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Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression

BACKGROUND: Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which acco...

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Autores principales: Schell, Greggory J, Lavieri, Mariel S, Stein, Joshua D, Musch, David C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878032/
https://www.ncbi.nlm.nih.gov/pubmed/24359562
http://dx.doi.org/10.1186/1472-6947-13-137
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author Schell, Greggory J
Lavieri, Mariel S
Stein, Joshua D
Musch, David C
author_facet Schell, Greggory J
Lavieri, Mariel S
Stein, Joshua D
Musch, David C
author_sort Schell, Greggory J
collection PubMed
description BACKGROUND: Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. METHODS: Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. RESULTS: The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. CONCLUSION: A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.
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spelling pubmed-38780322014-01-07 Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression Schell, Greggory J Lavieri, Mariel S Stein, Joshua D Musch, David C BMC Med Inform Decis Mak Research Article BACKGROUND: Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. METHODS: Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. RESULTS: The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. CONCLUSION: A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases. BioMed Central 2013-12-21 /pmc/articles/PMC3878032/ /pubmed/24359562 http://dx.doi.org/10.1186/1472-6947-13-137 Text en Copyright © 2013 Schell et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Schell, Greggory J
Lavieri, Mariel S
Stein, Joshua D
Musch, David C
Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
title Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
title_full Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
title_fullStr Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
title_full_unstemmed Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
title_short Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
title_sort filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878032/
https://www.ncbi.nlm.nih.gov/pubmed/24359562
http://dx.doi.org/10.1186/1472-6947-13-137
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