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# A Model to Estimate the Unmedicated Intraocular Pressure of Patients on Glaucoma Medications

Olivier Benoit, Clotilde Jumelle, Esmeralda Meunier, Philippe Sourdille,

Christophe Baudouin, Antoine Labbe

► To cite this version:

Olivier Benoit, Clotilde Jumelle, Esmeralda Meunier, Philippe Sourdille, Christophe Baudouin, et al. A Model to Estimate the Unmedicated Intraocular Pressure of Patients on Glaucoma Medications. Journal of Glaucoma, 2025, 10.1097/ijg.0000000000002638. hal-05297659

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Submitted on 7 Oct 2025

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# A Model to Estimate the Unmedicated Intraocular Pressure of Patients on Glaucoma
## Medications

**Authors:** O. BENOIT¹, C. JUMELLE², E. MEUNIER³, P. SOURDILLE¹,⁴, C. BAUDOUIN⁵, A. LABBE⁵,⁶

**Affiliations:**

1 Ciliatech SAS, Chavanod, France

2- Kea scientific, Fresnoy-en-Gohelle, France

3- Z-estima Ltd, Bath, United Kingdom

4 - Institut Sourdille Atlantique, Nantes, France

5 - Department of Ophthalmology, Quinze-Vingts National Eye Hospital, IHU FOReSight, Paris, France.

6- Ambroise Paré Hospital, APHP, UVSQ Paris Saclay University, Boulogne Billancourt, France

**Corresponding authors:**

Olivier Benoit
Ciliatech
178 Route de Cran-Gevrier
PAE ALTAIS c/o Galileo
74650 Chavanod France
+ 33 (0)6 35 55 01 53
Email: o.benoit@cilia.tech
**Conflict of interest:**

CILIATECH funded this study. PS and OB are co-founders of CILIATECH.

CJ is a professional Medical Writer and EM a professional Statistician, both contracted and paid by Ciliatech for conducting and writing the report. CB and AL are part of the monitoring board for Ciliatech clinical trials and received consultancy fees from Ciliatech.

**Précis:**

This report presents a model which can be used to determine the IOP-lowering effect of a glaucoma procedure or treatment when a medication washout is not ethical or not possible to be performed.

**Abstract:**

**Background:** The effect of individual (or certain predefined combinations of) glaucoma medications on intraocular pressure (IOP) has been well documented in various clinical trials. Conversely, the cumulative effect of several glaucoma medications, let alone their discontinuation (i.e. washout), on IOP is still relatively unknown. This is why it is generally recommended to perform a medications' washout to analyze the effect of glaucoma procedure without confounding factors. However, a washout is not always possible or ethical for some patients and can also increase the costs and complexity of clinical trials.

**Aims:** To build and validate a mathematical model of the IOP change after glaucoma medications washout.

**Method:** A literature search was conducted to identify studies reporting IOP values before and after washout, as well as the number of glaucoma medications used. Based on this, a quadratic equation was fitted to estimate the IOP change after washout, using the mean medicated IOP and the mean number of medications as input parameters.

**Findings:** A total of 31 groups (each group having a precise mean medicated IOP, mean number of medications, and mean post washout IOP) from 15 studies were selected to build and validate the model. Overall, over 90% of the variability of IOP change reported in the studies was
explained by the model (R²=0.9007). The mean difference between the IOP changes estimated by the model and the IOP changes reported in the studies was less than 1 mmHg.

**Conclusion:** Estimating the IOP change using this model may represent an useful tool to determine the IOP-lowering effect of a glaucoma procedure or treatment when the washout is not ethical or not possible to be performed on patients. More studies need to be conducted to validate the practical robustness of this formula.

**Keywords:** glaucoma, intraocular pressure, IOP-lowering medications, hypotensive treatment, washout.
# 1. Introduction

Glaucoma is one of the leading causes of blindness worldwide, leading to a significant burden for patients and healthcare systems¹. Nowadays, the most common strategy for glaucoma treatment is to lower the intraocular pressure (IOP), which represents one of the major risk factors of the disease². The selection of the most appropriate treatment for each glaucomatous patient represents a difficult choice for ophthalmologists and surgeons. The most common endpoint reported in clinical trials to assess the effectiveness of a glaucoma treatment is usually the IOP-lowering effect. However, the assessment on this effect for a specific procedure can be biased in cases where there is a concomitant use of hypotensive medications.

