Personalized Prediction of Glaucoma Progression Under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods

Ophthalmology

Volumn 125, Issue 4, 2018, Pages 569-577

  Kazemian, Pooyan ^a,b   Lavieri, Mariel S ^c   Van Oyen, Mark P ^c   Andrews, Chris ^c,d   Stein, Joshua D ^c,d,e  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

^d UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

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

Author keywords

[No Author keywords available]

Indexed keywords

ANTIGLAUCOMA AGENT; ANTIHYPERTENSIVE AGENT;

ADULT; ARTICLE; CLINICAL OUTCOME; CONTROLLED STUDY; DECISION SUPPORT SYSTEM; DISEASE EXACERBATION; DISEASE SEVERITY; FEMALE; FILTERED FORECASTING; FOLLOW UP; FORECASTING; HUMAN; INTRAOCULAR PRESSURE; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; MULTICENTER STUDY; OCULOPLETHYSMOGRAPHY; OPEN ANGLE GLAUCOMA; PREDICTION; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL; SEX DIFFERENCE; TRABECULECTOMY; TRABECULOPLASTY; AGED; CLINICAL TRIAL; PATHOPHYSIOLOGY; PERIMETRY; PERSONALIZED MEDICINE; PHYSIOLOGY; PROCEDURES; VALIDATION STUDY; VISUAL FIELD;

AGED; ANTIHYPERTENSIVE AGENTS; DECISION SUPPORT TECHNIQUES; DISEASE PROGRESSION; FEMALE; FOLLOW-UP STUDIES; FORECASTING; GLAUCOMA, OPEN-ANGLE; HUMANS; INTRAOCULAR PRESSURE; MALE; MIDDLE AGED; PRECISION MEDICINE; TONOMETRY, OCULAR; TRABECULECTOMY; VISUAL FIELD TESTS; VISUAL FIELDS;

EID: 85035320643     PISSN: 01616420     EISSN: 15494713     Source Type: Journal    
DOI: 10.1016/j.ophtha.2017.10.033     Document Type: Article

References (24)

1. Lefferts, E.J., Markley, F.L., Shuster, M.D., Kalman filtering for spacecraft attitude estimation. J Guid Control Dyn 5 (1982), 417–429.
2. 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: an extended Kalman filter approach. J Diabetes Sci Technol 8 (2014), 331–345.
3. Eberle, C., Ament, C., The unscented Kalman filter estimates the plasma insulin from glucose measurement. Biosystems 103 (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 (2012), 62–77.
5. Musch, D.C., Lichter, P.R., Guire, K.E., et al. The Collaborative Initial Glaucoma Treatment Study: study design, methods, and baseline characteristics of enrolled patients. Ophthalmology 106 (1999), 653–662.
6. AGIS Investigators. The Advanced Glaucoma Intervention Study (AGIS): 1. Study design and methods and baseline characteristics of study patients. Control Clin Trials 15 (1994), 299–325.
7. Kalman, R.E., A new approach to linear filtering and prediction problems. J Fluids Eng 82 (1960), 35–45.
8. Cunningham, J.P., Nuyujukian, P., Gilja, V., et al. A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces. J Neurophysiol 105 (2011), 1932–1949.
9. Foussier, J., Teichmann, D., Jia, J., et al. An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors. BMC Med Inform Decis Mak, 14, 2014, 37.
10. Kuure-Kinsey, M., Palerm, C.C., Bequette, B.W., A dual-rate Kalman filter for continuous glucose monitoring. Conf Proc IEEE Eng Med Biol Soc 1 (2006), 63–66.
11. Bogaarts, J.G., Gommer, E.D., Hilkman, D.M., et al. EEG feature pre-processing for neonatal epileptic seizure detection. Ann Biomed Eng 42 (2014), 2360–2368.
12. Bukhari, W., Hong, S.M., Real-time prediction and gating of respiratory motion using an extended Kalman filter and Gaussian process regression. Phys Med Biol 60 (2014), 233–252.
13. Kazemian, P., Helm, J.E., Lavieri, M.S., et al. Dynamic monitoring and control of irreversible chronic diseases with application to glaucoma. Available at SSRN: http://ssrn.com/abstract=2733399, 2016 Accessed April 17, 2017.
14. Helm, J.E., Lavieri, M.S., Van Oyen, M.P., et al. Dynamic forecasting and control algorithms of glaucoma progression for clinician decision support. Oper Res 63 (2015), 979–999.
15. Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via Em algorithm. J R Stat Soc Series B Stat Methodol 39 (1977), 1–38.
16. Chauhan, B.C., Malik, R., Shuba, L.M., et al. Rates of glaucomatous visual field change in a large clinical population. Invest Ophthalmol Vis Sci 55 (2014), 4135–4143.
17. Heijl, A., Buchholz, P., Norrgren, G., Bengtsson, B., Rates of visual field progression in clinical glaucoma care. Acta Ophthalmologica 91 (2013), 406–412.
18. Gardiner, S.K., Crabb, D.P., Examination of different pointwise linear regression methods for determining visual field progression. Invest Ophthalmol Vis Sci 43 (2002), 1400–1407.
19. De Moraes, C.G., Jasien, J.V., Simon-Zoula, S., et al. Visual field change and 24-hour IOP-related profile with a contact lens sensor in treated glaucoma patients. Ophthalmology 123 (2016), 744–753.
20. Heijl, A., Lindgren, G., Olsson, J., Normal variability of static perimetric threshold values across the central visual field. Arch Ophthalmol 105 (1987), 1544–1549.
21. Azuara-Blanco, A., Burr, J., The rising cost of glaucoma drugs. Br J Ophthalmol 90 (2006), 130–131.
22. Stein, J.D., Ruiz, D. Jr., Belsky, D., et al. Longitudinal rates of postoperative adverse outcomes after glaucoma surgery among Medicare beneficiaries 1994 to 2005. Ophthalmology 115 (2008), 1109–1116.e7.
23. Skalicky, S., Goldberg, I., Depression and quality of life in patients with glaucoma: a cross-sectional analysis using the Geriatric Depression Scale-15, assessment of function related to vision, and the Glaucoma Quality of Life-15. J Glaucoma 17 (2008), 546–551.
24. Black, A.A., Wood, J.M., Lovie-Kitchin, J.E., Inferior field loss increases rate of falls in older adults with glaucoma. Optom Vis Sci 88 (2011), 1275–1282.