Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Walczak, Steven, Velanovich, Vic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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
_version_ 1785021180012396544
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
work_keys_str_mv AT walczaksteven aneuralnetworkanalysisoftheeffectofhighandlowfrailtyindexindicatorsonpredictingelectivesurgerydischargedestinations
AT velanovichvic aneuralnetworkanalysisoftheeffectofhighandlowfrailtyindexindicatorsonpredictingelectivesurgerydischargedestinations
AT walczaksteven neuralnetworkanalysisoftheeffectofhighandlowfrailtyindexindicatorsonpredictingelectivesurgerydischargedestinations
AT velanovichvic neuralnetworkanalysisoftheeffectofhighandlowfrailtyindexindicatorsonpredictingelectivesurgerydischargedestinations