To avoid this bias, the World Glaucoma Association recommends performing a washout period (i.e. off medication) at baseline and postoperative timepoints³. Moreover, the FDA recommends a washout period for premarket studies assessing the performance of minimally invasive glaucoma surgical (MIGS) devices⁴. However, a washout period is not always safe, or ethical, to perform, especially for patients with severe forms of glaucoma who need glaucoma medications. Moreover, a washout period can be difficult to implement in clinical studies and can increase both the complexity and costs of clinical trials. For all these reasons, a washout is not systematically performed in clinical studies, making it difficult to objectively compare the effectiveness of different glaucoma procedures. Therefore, mathematical estimation of post-washout IOP could be an interesting tool to evaluate and compare the effectiveness of glaucoma treatments without the confounding effect of glaucoma drugs when washout is not possible to perform.

The IOP-lowering effect of different types and combinations of medications has been extensively studied in randomized clinical trials (RCTs). However, there is a well-known gap between the medication efficacy assessed during RCTs and the real-world effectiveness observed in clinical practice⁵. Also, glaucomatous patients are often prescribed more than one IOP-lowering medication. Two large studies (HORIZON and COMPASS) conducted on a total of 1,400 glaucomatous eyes assessed the postoperative IOP in patients after implantation of a MIGS device (Hydrus micro-stent for HORIZON and Cypass micro-stent for COMPASS). A secondary analysis of these studies was then performed to assess the IOP-lowering effect of 1 to 4
medications using washout⁶,⁷. This gap can be explained by the design of RCTs such as a strict patient selection, short period of study, high patient adherence and proper drug administration, which are not always representative of the real-world setting, especially for glaucomatous patients⁸. The second learning is that the total IOP reduction effect of an anti-glaucoma multi-medications regimen is less than the sum of the individual effects of each medication in the regimen.

The aim of this report is to propose a model to estimate the IOP change after washout of a group of patients based on their mean medicated IOP and the mean number of topical ocular IOP-lowering medications they are taking and to validate the model based on data reported in the literature.

## 2. Material and methods

### 2.1. Literature search and study selection

A literature search was conducted to identify studies reporting the effect of glaucoma medications on IOP using the technique of washout. A search using the keywords "IOP", "glaucoma", "washout" and "post-washout" was performed in the PubMed® database. Studies that reported both pre- and post-washout IOP as well as the mean number of IOP-lowering medications were considered, whilst studies assessing the washout effect on patients with normal tension glaucoma were excluded.

In total, we found 17 studies that met the inclusion/exclusion criteria. After further review, one study was excluded as the data appeared to be outliers⁹, and another one was also excluded due to data duplicates⁷. As a result, 15 studies comprising of 31 groups were selected to create and validate the model (Table 1). A "group" is defined by a specific and unique set of patients in a study (therefore a study can have multiple groups, but a patient can only belong to one group). To be usable for our analysis, a minimal set of data shall have been reported including mean medicated IOP, mean number of medications, and mean post washout IOP. For instance, Johnson et al. introduces 4 groups each of them intaking a specific number of medications (i.e. 1, 2, 3 or 4)⁶. In Samuelson et al. and Craven et al., one group corresponds to the treatment group and the second group corresponds to the control group¹⁰,¹¹. In Katz et al., the three different groups correspond to the implantation of 1, 2 or 3 micro-bypass stents¹². In Ho et al., the three different groups correspond to three different types of disease, i.e. open-angle glaucoma (OAG),
ocular hypertension and uveitic glaucoma¹³. Finally, in Myers *et al.*, the different groups listed in Table 1 correspond to different study visits performed by the same set of patients¹⁴. For the purpose of this analysis, we assumed that one medication corresponds to a class of molecules (e.g. prostaglandin analogs, beta blockers, carbonic anhydrase inhibitors). However, it is possible that the authors considered a combination of products as only one medication. Most of these studies were conducted on White/Caucasian patients with OAG, but several different ethnicities are also represented. Other types of glaucoma included angle-closure glaucoma, ocular hypertension, uveitic glaucoma and refractory glaucoma. The number of glaucoma medications ranged from 0 to 4, with most groups reporting between 1 and 2 medications prior to washout.

