Using Kalman Filtering to Forecast Disease Trajectory for Patients With Normal Tension Glaucoma

American Journal of Ophthalmology

Volumn 199, Issue , 2019, Pages 111-119

  Garcia, Gian Gabriel P ^a   Nitta, Koji ^e,f   Lavieri, Mariel S ^a   Andrews, Chris ^b,c   Liu, Xiang ^a   Lobaza, Elizabeth ^a   Van Oyen, Mark P ^a   Sugiyama, Kazuhisa ^f   Stein, Joshua D ^b,c,d  

^a University of Michigan   (United States)

^b UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

^d UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

^e FUKUI KEN SAISEIKAI HOSPITAL   (Japan)

^f KANAZAWA UNIVERSITY GRADUATE SCHOOL OF MEDICAL SCIENCE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; CLASSIFICATION ALGORITHM; DISEASE CLASSIFICATION; DISEASE EXACERBATION; FEMALE; FORECASTING; GLAUCOMA; HIGH TENSION GLAUCOMA; HUMAN; ILLNESS TRAJECTORY; INTRAOCULAR PRESSURE; JAPAN; LOW TENSION GLAUCOMA; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; PREDICTION; PREDICTIVE VALIDITY; PRIORITY JOURNAL; RETROSPECTIVE STUDY; RISK ASSESSMENT; SECONDARY ANALYSIS; AGED; ALGORITHM; ASIAN CONTINENTAL ANCESTRY GROUP; CLINICAL TRIAL; EPIDEMIOLOGY; ETHNOLOGY; MULTICENTER STUDY; PATHOPHYSIOLOGY; PERIMETRY; PHYSIOLOGY; VISUAL FIELD;

AGED; ALGORITHMS; ASIAN CONTINENTAL ANCESTRY GROUP; FEMALE; FORECASTING; HUMANS; INTRAOCULAR PRESSURE; JAPAN; LOW TENSION GLAUCOMA; MACHINE LEARNING; MALE; MIDDLE AGED; RETROSPECTIVE STUDIES; VISUAL FIELD TESTS; VISUAL FIELDS;

EID: 85059247479     PISSN: 00029394     EISSN: 18791891     Source Type: Journal    
DOI: 10.1016/j.ajo.2018.10.012     Document Type: Article

References (32)

