A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations.

Autor: Walczak S; School of Information & Florida Center for Cybersecurity, University of South Florida, Tampa, Florida, United States of America., Velanovich V; Department of Surgery, Morsani College of Medicine, University of South Florida, Tampa, Florida, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Apr 07; Vol. 18 (4), pp. e0284206. Date of Electronic Publication: 2023 Apr 07 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0284206
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Walczak, Velanovich. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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