## INSERT TABLE 1

### 2.2. Determination of the model type

*Johnson et al.* study reported the results of the two largest studies (COMPASS and HORIZON) aiming to describe the effect of glaucoma medications washout⁶. For this reason, data from these two studies were analyzed to determine the type of model best adapted to estimate IOP change based on the number of glaucoma medications and the pre-washout IOP. The authors concluded to a "generalized linear model" between IOP change after washout and the number of glaucoma medications. We performed further calculations on the data and found out that by using a linear model with an intercept value of 4.16, the regression line does not pass through or close to the (0,0) point. This suggest that when no IOP-lowering medications are used, an IOP change would be observed after washout. However, it is clinically expected that without IOP-lowering medications, no change in IOP occurs (as shown table above in 1st line *Johnson et al.* study). Therefore, a linear model was considered as not appropriate, and a quadratic regression model was chosen instead.

### 2.3. Creation of the model

Priority was given to building a model as precise as possible by including as much information as possible. Due to the limited number of published studies, the subjective number of 5 was set as the number of groups retained for validating the model. Data from *Johnson et al.* were included in the set of data used to build the model, since the HORIZON and COMPASS studies represent the largest population size of all studies we retrieved from the literature⁶. Being the third largest study from our list of 15 studies, the data from the *Samuelson et al.* study were also
used to build the model. In addition, the data from the *Myers et al.* study were selected to build
the model. This study showed the lowest pre-washout IOPs of all (i.e. 12-13 mmHg in this study
compared to 15-25 mmHg for all the other studies). Thereby, the model could be representative
of a larger range of pre-washout values. Each time point of the *Myers et al.* study was included
as data in the model fitting despite the between-visit correlation. Finally, 11 of the 16 remaining
groups were randomly selected for the building of the model. The list of the groups used to build
the model is shown in **Table 1**. In total, 26 groups (3,367 patients) were used to build the model
The fitted model was weighted based on the population size of each study group. The weighting
was performed using the number of subjects in the corresponding group as the weight variable in
the SAS® statement used for generating the model.

Only one group was found with the “0 medication” datapoint⁶. In order to build a clinically
relevant model that passes through the (0,0) point, the weight of the “0 medication” was
increased from its original value (100, i.e. population size of the “0 medication” group) to 518
(i.e., the total population size of the COMPASS study). The number of medications and pre-
washout IOP (also referred to as medicated IOP) were included as factors in the model, in
addition to the interaction factor. The goodness of fit test was used to evaluate the exclusion of a
factor from the full model. R² was calculated to evaluate the percentage of variability in IOP
change explained by the number of medications and pre-washout IOP. The model was created
using SAS® Version 9.4.

## 2.4. Validation of the model

The five remaining groups (348 patients) from the literature search that were not selected to
build the model were used to validate its applicability (**Table 1**). To do so, IOP change after
washout was compared to the IOP change calculated using the estimates of the fitted model. The
difference between the calculated and reported IOP changes were analyzed to assess the
robustness of the formula.
## 3. Results

### 3.1. Presentation of the model

Based on the data of the 26 groups selected to build the model, a quadratic equation describing IOP change as a function of the mean number of glaucoma medications and the mean pre-washout IOP was fitted (Equation 1).

$$ \begin{aligned} \text{IOP Change (mmHg)} & = 0.10 + 6.70 \times (\text{mean number of medications}) - 0.93 \\ & \times (\text{mean number of medications})^2 - 0.06 \\ & \times (\text{mean number of medications}) \times (\text{pre\_washout IOP}) \end{aligned} $$

**Equation 1. Model estimating the IOP change after washout.**

The final model does not include a factor for pre-washout IOP due to the non-statistically significant interaction between the number of IOP lowering medications removed is clinically expected to lead to a larger increase in IOP for patients with a low pre-washout IOP compared to patients with a high pre-washout IOP, the interaction between the number of IOP lowering medications and pre-washout IOP was kept into the final model, despite being not statistically significant (p-value Likelihood Ratio test=0.3759).

