Schell Greggory J, Lavieri Mariel S, Helm Jonathan E, Liu Xiang, Musch David C, Van Oyen Mark P, Stein Joshua D
Department of Industrial and Operations Engineering, University of Michigan School of Engineering, Ann Arbor, Michigan.
Department of Operations and Decision Technologies, Indiana University Kelley School of Business, Bloomington, Indiana.
Ophthalmology. 2014 Aug;121(8):1539-46. doi: 10.1016/j.ophtha.2014.02.021. Epub 2014 Apr 4.
To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG).
Secondary analyses using longitudinal data from 2 randomized controlled trials.
A total of 571 participants from the Advanced Glaucoma Intervention Study (AGIS) and the Collaborative Initial Glaucoma Treatment Study (CIGTS).
Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant's disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant's disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared with 1-, 1.5-, and 2-year fixed interval schedules of obtaining VF and IOP measurements.
Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, and number of VF and IOP measurements needed to assess for progression.
Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (P<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (P = 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well for patients with mild and advanced disease. The model performed significantly more testing of patients who exhibited OAG progression than nonprogressing patients (1.3 vs. 1.0 tests per year; P<0.0001).
Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management.
在评估开角型青光眼（OAG）患者时，确定与进行这些检查的固定间隔时间表相比，动态个性化的视野（VF）测试和眼压（IOP）测量时间表是否能改善疾病进展检测情况。
对两项随机对照试验的纵向数据进行二次分析。
来自高级青光眼干预研究（AGIS）和协作性初始青光眼治疗研究（CIGTS）的571名参与者。
获取AGIS和CIGTS试验参与者的视野和眼压数据，用于参数化和验证卡尔曼滤波模型。随着获得更多的VF测试和IOP测量结果，卡尔曼滤波会更新有关每个参与者疾病动态的知识。纳入最新的VF和IOP测量结果后，该模型预测每个参与者未来的疾病动态并表征预测误差。为了确定未来VF测试和IOP测量的个性化时间表，我们通过将用于状态估计的卡尔曼滤波与逻辑回归的预测能力相结合，开发了一种算法来识别OAG进展。将该算法与获取VF和IOP测量的1年、1.5年和2年固定间隔时间表进行比较。
检测OAG进展的诊断延迟时长、检测进展的效率以及评估进展所需的VF和IOP测量次数。
在AGIS和CIGTS试验中，参与者的平均（标准差）随访时间为6.5（2.8）年。我们的预测模型在识别OAG进展方面效率提高了29%（P<0.0001），并且比遵循固定的年度监测时间表提前57%检测到OAG进展（减少诊断延迟）（P = 0.02），同时没有增加所需的VF测试和IOP测量次数。该模型对轻度和重度疾病患者均表现良好。与未进展的患者相比，该模型对表现出OAG进展的患者进行的测试显著更多（每年1.3次对1.0次测试；P<0.0001）。
与每年固定的监测间隔相比，使用动态个性化测试时间表可以提高OAG进展检测的效率并减少诊断延迟。如果进一步的验证研究证实这些发现，此类算法可能能够极大地改善OAG的管理。
