Cargando…
An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery
Prediction of blood transfusion after adult spinal deformity (ASD) surgery can identify at-risk patients and potentially reduce its utilization and the complications associated with it. The use of artificial neural networks (ANNs) offers the potential for high predictive capability. A total of 1173...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369471/ https://www.ncbi.nlm.nih.gov/pubmed/35956053 http://dx.doi.org/10.3390/jcm11154436 |
_version_ | 1784766469113905152 |
---|---|
author | De la Garza Ramos, Rafael Hamad, Mousa K. Ryvlin, Jessica Krol, Oscar Passias, Peter G. Fourman, Mitchell S. Shin, John H. Yanamadala, Vijay Gelfand, Yaroslav Murthy, Saikiran Yassari, Reza |
author_facet | De la Garza Ramos, Rafael Hamad, Mousa K. Ryvlin, Jessica Krol, Oscar Passias, Peter G. Fourman, Mitchell S. Shin, John H. Yanamadala, Vijay Gelfand, Yaroslav Murthy, Saikiran Yassari, Reza |
author_sort | De la Garza Ramos, Rafael |
collection | PubMed |
description | Prediction of blood transfusion after adult spinal deformity (ASD) surgery can identify at-risk patients and potentially reduce its utilization and the complications associated with it. The use of artificial neural networks (ANNs) offers the potential for high predictive capability. A total of 1173 patients who underwent surgery for ASD were identified in the 2017–2019 NSQIP databases. The data were split into 70% training and 30% testing cohorts. Eighteen patient and operative variables were used. The outcome variable was receiving RBC transfusion intraoperatively or within 72 h after surgery. The model was assessed by its sensitivity, positive predictive value, F1-score, accuracy (ACC), and area under the curve (AUROC). Average patient age was 56 years and 63% were female. Pelvic fixation was performed in 21.3% of patients and three-column osteotomies in 19.5% of cases. The transfusion rate was 50.0% (586/1173 patients). The best model showed an overall ACC of 81% and 77% on the training and testing data, respectively. On the testing data, the sensitivity was 80%, the positive predictive value 76%, and the F1-score was 78%. The AUROC was 0.84. ANNs may allow the identification of at-risk patients, potentially decrease the risk of transfusion via strategic planning, and improve resource allocation. |
format | Online Article Text |
id | pubmed-9369471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93694712022-08-12 An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery De la Garza Ramos, Rafael Hamad, Mousa K. Ryvlin, Jessica Krol, Oscar Passias, Peter G. Fourman, Mitchell S. Shin, John H. Yanamadala, Vijay Gelfand, Yaroslav Murthy, Saikiran Yassari, Reza J Clin Med Article Prediction of blood transfusion after adult spinal deformity (ASD) surgery can identify at-risk patients and potentially reduce its utilization and the complications associated with it. The use of artificial neural networks (ANNs) offers the potential for high predictive capability. A total of 1173 patients who underwent surgery for ASD were identified in the 2017–2019 NSQIP databases. The data were split into 70% training and 30% testing cohorts. Eighteen patient and operative variables were used. The outcome variable was receiving RBC transfusion intraoperatively or within 72 h after surgery. The model was assessed by its sensitivity, positive predictive value, F1-score, accuracy (ACC), and area under the curve (AUROC). Average patient age was 56 years and 63% were female. Pelvic fixation was performed in 21.3% of patients and three-column osteotomies in 19.5% of cases. The transfusion rate was 50.0% (586/1173 patients). The best model showed an overall ACC of 81% and 77% on the training and testing data, respectively. On the testing data, the sensitivity was 80%, the positive predictive value 76%, and the F1-score was 78%. The AUROC was 0.84. ANNs may allow the identification of at-risk patients, potentially decrease the risk of transfusion via strategic planning, and improve resource allocation. MDPI 2022-07-29 /pmc/articles/PMC9369471/ /pubmed/35956053 http://dx.doi.org/10.3390/jcm11154436 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article De la Garza Ramos, Rafael Hamad, Mousa K. Ryvlin, Jessica Krol, Oscar Passias, Peter G. Fourman, Mitchell S. Shin, John H. Yanamadala, Vijay Gelfand, Yaroslav Murthy, Saikiran Yassari, Reza An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery |
title | An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery |
title_full | An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery |
title_fullStr | An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery |
title_full_unstemmed | An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery |
title_short | An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery |
title_sort | artificial neural network model for the prediction of perioperative blood transfusion in adult spinal deformity surgery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369471/ https://www.ncbi.nlm.nih.gov/pubmed/35956053 http://dx.doi.org/10.3390/jcm11154436 |
work_keys_str_mv | AT delagarzaramosrafael anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT hamadmousak anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT ryvlinjessica anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT kroloscar anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT passiaspeterg anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT fourmanmitchells anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT shinjohnh anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT yanamadalavijay anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT gelfandyaroslav anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT murthysaikiran anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT yassarireza anartificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT delagarzaramosrafael artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT hamadmousak artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT ryvlinjessica artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT kroloscar artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT passiaspeterg artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT fourmanmitchells artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT shinjohnh artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT yanamadalavijay artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT gelfandyaroslav artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT murthysaikiran artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery AT yassarireza artificialneuralnetworkmodelforthepredictionofperioperativebloodtransfusioninadultspinaldeformitysurgery |