As shown in **Figure 1**, 90% of the variability in IOP changes due to washout is explained by the model (R²=0.9007). Moreover, 88% (23 out of 26) of IOP change values calculated using the model formula was within ± 2 mmHg of the corresponding reported IOP change values.

**INSERT FIGURE 1**

For illustration purpose, when taking the subgroup of data from the *Myers et al.* study, the difference between the post-washout IOP (corresponding to unmedicated IOP) reported and the one calculated by the model was less than 2 mmHg for each timepoints and less than 1 mmHg for 3 out 4 timepoints (Figure 2).

**INSERT FIGURE 2**

On one hand, the model suggests that washing out medication in those patients who are prescribed a single medication would increase the pre-washout IOP by 27%, for a given pre-
washout IOP of 18 mmHg. Washing out medications in those patients with 2 prescribed medications would increase IOP by an additional 16% compared to those on a single prescribed medication, and by an additional 5% in those with 3 prescribed medications. One the other hand, washing out medications in patients prescribed 4 medications would decrease IOP by 5% from the 48% increase resulting in stopping 3 medications.

## 3.2. Model validation

To evaluate the validity of the model, the model was fitted to the data of the five studies assigned to the model validation. It was found that all predicted IOP changes were within the +/- 2 mmHg window (Figure 3).

## INSERT FIGURE 3

The mean absolute difference between IOP changes estimated by the formula and the IOP changes reported in the 31 groups was 0.87 ± 0.68 mmHg.

## 4. Discussion

In this report, we described a new model to estimate the IOP change after washout based on pre- and post-washout data reported in studies selected from the literature. Figure 4 illustrates IOP changes after washout reported in all the selected studies based on the number of glaucoma medications. Interestingly, the IOP changes observed between 0 and 1 washed out medication looked higher than the IOP changes observed between 1 and 2 washed out medications, 2 and 3 washed out medications or 3 and 4 washed out medications. This suggests that, when prescribing medications, the IOP-lowering effect of the second, third and fourth prescribed medications is much lower compared to the one of the first prescribed medication. Similar trends have been reported in previous studies⁶,⁷. The IOP-lowering cumulative effect of 2 or more prescribed medications is not well documented. It is assumed that the first-line medication would usually correspond to the medication with the most potent effect, which would lead to a lessened effect of the second, third and fourth prescribed medications. Another explanation is that treatment compliance was shown to decrease when more than one medication is prescribed²³,²⁴.
## INSERT FIGURE 4

In this report, we introduce a model that estimates IOP change following glaucoma medications washout, allowing for a calculated post-washout IOP (cal_IOP) as an expression of the mean pre-washout IOP and mean number of IOP-lowering medications (Equation 2).

$$ Cal\_IOP = 0.10 + 6.70 \times (\text{mean number of medications}) - 0.93 \times (\text{mean number of medications})^2 + (1 - 0.06 \times (\text{mean number of medications})) \times (\text{pre\_washout IOP}) $$

### Equation 2. Model calculating the IOP (cal_IOP) after washout.

Based on all 31 groups, the mean absolute difference between the IOP changes estimated using the formula and the IOP changes reported in the 31 groups was found to be less than 1 mmHg.

This suggests that the model is accurate enough to determine the unmedicated IOP when a washout is not possible or ethical to perform. The effect of specific factors such as age, gender, length of time since taking glaucoma drops, prior surgeries and lasers, type of IOP lowering agent were not assessed because these factors were not detailed in the studies.

The use of this model includes several limitations. First, the model was built using aggregated data rather than individual data. We therefore suggest using it on groups of patients instead of individuals. With access to the individual data, one could fit an updated model for better precision. Methods to measure the reported IOPs were not systematically reported in the publications, so that a regression-to-the-mean phenomenon cannot be excluded for some studies. This might lead to an overestimated washout effect.

