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Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients

Hip fracture patients have a high risk of mortality after surgery, with 30-day postoperative rates as high as 10%. This study aimed to explore the predictive ability of preoperative characteristics in traumatic hip fracture patients as they relate to 30-day postoperative mortality using readily avai...

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Autores principales: Cao, Yang, Forssten, Maximilian Peter, Mohammad Ismail, Ahmad, Borg, Tomas, Ioannidis, Ioannis, Montgomery, Scott, Mohseni, Shahin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8146802/
https://www.ncbi.nlm.nih.gov/pubmed/33924993
http://dx.doi.org/10.3390/jpm11050353
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author Cao, Yang
Forssten, Maximilian Peter
Mohammad Ismail, Ahmad
Borg, Tomas
Ioannidis, Ioannis
Montgomery, Scott
Mohseni, Shahin
author_facet Cao, Yang
Forssten, Maximilian Peter
Mohammad Ismail, Ahmad
Borg, Tomas
Ioannidis, Ioannis
Montgomery, Scott
Mohseni, Shahin
author_sort Cao, Yang
collection PubMed
description Hip fracture patients have a high risk of mortality after surgery, with 30-day postoperative rates as high as 10%. This study aimed to explore the predictive ability of preoperative characteristics in traumatic hip fracture patients as they relate to 30-day postoperative mortality using readily available variables in clinical practice. All adult patients who underwent primary emergency hip fracture surgery in Sweden between 2008 and 2017 were included in the analysis. Associations between the possible predictors and 30-day mortality was performed using a multivariate logistic regression (LR) model; the bidirectional stepwise method was used for variable selection. An LR model and convolutional neural network (CNN) were then fitted for prediction. The relative importance of individual predictors was evaluated using the permutation importance and Gini importance. A total of 134,915 traumatic hip fracture patients were included in the study. The CNN and LR models displayed an acceptable predictive ability for predicting 30-day postoperative mortality using a test dataset, displaying an area under the ROC curve (AUC) of as high as 0.76. The variables with the highest importance in prediction were age, sex, hypertension, dementia, American Society of Anesthesiologists (ASA) classification, and the Revised Cardiac Risk Index (RCRI). Both the CNN and LR models achieved an acceptable performance in identifying patients at risk of mortality 30 days after hip fracture surgery. The most important variables for prediction, based on the variables used in the current study are age, hypertension, dementia, sex, ASA classification, and RCRI.
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spelling pubmed-81468022021-05-26 Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients Cao, Yang Forssten, Maximilian Peter Mohammad Ismail, Ahmad Borg, Tomas Ioannidis, Ioannis Montgomery, Scott Mohseni, Shahin J Pers Med Article Hip fracture patients have a high risk of mortality after surgery, with 30-day postoperative rates as high as 10%. This study aimed to explore the predictive ability of preoperative characteristics in traumatic hip fracture patients as they relate to 30-day postoperative mortality using readily available variables in clinical practice. All adult patients who underwent primary emergency hip fracture surgery in Sweden between 2008 and 2017 were included in the analysis. Associations between the possible predictors and 30-day mortality was performed using a multivariate logistic regression (LR) model; the bidirectional stepwise method was used for variable selection. An LR model and convolutional neural network (CNN) were then fitted for prediction. The relative importance of individual predictors was evaluated using the permutation importance and Gini importance. A total of 134,915 traumatic hip fracture patients were included in the study. The CNN and LR models displayed an acceptable predictive ability for predicting 30-day postoperative mortality using a test dataset, displaying an area under the ROC curve (AUC) of as high as 0.76. The variables with the highest importance in prediction were age, sex, hypertension, dementia, American Society of Anesthesiologists (ASA) classification, and the Revised Cardiac Risk Index (RCRI). Both the CNN and LR models achieved an acceptable performance in identifying patients at risk of mortality 30 days after hip fracture surgery. The most important variables for prediction, based on the variables used in the current study are age, hypertension, dementia, sex, ASA classification, and RCRI. MDPI 2021-04-28 /pmc/articles/PMC8146802/ /pubmed/33924993 http://dx.doi.org/10.3390/jpm11050353 Text en © 2021 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
Cao, Yang
Forssten, Maximilian Peter
Mohammad Ismail, Ahmad
Borg, Tomas
Ioannidis, Ioannis
Montgomery, Scott
Mohseni, Shahin
Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
title Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
title_full Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
title_fullStr Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
title_full_unstemmed Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
title_short Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
title_sort predictive values of preoperative characteristics for 30-day mortality in traumatic hip fracture patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8146802/
https://www.ncbi.nlm.nih.gov/pubmed/33924993
http://dx.doi.org/10.3390/jpm11050353
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