1. Stein, J.D., Kim, D.S., Niziol, L.M., et al. Differences in rates of glaucoma among Asian Americans and other racial groups, and among various Asian ethnic groups. Ophthalmology 118:6 (2011), 1031–1037.
2. Lefferts, E.J., Markley, F.L., Shuster, M.D., Kalman filtering for spacecraft attitude estimation. J Guid Control Dyn 5:5 (1982), 417–429.
3. Eberle, C., Ament, C., The unscented Kalman filter estimates the plasma insulin from glucose measurement. Biosystems 103:1 (2011), 67–72.
4. Lavieri, M.S., Puterman, M.L., Tyldesley, S., Morris, W.J., When to treat prostate cancer patients based on their PSA dynamics. IIE Trans Healthc Syst Eng 2:1 (2012), 62–77.
5. Wang, Q., Molenaar, P., Harsh, S., et al. Personalized state-space modeling of glucose dynamics for type 1 diabetes using continuously monitored glucose, insulin dose, and meal Intake. J Diabetes Sci Technol 8:2 (2014), 331–345.
6. Catlin, D.E., The Discrete Kalman Filter. Estimation, Control, and the Discrete Kalman Filter, 71st ed., 2012, Springer Science & Business Media, New York, NY, 133–163.
7. Schell, G.J., Lavieri, M.S., Helm, J.E., et al. Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma. Ophthalmology 121:8 (2014), 1539–1546.
8. The AGIS Investigators. The Advanced Glaucoma Intervention Study (AGIS): 1. Study design and methods and baseline characteristics of study patients. Control Clin Trials 15:4 (1994), 299–325.
9. Musch, D.C., Lichter, P.R., Guire, K.E., Standardi, C.L., the CIGTS Study Group. The Collaborative Initial Glaucoma Treatment Study: study design, methods, and baseline characteristics of enrolled patients. Ophthalmology 106:4 (1999), 653–662.
10. Musch, D.C., Gillespie, B.W., Lichter, P.R., Niziol, L.M., Janz, N.K., Visual field progression in the Collaborative Initial Glaucoma Treatment Study. The impact of treatment and other baseline factors. Ophthalmology 116:2 (2009), 200–207.e1.
11. The Japan Glaucoma Society Guidelines for Glaucoma, 3rd ed [in Japanese]. Nihon Ganka Gakkai Zasshi 116:1 (2012), 3–46.
12. European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th ed - Chapter 2: Classification and terminology. Supported by the EGS Foundation: Part 1: Foreword; Introduction; Glossary; Chapter 2 Classification and Terminology. Br J Ophthalmol 101 (2017), 73–127.
13. Hodapp, E., Parrish, R.K., Anderson, D.R., Clinical Decisions in Glaucoma. 1993, Mosby Inc, London, UK.
14. Gardiner, S.K., Crabb, D.P., Examination of different pointwise linear regression methods for determining visual field progression. Invest Ophthalmol Vis Sci 43:5 (2002), 1400–1407.
15. Bengtsson, B., Patella, V.M., Heijl, A., Prediction of glaucomatous visual field loss by extrapolation of linear trends. Arch Ophthalmol 127:12 (2009), 1610–1614.
16. Heijl, A., Bengtsson, B., Hyman, L., Leske, M.C., Natural history of open-angle glaucoma. Ophthalmology 116:12 (2009), 2271–2276.
17. Anderson, D.R., Drance, S.M., Schulzer, M., Collaborative Normal-Tension Glaucoma Study Group. Natural history of normal-tension glaucoma. Ophthalmology 108:2 (2001), 247–253.
18. Nitta, K., Wajima, R., Tachibana, G., et al. Prediction of visual field progression in patients with primary open-angle glaucoma, mainly including normal tension glaucoma. Sci Rep, 7(1), 2017, 15048.
19. Sung, M.S., Kang, Y.S., Heo, H., Park, S.W., Optic disc rotation as a clue for predicting visual field progression in myopic normal-tension glaucoma. Ophthalmology 123:7 (2016), 1484–1493.
20. Lee, J., Kong, M., Kim, J., Kee, C., Comparison of visual field progression between relatively low and high intraocular pressure groups in normal tension glaucoma patients. J Glaucoma 23:8 (2014), 553–560.
21. Ahrlich, K.G., De Moraes, C.G., Teng, C.C., et al. Visual field progression differences between normal-tension and exfoliative high-tension glaucoma. Invest Ophthalmol Vis Sci 51:3 (2010), 1458–1463.
22. Drance, S., Anderson, D.R., Schulzer, M., Risk factors for progression of visual field abnormalities in normal-tension glaucoma. Am J Ophthalmol 131:6 (2001), 699–708.
23. McNaught, A.I., Hitchings, R.A., Crabb, D.P., Fitzke, F.W., Modelling series of visual fields to detect progression in normal-tension glaucoma. Graefes Arch Clin Exp Ophthalmol 233:12 (1995), 750–755.
24. Krajewski, L., Ritzman, L., Malhotra, M., Forecasting demand. Operations Management: Processes and Supply Chains, 10th ed., 2013, Pearson, Boston, 490–500.
25. Hastie, T., Tibshirani, R., Friedman, J., Model assessment and selection. The Elements of Statistical Learning, 2nd ed., 2009, Springer, New York, NY, 241–249.
26. Bhapkar, V.P., A note on the equivalence of two test criteria for hypotheses in categorical data. J Am Stat Assoc 61:313 (1966), 228–235.
27. Thonginnetra, O., Greenstein, V.C., Chu, D., Liebmann, J.M., Ritch, R., Hood, D.C., Normal versus high tension glaucoma. J Glaucoma 19:3 (2010), 151–157.
28. Sommer, A., Ocular hypertension and normal-tension glaucoma. Arch Ophthalmol, 129(6), 2011, 785.
29. Shields, M.B., Normal-tension glaucoma: is it different from primary open-angle glaucoma?. Curr Opin Ophthalmol 19:2 (2008), 85–88.
30. Shields, M.B., Spaeth, G.L., The glaucomatous process and the evolving definition of glaucoma. J Glaucoma 21:3 (2012), 141–143.
31. Mroczkowska, S., Benavente-Perez, A., Negi, A., Sung, V., Patel, S.R., Gherghel, D., Primary open-angle glaucoma vs normal-tension glaucoma. JAMA Ophthalmol, 131(1), 2013, 36.
32. Kazemian, P., Lavieri, M.S., Van Oyen, M.P., Andrews, C., Stein, J.D., Personalized prediction of glaucoma progression under different target intraocular pressure levels using filtered forecasting methods. Ophthalmology 125:4 (2017), 569–577.