Moreover, the set of data used to build the model covers a large pre-washout IOP span from 12 to 22 mmHg. None of the studies reported a mean pre-washout IOP above 22 mmHg, probably because of the inclusion/exclusion criteria restricting high IOP values in order to avoid exposing patients to visual damage after washout. Therefore, we were not able to assess in this report the validity of the formula on higher pre-washout IOP. However, it is common to find clinical studies publications without washout but with medicated baseline IOP above this threshold.

Among other parameters, the response to medication treatments can also be influenced by ethnicity or the type of glaucoma. Kalala et al. study⁹ was excluded for this reason, as the post-
washout IOP variation reported in an African population was abnormally high compared to the
other selected studies. Therefore, the authors exercise caution regarding the applicability of this
formula to types of glaucoma and ethnicities that are under or not represented in the analysis.
Another limitation of this study is that most IOP data used to build our model are derived from
pre-intervention (i.e. before MIGS implantation) data which can impact the applicability of our
model to estimate IOP change after MIGS implantation. However, the mode of action of IOP
lowering medications and MIGS are generally very different, and to the best of our knowledge,
no study reported that implantation a MIGS resulted in a decrease or increase in the efficacy of
a IOP-lowering medication. More data reporting IOP before and after washout at post-
intervention timepoints are still needed to confirm the applicability of our model after MIGS
implantation.

Another limitation resides in the relevance of the equation for large values of the number of IOP-
lowering medications. For a pre-washout IOP value of 18 mmHg, the equation results in a
*cal_IOP* values of 22.8, 25.6, 26.6 and 25.7 mmHg for 1, 2, 3 and 4 IOP-lowering medications,
respectively (**Table 2**). These estimates derived from the equation adequately reflect the clinical
effects observed when prescribing medications: the lower effect of the second and third
prescribed medications compared to the effect of the first prescribed medication. However,
according to the model, the *cal_IOP* after washout of 4 medications is lower compared to the
*cal_IOP* after washout of 3 medications, suggesting the 4th washed out medication would not
increase but decrease the post-washout IOP. This result may appear counterintuitive, however it
has been reported in some publications that prescribing a 4th medication can increase eye
irritation and therefore be deleterious to IOP control²⁵,²⁶. This result is most likely due to the fact
that most of the datapoints used to build the model were between 1 and 2 medications with a
single datapoint related to “4 medications”, and 2 datapoints related to “3 medications”. In
addition, the sample size, used as a weight in the model fitting, of the “4 medications” datapoint
was low (i.e. 25), while on the opposite the “0 medication” datapoint was overweighed to force
the model to clinical significance. As a consequence, the equation appears to be not applicable
for a number of medications superior to 3. Nevertheless, the prescription of more than 3 IOP-
lowering medications to patients with glaucoma remains limited in clinical practice, and the
clinical effect of the 4th medication hardly predictable.
Another counter-intuitive result derived from the model is that the higher the pre-washout IOP is, the lower is the IOP increase; while, when prescribing drops to improve IOP levels, the higher the IOP is, the larger mmHg decrease will be observed. One hypothesis is that, for a given number of medications, patients with low IOP prior to washout might correspond to “good” responders compared to patients with high IOP. An additional observation derived from the model is that, for a given number of medications, the amplitude of the IOP increase after washout appears to be lower than the IOP decrease observed when prescribing medications to an unmedicated patient. This observation could be explained by the timeframe between the washout and the first intake of the drops, which would most probably be measured in months, if not years. The intake of continuous medication over a large period of time, combined with the ageing process modifying the anatomy and physiology of the eye, could explain the change in the response to drugs. The patients enrolled in the studies used to build the model tend to be patients who are resistant to medications. This might also explain why the washout is not increasing the IOP in the same manner as medications would decrease an unmedicated IOP

We believe that this formula can be helpful in estimating unmedicated IOP after a glaucoma
surgical intervention with or without medical devices (including MIGS) when a washout is not
ethical or feasible.

