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A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations
BACKGROUND: Frailty is frequently used by clinicians to help determine surgical outcomes. The frailty index, which represents the frequency of frailty indicators present in an individual, is one method for evaluating patient frailty to predict surgical outcomes. However, the frailty index treats all...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10081744/ https://www.ncbi.nlm.nih.gov/pubmed/37027382 http://dx.doi.org/10.1371/journal.pone.0284206 |
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author | Walczak, Steven Velanovich, Vic |
author_facet | Walczak, Steven Velanovich, Vic |
author_sort | Walczak, Steven |
collection | PubMed |
description | BACKGROUND: Frailty is frequently used by clinicians to help determine surgical outcomes. The frailty index, which represents the frequency of frailty indicators present in an individual, is one method for evaluating patient frailty to predict surgical outcomes. However, the frailty index treats all indicators of frailty that are used in the index as equivalent. Our hypothesis is that frailty indicators may be divided into groups of high and low-impact indicators and this separation will improve surgical discharge outcome prediction accuracy. DATA AND METHODS: Population data for inpatient elective operations was collected from the 2018 American College of Surgeons National Surgical Quality Improvement Program Participant Use Files. Artificial neural network (ANN) models trained using backpropagation are used to evaluate the relative accuracy for predicting surgical outcome of discharge destination using a traditional modified frailty index (mFI) or a new joint mFI that separates high-impact and low-impact indicators into distinct groups as input variables. Predictions are made across nine possible discharge destinations. A leave-one-out method is used to indicate the relative contribution of high and low-impact variables. RESULTS: Except for the surgical specialty of cardiac surgery, the ANN model using distinct high and low-impact mFI indexes uniformly outperformed the ANN models using a single traditional mFI. Prediction accuracy improved from 3.4% to 28.1%. The leave-one-out experiment shows that except for the case of otolaryngology operations, the high-impact index indicators provided more support when determining surgical discharge destination outcomes. CONCLUSION: Frailty indicators are not uniformly similar and should be treated differently in clinical outcome prediction systems. |
format | Online Article Text |
id | pubmed-10081744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100817442023-04-08 A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations Walczak, Steven Velanovich, Vic PLoS One Research Article BACKGROUND: Frailty is frequently used by clinicians to help determine surgical outcomes. The frailty index, which represents the frequency of frailty indicators present in an individual, is one method for evaluating patient frailty to predict surgical outcomes. However, the frailty index treats all indicators of frailty that are used in the index as equivalent. Our hypothesis is that frailty indicators may be divided into groups of high and low-impact indicators and this separation will improve surgical discharge outcome prediction accuracy. DATA AND METHODS: Population data for inpatient elective operations was collected from the 2018 American College of Surgeons National Surgical Quality Improvement Program Participant Use Files. Artificial neural network (ANN) models trained using backpropagation are used to evaluate the relative accuracy for predicting surgical outcome of discharge destination using a traditional modified frailty index (mFI) or a new joint mFI that separates high-impact and low-impact indicators into distinct groups as input variables. Predictions are made across nine possible discharge destinations. A leave-one-out method is used to indicate the relative contribution of high and low-impact variables. RESULTS: Except for the surgical specialty of cardiac surgery, the ANN model using distinct high and low-impact mFI indexes uniformly outperformed the ANN models using a single traditional mFI. Prediction accuracy improved from 3.4% to 28.1%. The leave-one-out experiment shows that except for the case of otolaryngology operations, the high-impact index indicators provided more support when determining surgical discharge destination outcomes. CONCLUSION: Frailty indicators are not uniformly similar and should be treated differently in clinical outcome prediction systems. Public Library of Science 2023-04-07 /pmc/articles/PMC10081744/ /pubmed/37027382 http://dx.doi.org/10.1371/journal.pone.0284206 Text en © 2023 Walczak, Velanovich https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Walczak, Steven Velanovich, Vic A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations |
title | A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations |
title_full | A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations |
title_fullStr | A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations |
title_full_unstemmed | A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations |
title_short | A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations |
title_sort | neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10081744/ https://www.ncbi.nlm.nih.gov/pubmed/37027382 http://dx.doi.org/10.1371/journal.pone.0284206 |
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