Cargando…

Severity Grading, Risk Factors, and Prediction Model of Complications After Epilepsy Surgery: A Large-Scale and Retrospective Study

Purpose: To report complications after epilepsy surgery, grade the severity of complications, investigate risk factors, and develop a nomogram for risk prediction of complications. Methods: Patients with epilepsy surgery performed by a single surgeon at a single center between October 1, 2003 and Ap...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Yong, Wu, Hao, Li, Huanfa, Dong, Shan, Liu, Xiaofang, Li, Kuo, Du, Changwang, Meng, Qiang, Zhang, Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543040/
https://www.ncbi.nlm.nih.gov/pubmed/34707556
http://dx.doi.org/10.3389/fneur.2021.722478
_version_ 1784589558092922880
author Liu, Yong
Wu, Hao
Li, Huanfa
Dong, Shan
Liu, Xiaofang
Li, Kuo
Du, Changwang
Meng, Qiang
Zhang, Hua
author_facet Liu, Yong
Wu, Hao
Li, Huanfa
Dong, Shan
Liu, Xiaofang
Li, Kuo
Du, Changwang
Meng, Qiang
Zhang, Hua
author_sort Liu, Yong
collection PubMed
description Purpose: To report complications after epilepsy surgery, grade the severity of complications, investigate risk factors, and develop a nomogram for risk prediction of complications. Methods: Patients with epilepsy surgery performed by a single surgeon at a single center between October 1, 2003 and April 30, 2019 were retrospectively analyzed. Study outcomes included severity grading of complications occurring during the 3-month period after surgery, risk factors, and a prediction model of these complications. Multivariable logistic regression analysis was used to calculate odds ratio and 95% confidence interval to identify risk factors. Results: In total, 2,026 surgical procedures were eligible. There were 380 patients with mild complications, 23 with moderate complications, and 82 with severe complications. Being male (odds ratio 1.29, 95% confidence interval 1.02–1.64), age at surgery (>40 years: 2.58, 1.55–4.31; ≤ 40: 2.25, 1.39–3.65; ≤ 30: 1.83, 1.18–2.84; ≤ 20: 1.71, 1.11–2.63), intracranial hemorrhage in infancy (2.28, 1.14–4.57), serial number of surgery ( ≤ 1,000: 1.41, 1.01–1.97; ≤ 1,500: 1.63, 1.18–2.25), type of surgical procedure (extratemporal resections: 2.04, 1.55–2.70; extratemporal plus temporal resections: 2.56, 1.80–3.65), surgery duration (>6 h: 1.94, 1.25–3.00; ≤ 6: 1.92, 1.39–2.65), and acute postoperative seizure (1.44, 1.06–1.97) were independent risk factors of complications. A nomogram including age at surgery, type of surgical procedure, and surgery duration was developed to predict the probability of complications. Conclusions: Although epilepsy surgery has a potential adverse effect on the patients, most complications are mild and severe complications are few. Risk factors should be considered during the perioperative period. Patients with the above risk factors should be closely monitored to identify and treat complications timely. The prediction model is very useful for surgeons to improve postoperative management.
format Online
Article
Text
id pubmed-8543040
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-85430402021-10-26 Severity Grading, Risk Factors, and Prediction Model of Complications After Epilepsy Surgery: A Large-Scale and Retrospective Study Liu, Yong Wu, Hao Li, Huanfa Dong, Shan Liu, Xiaofang Li, Kuo Du, Changwang Meng, Qiang Zhang, Hua Front Neurol Neurology Purpose: To report complications after epilepsy surgery, grade the severity of complications, investigate risk factors, and develop a nomogram for risk prediction of complications. Methods: Patients with epilepsy surgery performed by a single surgeon at a single center between October 1, 2003 and April 30, 2019 were retrospectively analyzed. Study outcomes included severity grading of complications occurring during the 3-month period after surgery, risk factors, and a prediction model of these complications. Multivariable logistic regression analysis was used to calculate odds ratio and 95% confidence interval to identify risk factors. Results: In total, 2,026 surgical procedures were eligible. There were 380 patients with mild complications, 23 with moderate complications, and 82 with severe complications. Being male (odds ratio 1.29, 95% confidence interval 1.02–1.64), age at surgery (>40 years: 2.58, 1.55–4.31; ≤ 40: 2.25, 1.39–3.65; ≤ 30: 1.83, 1.18–2.84; ≤ 20: 1.71, 1.11–2.63), intracranial hemorrhage in infancy (2.28, 1.14–4.57), serial number of surgery ( ≤ 1,000: 1.41, 1.01–1.97; ≤ 1,500: 1.63, 1.18–2.25), type of surgical procedure (extratemporal resections: 2.04, 1.55–2.70; extratemporal plus temporal resections: 2.56, 1.80–3.65), surgery duration (>6 h: 1.94, 1.25–3.00; ≤ 6: 1.92, 1.39–2.65), and acute postoperative seizure (1.44, 1.06–1.97) were independent risk factors of complications. A nomogram including age at surgery, type of surgical procedure, and surgery duration was developed to predict the probability of complications. Conclusions: Although epilepsy surgery has a potential adverse effect on the patients, most complications are mild and severe complications are few. Risk factors should be considered during the perioperative period. Patients with the above risk factors should be closely monitored to identify and treat complications timely. The prediction model is very useful for surgeons to improve postoperative management. Frontiers Media S.A. 2021-10-07 /pmc/articles/PMC8543040/ /pubmed/34707556 http://dx.doi.org/10.3389/fneur.2021.722478 Text en Copyright © 2021 Liu, Wu, Li, Dong, Liu, Li, Du, Meng and Zhang. 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 Neurology
Liu, Yong
Wu, Hao
Li, Huanfa
Dong, Shan
Liu, Xiaofang
Li, Kuo
Du, Changwang
Meng, Qiang
Zhang, Hua
Severity Grading, Risk Factors, and Prediction Model of Complications After Epilepsy Surgery: A Large-Scale and Retrospective Study
title Severity Grading, Risk Factors, and Prediction Model of Complications After Epilepsy Surgery: A Large-Scale and Retrospective Study
title_full Severity Grading, Risk Factors, and Prediction Model of Complications After Epilepsy Surgery: A Large-Scale and Retrospective Study
title_fullStr Severity Grading, Risk Factors, and Prediction Model of Complications After Epilepsy Surgery: A Large-Scale and Retrospective Study
title_full_unstemmed Severity Grading, Risk Factors, and Prediction Model of Complications After Epilepsy Surgery: A Large-Scale and Retrospective Study
title_short Severity Grading, Risk Factors, and Prediction Model of Complications After Epilepsy Surgery: A Large-Scale and Retrospective Study
title_sort severity grading, risk factors, and prediction model of complications after epilepsy surgery: a large-scale and retrospective study
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543040/
https://www.ncbi.nlm.nih.gov/pubmed/34707556
http://dx.doi.org/10.3389/fneur.2021.722478
work_keys_str_mv AT liuyong severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy
AT wuhao severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy
AT lihuanfa severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy
AT dongshan severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy
AT liuxiaofang severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy
AT likuo severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy
AT duchangwang severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy
AT mengqiang severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy
AT zhanghua severitygradingriskfactorsandpredictionmodelofcomplicationsafterepilepsysurgeryalargescaleandretrospectivestudy