INSERT TABLE 2

In order to make this model accessible to everyone, a free access internet page was created and is
available at https://www.cilia.tech/cal_IOP. By entering the mean pre-washout IOP and the mean
number of glaucoma medications taken by a group of patients, the page gives a calculated post-
washout IOP using the equation described in this report. We believe that the estimation, even if
approximate, of the post-washout IOP could help clinicians to better assess the risk/benefit ratio
of a glaucoma treatment, as well as provide unbiased parameters for medico-economic activities
such as reimbursement. This tool can ultimately help ophthalmologists and surgeons to choose
the most appropriate treatment for their patients.

5. Conclusion

The total number of published clinical studies on glaucoma is large, however only a small
number of studies describe the use of a medication washout, and an even smaller number of
these studies report IOP values before and after washout despite the recommendations from glaucoma associations or regulatory bodies. This can be understood because of the difficulties faced by the industry and clinicians in implementing medication washout in clinical trials, or the risk a washout would pose on the patient's visual health. From our perspective, this reinforces the need for a mathematical formula in estimating post-washout IOP.

In this report, we demonstrated that over 90% of the variability in IOP change due to washout was explained by the model (R²=0.9007). When an IOP-lowering medication washout is not logistically possible or cannot be performed for ethical reasons, the proposed mathematical model can be used to estimate the IOP change of groups of patients with glaucoma and on hypotensive medications with a mean absolute difference lower than 1 mmHg compared to reported values.
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<figure><img src="image_4.png" /><figcaption>Figure 1. Calculated IOP change based on the model versus reported IOP change for each group included in the dataset used to create the model.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Comparison of the post-washout IOP reported in Myers et al. study versus the post-washout IOP estimated by the model, i.e. post-washout IOP = pre-washout-(0.10 - +6.70*(number of medications)-0.93*(number of medications))² -0.06*(number of medications)*(pre-washout IOP)).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 3. Estimated IOP change estimated using the model versus IOP change reported for each group used to validate the model</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Bubble chart representing mean IOP changes between pre-washout and post-washout IOP based on the number of medications taken before washout. One bubble represents one group. The size of the bubble corresponds to the population size of the group. *The (min), (ave) and (max) curves represent IOP change derived from the formula for the minimum, average and maximum reported pre-washout IOP values, respectively. The curves show that IOP increase is lower when pre-washout IOP is higher, as one could expect from clinical experience. The horizontal dotted lines were calculated using the mean IOP changes from the HORIZON and COMPASS studies<sup>6</sup>.*</figcaption></figure>
