Purpose

To generate personalized forecasts of how patients with open-angle glaucoma (OAG) experience disease progression at different intraocular pressure (IOP) levels to aid clinicians with setting personalized target IOPs.

Design

Secondary analyses using longitudinal data from 2 randomized controlled trials.

Participants

Participants with moderate or advanced OAG from the Collaborative Initial Glaucoma Treatment Study (CIGTS) or the Advanced Glaucoma Intervention Study (AGIS).

Methods

By using perimetric and tonometric data from trial participants, we developed and validated Kalman Filter (KF) models for fast-, slow-, and nonprogressing patients with OAG. The KF can generate personalized and dynamically updated forecasts of OAG progression under different target IOP levels. For each participant, we determined how mean deviation (MD) would change if the patient maintains his/her IOP at 1 of 7 levels (6, 9, 12, 15, 18, 21, or 24 mmHg) over the next 5 years. We also model and predict changes to MD over the same time horizon if IOP is increased or decreased by 3, 6, and 9 mmHg from the level attained in the trials.

Main Outcome Measures

Personalized estimates of the change in MD under different target IOP levels.

Results

A total of 571 participants (mean age, 64.2 years; standard deviation, 10.9) were followed for a mean of 6.5 years (standard deviation, 2.8). Our models predicted that, on average, fast progressors would lose 2.1, 6.7, and 11.2 decibels (dB) MD under target IOPs of 6, 15, and 24 mmHg, respectively, over 5 years. In contrast, on average, slow progressors would lose 0.8, 2.1, and 4.1 dB MD under the same target IOPs and time frame. When using our tool to quantify the OAG progression dynamics for all 571 patients, we found no statistically significant differences over 5 years between progression for black versus white, male versus female, and CIGTS versus AGIS participants under different target IOPs (P > 0.05 for all).

Conclusions

To our knowledge, this is the first clinical decision-making tool that generates personalized forecasts of the trajectory of OAG progression at different target IOP levels. This approach can help clinicians determine appropriate, personalized target IOPs for patients with OAG.

中文翻译：

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使用过滤预测方法个性化预测不同目标眼压水平下的青光眼进展

目的

生成有关开角型青光眼（OAG）患者在不同眼压（IOP）水平下疾病进展情况的个性化预测，以帮助临床医生设置个性化目标IOP。

设计

使用来自2个随机对照试验的纵向数据进行二级分析。

参加者

来自协作性初始青光眼治疗研究（CIGTS）或晚期青光眼干预研究（AGIS）的中度或晚期OAG的参与者。

方法

通过使用试验参与者的视野和眼压数据，我们为快，慢和非进行性OAG患者开发并验证了卡尔曼滤波（KF）模型。在不同的目标IOP水平下，KF可以生成个性化且动态更新的OAG进度预测。对于每位参与者，我们确定了如果患者在接下来的5年中将其IOP维持在7 mmHg（6、9、12、15、18、21或24 mmHg）中的1个水平，则平均偏差（MD）将会如何变化。如果IOP比试验中达到的水平增加或减少3、6和9 mmHg，我们还将对同一时间范围内MD的变化进行建模和预测。

主要观察指标

在不同目标IOP水平下MD的变化的个性化估计。

结果

总共571名参与者（平均年龄，64.2岁；标准差，10.9）被随访，平均6.5年（标准差，2.8）。我们的模型预测，在5年内，平均目标眼压分别为6、15和24 mmHg时，快速进展者平均将分别损失2.1、6.7和11.2分贝（dB）MD。相反，在相同的目标IOP和时间范围内，缓慢的进程平均会损失0.8、2.1和4.1 dB的MD。当使用我们的工具量化所有571名患者的OAG进展动态时，我们发现在不同目标IOP下，黑人与白人，男性与女性以及CIGTS与AGIS参与者之间的进展在5年内没有统计学上的显着差异（所有P均> 0.05 ）。

结论

据我们所知，这是第一个临床决策工具，可以生成在不同目标IOP水平下OAG进展轨迹的个性化预测。这种方法可以帮助临床医生为OAG患者确定合适的个性化目标眼压。