Cargando…

Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis

PURPOSE: Intraoperative blood transfusion is associated with adverse events. We aimed to establish a machine learning model to predict the probability of intraoperative blood transfusion during intracranial aneurysm surgery. METHODS: Patients, who underwent intracranial aneurysm surgery in our hospi...

Descripción completa

Detalles Bibliográficos
Autores principales: Xiao, Shugen, Liu, Fan, Yu, Liyuan, Li, Xiaopei, Ye, Xihong, Gong, Xingrui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114399/
https://www.ncbi.nlm.nih.gov/pubmed/37076865
http://dx.doi.org/10.1186/s12911-023-02157-9
_version_ 1785028008115961856
author Xiao, Shugen
Liu, Fan
Yu, Liyuan
Li, Xiaopei
Ye, Xihong
Gong, Xingrui
author_facet Xiao, Shugen
Liu, Fan
Yu, Liyuan
Li, Xiaopei
Ye, Xihong
Gong, Xingrui
author_sort Xiao, Shugen
collection PubMed
description PURPOSE: Intraoperative blood transfusion is associated with adverse events. We aimed to establish a machine learning model to predict the probability of intraoperative blood transfusion during intracranial aneurysm surgery. METHODS: Patients, who underwent intracranial aneurysm surgery in our hospital between January 2019 and December 2021 were enrolled. Four machine learning models were benchmarked and the best learning model was used to establish the nomogram, before conducting a discriminative assessment. RESULTS: A total of 375 patients were included for analysis in this model, among whom 108 received an intraoperative blood transfusion during the intracranial aneurysm surgery. The least absolute shrinkage selection operator identified six preoperative relative factors: hemoglobin, platelet, D-dimer, sex, white blood cell, and aneurysm rupture before surgery. Performance evaluation of the classification error demonstrated the following: K-nearest neighbor, 0.2903; logistic regression, 0.2290; ranger, 0.2518; and extremely gradient boosting model, 0.2632. A nomogram based on a logistic regression algorithm was established using the above six parameters. The AUC values of the nomogram were 0.828 (0.775, 0.881) and 0.796 (0.710, 0.882) in the development and validation groups, respectively. CONCLUSIONS: Machine learning algorithms present a good performance evaluation of intraoperative blood transfusion. The nomogram established using a logistic regression algorithm showed a good discriminative ability to predict intraoperative blood transfusion during aneurysm surgery.
format Online
Article
Text
id pubmed-10114399
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-101143992023-04-20 Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis Xiao, Shugen Liu, Fan Yu, Liyuan Li, Xiaopei Ye, Xihong Gong, Xingrui BMC Med Inform Decis Mak Research PURPOSE: Intraoperative blood transfusion is associated with adverse events. We aimed to establish a machine learning model to predict the probability of intraoperative blood transfusion during intracranial aneurysm surgery. METHODS: Patients, who underwent intracranial aneurysm surgery in our hospital between January 2019 and December 2021 were enrolled. Four machine learning models were benchmarked and the best learning model was used to establish the nomogram, before conducting a discriminative assessment. RESULTS: A total of 375 patients were included for analysis in this model, among whom 108 received an intraoperative blood transfusion during the intracranial aneurysm surgery. The least absolute shrinkage selection operator identified six preoperative relative factors: hemoglobin, platelet, D-dimer, sex, white blood cell, and aneurysm rupture before surgery. Performance evaluation of the classification error demonstrated the following: K-nearest neighbor, 0.2903; logistic regression, 0.2290; ranger, 0.2518; and extremely gradient boosting model, 0.2632. A nomogram based on a logistic regression algorithm was established using the above six parameters. The AUC values of the nomogram were 0.828 (0.775, 0.881) and 0.796 (0.710, 0.882) in the development and validation groups, respectively. CONCLUSIONS: Machine learning algorithms present a good performance evaluation of intraoperative blood transfusion. The nomogram established using a logistic regression algorithm showed a good discriminative ability to predict intraoperative blood transfusion during aneurysm surgery. BioMed Central 2023-04-19 /pmc/articles/PMC10114399/ /pubmed/37076865 http://dx.doi.org/10.1186/s12911-023-02157-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Xiao, Shugen
Liu, Fan
Yu, Liyuan
Li, Xiaopei
Ye, Xihong
Gong, Xingrui
Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis
title Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis
title_full Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis
title_fullStr Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis
title_full_unstemmed Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis
title_short Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis
title_sort development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114399/
https://www.ncbi.nlm.nih.gov/pubmed/37076865
http://dx.doi.org/10.1186/s12911-023-02157-9
work_keys_str_mv AT xiaoshugen developmentandvalidationofanomogramforbloodtransfusionduringintracranialaneurysmclampingsurgeryaretrospectiveanalysis
AT liufan developmentandvalidationofanomogramforbloodtransfusionduringintracranialaneurysmclampingsurgeryaretrospectiveanalysis
AT yuliyuan developmentandvalidationofanomogramforbloodtransfusionduringintracranialaneurysmclampingsurgeryaretrospectiveanalysis
AT lixiaopei developmentandvalidationofanomogramforbloodtransfusionduringintracranialaneurysmclampingsurgeryaretrospectiveanalysis
AT yexihong developmentandvalidationofanomogramforbloodtransfusionduringintracranialaneurysmclampingsurgeryaretrospectiveanalysis
AT gongxingrui developmentandvalidationofanomogramforbloodtransfusionduringintracranialaneurysmclampingsurgeryaretrospectiveanalysis