<table><caption>Table 1. Description of the studies including to build and validate the model.</caption><thead><tr><th>Study</th><th>Gro up size</th><th>Condi ti on</th><th>Race/Ethn icity</th><th>Washou t period</th><th>Numbe r of glauco ma medica tion</th><th>Pre- wash out IOP, mmH g</th><th>Post- wash out IOP, mmH g</th><th>Used to build/vali date the model</th><th>Re f.</th></tr></thead><tbody><tr><td rowspan="4">Johnson et al. (HORIZ ON trial)</td><td>429</td><td>OAG</td><td>78.2% White, 11.1% Black, 6.8% Asian, 3.8% Others</td><td rowspan="4">PG = 4 w, BB = 4 w, AA = 2 w, CA = 7 d, CAI = 2 w</td><td>1</td><td>17.8 ± 3.4</td><td>23.6 ± 4.1</td><td>Build</td><td rowspan="4">6</td></tr><tr><td>202</td><td>OAG</td><td></td><td>2</td><td>17.3 ± 3.3</td><td>24.3 ± 4.5</td><td>Build</td></tr><tr><td>125</td><td>OAG</td><td></td><td>3</td><td>17.5 ± 3.6</td><td>26.3 ± 6.3</td><td>Build</td></tr><tr><td>25</td><td>OAG</td><td></td><td>4</td><td>16.8 ± 4.2</td><td>26.5 ± 5.5</td><td>Build</td></tr><tr><td rowspan="4">Johnson et al. (COMP ASS trial)</td><td>100</td><td>OAG</td><td>83.7% White, 9.1% Black, 1.2% Asian, 6.0% Others</td><td rowspan="4">PG = 4 w, BB = 4 w, AA = 2 w, CA = 7 d, CAI = 5 d</td><td>0</td><td>24.2 ± 3.2*</td><td>24.0 ± 2.9*</td><td>Build</td><td rowspan="4">6</td></tr><tr><td>276</td><td>OAG</td><td></td><td>1</td><td>17.5 ±3.2</td><td>22.9 ± 3.4</td><td>Build</td></tr><tr><td>153</td><td>OAG</td><td></td><td>2</td><td>17.4 ± 3.2</td><td>24.3 ± 3.6</td><td>Build</td></tr><tr><td>89</td><td>OAG</td><td></td><td>3</td><td>17.4 ±3.2</td><td>26.1 ± 4.2</td><td>Build</td></tr><tr><td>Lindstro m et al.</td><td>57</td><td>OAG</td><td>100% Caucasian</td><td>1 m</td><td>1</td><td>19.5 ± 1.5</td><td>24.4 ± 1.3</td><td>Build</td><td>15</td></tr><tr><td>Saheb et al.</td><td>39</td><td>OAG</td><td>100% White</td><td>1 m</td><td>1</td><td>20.6 ± 2.0</td><td>24.1 ± 1.4</td><td>Validate</td><td>16</td></tr><tr><td>Realini et al.</td><td>133</td><td>OAG</td><td>100% Afro-Caribbean</td><td>4 to 6 w</td><td>1.2</td><td>15.5 ± 3.4</td><td>21.2 ± 3.4</td><td>Build</td><td>17</td></tr><tr><td>Sng et</td><td>31</td><td>ACG/O</td><td>100%</td><td>Not</td><td>1.4</td><td>15.6 ±</td><td>22.1 ±</td><td>Build</td><td>18</td></tr></tbody></table>
<table><thead><tr><th>al.</th><th></th><th>AG</th><th>Chinese</th><th>reported</th><th></th><th>2.7</th><th>3.6</th><th></th><th></th></tr></thead><tbody><tr><td rowspan="2">Samuels<br>on et al.</td><td>118</td><td></td><td></td><td>CAI = 5,<br>AA = 2<br>w, BB =<br>4 w, PG<br>= 4 w,<br>combination<br>products<br>= 4 w,<br>pilocarpine<br>= 4 w</td><td>1.5</td><td>17.5 ±<br>2.8</td><td>24.5 ±<br>3.1</td><td>Build</td><td rowspan="2">10</td></tr><tr><td>387</td><td>OAG</td><td>72.9%<br>White,<br>19.9%<br>Black,<br>6.2%<br>Hispanic/Latino</td><td></td><td>1.6</td><td>17.5 ±<br>3.0</td><td>24.8 ±<br>3.3</td><td>Build</td></tr><tr><td rowspan="2">Craven<br>et al.</td><td>101</td><td></td><td></td><td>Not<br>reported</td><td>1.5</td><td>17.9 ±<br>3.0</td><td>25.2 ±<br>3.6</td><td>Validate</td><td rowspan="2">11</td></tr><tr><td>98</td><td>OAG</td><td>Not<br>reported</td><td>Not<br>reported</td><td>1.6</td><td>18.6 ±<br>3.4</td><td>25.4 ±<br>3.6</td><td>Build</td></tr><tr><td rowspan="3">Katz et<br>al.