BMC Med Inform Decis Mak - Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground openangl glaucoma oag preval degener ocular diseas can lead blind without proper clinic manag test use assess diseas progress suscept process measur nois aim studi develop methodolog account inher nois data improv signific diseas progress identificationmethod longitudin observ collabor initi glaucoma treatment studi cigt use parameter valid kalman filter model logist regress function kalman filter estim true valu biomark associ oag forecast futur valu variabl develop two logist regress model via general estim equat gee calcul probabl experienc signific oag progress one model base raw measur cigt anoth model base kalman filter estim cigt data receiv oper characterist roc curv associ area roc curv auc estim calcul use crossfold validationresult logist regress model develop use kalman filter estim data input achiev higher sensit specif model develop use raw measur mean auc kalman filterbas model mean auc raw measur model henc use probabl function generat via kalman filter estim gee logist regress abl accur classifi patient instanc experienc signific oag progressionconclus kalman filter approach estim true valu oag biomark result data input improv accuraci logist regress classif model compar model use raw measur input methodolog account process measur nois enabl improv discrimin progress nonprogress chronic diseas

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