Evidence map›Paper›PMID 36246178›Full record

ArticleOphthalmology science2022

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.

Mohammad Zhalechian, Mark P Van Oyen, Mariel S Lavieri, Carlos Gustavo De Moraes, Christopher A Girkin, Massimo A Fazio, Robert N Weinreb, Christopher Bowd, Jeffrey M Liebmann, Linda M Zangwill and 2 more

Abstract read
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In one paragraph

Article in Ophthalmology science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Mohammad ZhalechianDepartment of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
Mark P Van OyenDepartment of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
Mariel S LavieriDepartment of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
Carlos Gustavo De MoraesBernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, New York.
Christopher A GirkinDepartment of Ophthalmology, University of Alabama at Birmingham, Birmingham, Alabama.
Massimo A FazioDepartment of Ophthalmology, University of Alabama at Birmingham, Birmingham, Alabama.
Robert N WeinrebHamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
Christopher BowdHamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
Jeffrey M LiebmannBernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, New York.
Linda M ZangwillHamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
Christopher A AndrewsDepartment of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, Michigan.
Joshua D SteinDepartment of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, Michigan.

Funding

African Descent And Glaucoma Evaluation Study (ADAGES)U10EY014267 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZANGWILL, LINDA M · 2002 to 2010
$13.2M
Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LINDA M ZANGWILL · 2012 to 2026
$10.3M
Diagnostic Innovations in Glaucoma:Structural AssessmentR01EY011008 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZANGWILL, LINDA M · 1995 to 2015
$8.7M
Diagnostic Innovations in Glaucoma Study (DIGS): High Myopia and Advanced DiseaseR01EY027510 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LINDA M ZANGWILL · 2017 to 2026
$5.3M
Diagnosis and Monitoring of Glaucoma with Optical Coherence Tomography AngiographyR01EY029058 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WEINREB, ROBERT N · 2018 to 2024
$4.2M
Personalized Forecasting of Disease Trajectory for Patients with Open Angle GlaucomaR01EY026641 · NEI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LAVIERI-WILLIAMS, MARIEL SOFIA, STEIN, JOSHUA D · 2016 to 2020
$2.9M
African Descent and Glaucoma Evaluation (ADAGES) II: Glaucoma ProgressionR01EY019869 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZANGWILL, LINDA M · 2010 to 2014
$2.8M
African Descent and Glaucoma Evaluation (ADAGES) IV: Alterations of the lamina cribrosa in progressionR01EY026574 · NEI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FAZIO, MASSIMO ANTONIO, GIRKIN, CHRISTOPHER ANTHONY · 2017 to 2020
$2.6M
NEI NIH HHS P30 EY022589NEI NIH HHS R01 EY011008NEI NIH HHS R01 EY019869NEI NIH HHS R01 EY026574NEI NIH HHS R01 EY026641NEI NIH HHS R01 EY027510NEI NIH HHS R01 EY029058NEI NIH HHS U10 EY014267
6 · The paper itself

Abstract

Purpose: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indices on perimetry can be enhanced by adding global retinal nerve fiber layer (RNFL) data and whether model performance is influenced by the racial composition of the training and testing sets. Design: Retrospective, longitudinal cohort study. Participants: Patients with open-angle glaucoma (OAG) or glaucoma suspects enrolled in the African Descent and Glaucoma Evaluation Study or Diagnostic Innovation in Glaucoma Study. Methods: We developed a Kalman filter (KF) with tonometry and perimetry data (KF-TP) and another KF with tonometry, perimetry, and global RNFL data (KF-TPO), comparing these models with one another and with 2 linear regression (LR) models for predicting mean deviation (MD) and pattern standard deviation values 36 months into the future for patients with OAG and glaucoma suspects. We also compared KF model performance when trained on individuals of European and African descent and tested on patients of the same versus the other race. Main Outcome Measures: Predictive accuracy (percentage of MD values forecasted within the 95% repeatability interval) differences among the models. Results: Among 362 eligible patients, the mean ± standard deviation age at baseline was 71.3 ± 10.4 years; 196 patients (54.1%) were women; 202 patients (55.8%) were of European descent, and 139 (38.4%) were of African descent. Among patients with OAG (n = 296), the predictive accuracy for 36 months in the future was higher for the KF models (73.5% for KF-TP, 71.2% for KF-TPO) than for the LR models (57.5%, 58.0%). Predictive accuracy did not differ significantly between KF-TP and KF-TPO ( Conclusions: Adding global RNFL data to existing KFs minimally improved their predictive accuracy. Although KFs attained better predictive accuracy when the races of the training and testing sets were aligned, these improvements were modest. These findings will help to guide implementation of KFs in clinical practice.

Indexed as

AD, African descentADAGES, African Descent and Glaucoma Evaluation StudyAlgorithm biasCI, confidence intervalD, diopterDIGS, Diagnostic Innovation in Glaucoma StudyED, European descentGlaucomaIOP, intraocular pressureKalman filterKF, Kalman filterKF-TP, Kalman filter with tonometry and perimetry dataKF-TPO, Kalman filter with tonometry, perimetry, and global retinal nerve fiber layer dataLR1, linear regression model 1LR2, linear regression model 2Machine learningMAE, mean absolute errorMD, mean deviationOAG, open-angle glaucomaOCTPSD, pattern standard deviationRMSE, root mean square errorRNFL, retinal nerve fiber layerSD, standard deviationVF, visual field

Identifiers

PMID36246178
PMCPMC9560647

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.