made for model evaluation. Finally, the clinical implication of the prediction model was clarified by associating postoperative fever with prognosis. RESULTS: High risk factors of postoperative fever included in the prediction model were onset time (X1), preoperative temperature (X2), leukocyte count (X3), C-reactive protein (X4) and operation time (X5). The regression equation is logit (P) = 0.005X1+0.166X2+0.056X3+0.004X4+0.005X5-9.042. ROC curve showed that the area under the curve (AUC) of the training set was 0.660 (0.621, 0.699), and the AUC of the verification set was 0.712 (0.639, 0.784). The calibration curve suggested that the prediction probability was close to the actual probability. Decision curve analysis (DCA) showed that patients could benefit from clinician’s judgment. Furthermore, prognostic analysis showed children presenting with postoperative fever had the more duration of postoperative fever, hospitalization stays and cost, except for rehospitalization. CONCLUSION: All the results revealed that the model had good predictive ability. Pediatricians can calculate the probability of postoperative fever and make timely interventions to reduce pain for children and parents. |
format | Online Article Text |
id | pubmed-9445266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94452662022-09-07 Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis Chen, Yang Ren, Feng Xiao, Dong Guan, Ai-hui Zhu, Le-dao Ma, Xiao-peng Wang, Zhi-yong Front Pediatr Pediatrics OBJECTIVE: The purpose of this study was to establish a predictive model of postoperative fever in children with acute appendicitis through retrospective analysis, and the prediction ability of the model is demonstrated by model evaluation and external validation. METHODS: Medical records information on children undergoing surgery for acute appendicitis within 2 years were retrospectively collected, prospective collection was performed for external validation in the next 3 months. The patients were divided into two groups according to whether the postoperative body temperature exceeded 38.5°C. Multivariate logistic regression analysis was used to determine independent risk factors and develop regression equations and nomogram. ROC curve, calibration curve and decision curve were made for model evaluation. Finally, the clinical implication of the prediction model was clarified by associating postoperative fever with prognosis. RESULTS: High risk factors of postoperative fever included in the prediction model were onset time (X1), preoperative temperature (X2), leukocyte count (X3), C-reactive protein (X4) and operation time (X5). The regression equation is logit (P) = 0.005X1+0.166X2+0.056X3+0.004X4+0.005X5-9.042. ROC curve showed that the area under the curve (AUC) of the training set was 0.660 (0.621, 0.699), and the AUC of the verification set was 0.712 (0.639, 0.784). The calibration curve suggested that the prediction probability was close to the actual probability. Decision curve analysis (DCA) showed that patients could benefit from clinician’s judgment. Furthermore, prognostic analysis showed children presenting with postoperative fever had the more duration of postoperative fever, hospitalization stays and cost, except for rehospitalization. CONCLUSION: All the results revealed that the model had good predictive ability. Pediatricians can calculate the probability of postoperative fever and make timely interventions to reduce pain for children and parents. Frontiers Media S.A. 2022-08-23 /pmc/articles/PMC9445266/ /pubmed/36081635 http://dx.doi.org/10.3389/fped.2022.982614 Text en Copyright © 2022 Chen, Ren, Xiao, Guan, Zhu, Ma and Wang. 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 | Pediatrics Chen, Yang Ren, Feng Xiao, Dong Guan, Ai-hui Zhu, Le-dao Ma, Xiao-peng Wang, Zhi-yong Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis |
title | Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis |
title_full | Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis |
title_fullStr | Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis |
title_full_unstemmed | Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis |
title_short | Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis |
title_sort | prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis |
topic | Pediatrics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445266/ https://www.ncbi.nlm.nih.gov/pubmed/36081635 http://dx.doi.org/10.3389/fped.2022.982614 |
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