</td><td>40</td><td></td><td></td><td></td><td>1.5</td><td>20.4 ±<br>1.8</td><td>25.1 ±<br>1.9</td><td>Build</td><td rowspan="3">12</td></tr><tr><td>38</td><td>OAG</td><td>100%<br>Caucasian</td><td>1 m</td><td>1.7</td><td>19.8 ±<br>1.3</td><td>25.0 ±<br>1.1</td><td>Validate</td></tr><tr><td>41</td><td></td><td></td><td></td><td>1.8</td><td>20.1 ±<br>1.6</td><td>25.0 ±<br>1.7</td><td>Build</td></tr><tr><td>Gallard<br>o et al.</td><td>149</td><td>OAG</td><td>82% White</td><td></td><td>1.8</td><td>17.1 ±<br>3.0</td><td>23.9 ±<br>3.0</td><td>Build</td><td>19</td></tr><tr><td rowspan="3">Ho et<br>al.</td><td>110</td><td>OAG</td><td></td><td></td><td>2</td><td>18.1 ±<br>3.3</td><td>26.6 ±<br>4.8</td><td>Build</td><td rowspan="3">13</td></tr><tr><td>33</td><td>OHT</td><td>Not<br>reported</td><td>1 m</td><td>1.4</td><td>18.8 ±<br>3.3</td><td>26.4 ±<br>3.9</td><td>Build</td></tr><tr><td>43</td><td>UG</td><td></td><td></td><td>2</td><td>17.9 ±<br>8.8</td><td>23.1 ±<br>10.1</td><td>Validate</td></tr><tr><td>Lazcano<br>-Gomez<br>et al.</td><td>20</td><td>OAG</td><td>100%<br>Hispanic</td><td>PG = 4<br>w, BB =<br>2 w,<br>miotics<br>= 5 d,<br>CAI = 5</td><td>2</td><td>16.3 ±<br>3.6</td><td>23.5 ±<br>2.5</td><td>Build</td><td>20</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td></td><td>d<br>combination<br>products<br>based on<br>the<br>longest<br>washout<br>of<br>components</td><td></td><td></td><td></td><td></td></tr><tr><td>Berdahl<br>et al.</td><td>53</td><td>OAG</td><td>Not<br>reported</td><td>PG = 4<br>w, BB =<br>4 w, AA<br>= 2 w,<br>CAI = 5<br>d,<br>pilocarpine = 4<br>w,<br>combination<br>products<br>= 4 w</td><td>2</td><td>19.7 ±<br>1.6</td><td>24.9 ±<br>1.1</td><td>Build</td><td>21</td></tr><tr><td>Hernsta<br>dt et al.</td><td>37</td><td>ACG</td><td>89.2%<br>Chinese,<br>8.1%<br>Indian,<br>2.7%<br>Malay</td><td>Not<br>reported</td><td>1.5</td><td>17.5 ±<br>3.82</td><td>24.6 ±<br>3.41</td><td>Validate</td><td>22</td></tr><tr><td rowspan="4">Myers<br>et al.**</td><td>80</td><td></td><td></td><td rowspan="4">Not<br>reported<br>1 m</td><td>1.2</td><td>22.0 ±<br>3.1</td><td>26.4 ±<br>2.4</td><td>Build</td><td rowspan="4">14</td></tr><tr><td>78</td><td rowspan="3">RG</td><td></td><td>1.07</td><td>13.6 ±<br>1.5</td><td>17.1 ±<br>1.4</td><td>Build</td></tr><tr><td>77</td><td></td><td>1.07</td><td>12.5 ±<br>1.3</td><td>17.2 ±<br>1.3</td><td>Build</td></tr><tr><td>72</td><td></td><td>1.07</td><td>12.6 ±</td><td>17.1 ±</td><td>Build</td></tr></table>
<table><tr><td></td><td></td><td></td><td></td><td></td><td>1.0</td><td>1.5</td><td></td></tr><tr><td>63</td><td></td><td></td><td></td><td><b>1.07</b></td><td>12.9 ±<br>0.9</td><td>18.4 ±<br>1.4</td><td>Build</td></tr></table>

*There is no washout for this group. These data show that without a change in medications, IOP remains stable as expected. **Each row reported for this study represents a different study visit for the same set of patients. Abbreviations: AA, alpha adrenergic agonists; ACG, angle-closure glaucoma; BB, beta-adrenergic antagonists or beta-blockers; CA, cholinergic agonists; CAI, carbonic anhydrase inhibitors; OAG, open-angle glaucoma; OHT, ocular hypertension; PG, prostaglandin analogues; RG, refractory glaucoma; UG, uveitic glaucoma.
<table><caption>Table 2. Calculation of the cal_IOP based on the number of medications and a pre-washout IOP of 18 mmHg.</caption><thead><tr><td>Number of medications</td><td>Calculated post-washout IOP</td></tr></thead><tbody><tr><td>1</td><td>22.8</td></tr><tr><td>2</td><td>25.6</td></tr><tr><td>3</td><td>26.6</td></tr><tr><td>4</td><td>25.7</td></tr></tbody></table>