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Keywords:

  • cost-effectiveness;
  • long-term outcomes;
  • ocular hypertension;
  • treatment

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Funding
  8. References
  9. Supporting Information

Purpose

To investigate the long-term health and economic consequences of direct treatment initiation in ocular hypertension patients.

Methods

A cost-effectiveness analysis with a societal perspective and a lifelong horizon was performed. The primary outcomes were the incremental quality-adjusted life years (QALYs) and costs of direct pressure-lowering treatment for ocular hypertension, compared to a strategy where treatment is postponed until conversion to glaucoma has been observed. We used a decision analytic model based on individual patient simulation to forecast disease progression and treatment decisions in both strategies in a representative heterogeneous patient population and in 18 patient subgroups stratified by initial intraocular pressure and additional risk factors for conversion.

Results

The incremental discounted health gain of direct treatment was 0.27 QALYs, whereas the incremental discounted costs were −€ 649 during an average lifetime of 26 years. In the simulations of patient subgroups, the model outcomes moved towards higher health gains and lower incremental costs with increasing risk of conversion in the patient population. The incremental cost-effectiveness ratio of direct treatment ranged from € 15,425 per QALY gained in the lowest-risk subgroup to dominance in the highest-risk subgroup. Probabilistic sensitivity analysis indicated that uncertainty surrounding the model input parameters did not affect the conclusions.

Conclusion

Direct, early, pressure-lowering treatment is a dominant cost-effective treatment strategy over a strategy to start the same treatment approach later, after glaucoma has occurred for patients with ocular hypertension. Its implementation and consequences should be discussed with ophthalmologists and individual patients.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Funding
  8. References
  9. Supporting Information

An elevated intraocular pressure (IOP) is a well-known risk factor for the development of primary open-angle glaucoma (POAG) that may cause blindness later in life (Ernest et al. 2013). Pressure-lowering treatment has been shown to reduce the risk of glaucoma onset (Maier et al. 2005; Kwon et al. 2009; Peeters et al. 2010; Heijl 2013). Still, the need to initiate treatment in every ocular hypertension (OHT) patient is subject of debate, since an appreciable proportion (of about 40%) of the OHT population does not develop glaucoma even if untreated, whereas treatment itself may cause discomfort and side effects (Heijl & Bengtsson 2000; Maier et al. 2005; Beckers et al. 2008). On the other hand, treating every OHT patients may prevent POAG and visual field (VF) loss to a greater extend and avoid the costs of intensive glaucoma treatment (Olsen et al. 2013). To maximize health, caregivers will have to selectively initiate treatment in patients that are expected to benefit from it, and withhold it from patients in whom the harm from treatment is expected to surpass the benefits. Currently, the European Glaucoma Society recommends to consider treatment in OHT patients if the IOP is repeatedly in the high twenties, even without risk factors, and the UK guidelines provide an algorithm for treatment initiation based on central corneal thickness, IOP and age (European Glaucoma Society 2008; National Collaborating Centre for Acute Care 2009). These guidelines are mainly based on clinical studies of treatment effectiveness, but also consider the outcomes of economic analyses. Economic evaluations assess the question whether the resource allocations required to carry out a particular guideline are justified given the expected benefits, and are therefore useful sources of information in guideline development. Currently, the availability of economic evaluations of OHT treatment to substantiate decision rules about treatment initiation in literature is limited to one report, which presents a cost-effectiveness analysis based on data from the Ocular Hypertension Treatment Study (OHTS; Kymes et al. 2006). The authors used a Markov model for glaucomatous disease progression and concluded that it is likely cost-effective to initiate OHT treatment only in patients with an IOP ≥24 mmHg and an annual risk to develop glaucoma of 2% or more. The US perspective of the study has been noted as a minor limitation to its applicability in the UK treatment guidelines. Additional issues that may further limit the applicability of these study outcomes are the fact that the analysis was based entirely on the relatively low-risk OHTS population, and the limited possibility in a Markov model to account for patient characteristics, multiple treatment options and the gradual progression of glaucoma (Caro et al. 2010; Peeters et al. 2010).

The aim of this study is to generate additional data with a patient-level simulation model to further inform (stratified) treatment guidelines for OHT by assessing the long-term effectiveness and efficiency of OHT treatment. It may aid in choosing the debated best management strategy for OHT patients were individualizing treatment based on predicted risks and monitoring progression and the best way to spend limited resources are major themes (Heijl 2013; Tuulonen 2013).

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Funding
  8. References
  9. Supporting Information

Treatment strategies compared

We investigated the cost-effectiveness of immediate pressure-lowering treatment for OHT relative to the comparator strategy ‘watchful waiting’. The two comparative strategies, that is, either monitor and treat only after conversion to POAG has occurred or treat everyone once OHT has been diagnosed, were based on the choice the ophthalmologist can make in a clinical setting. This comparison represents the two possible extremes of complete avoidance of over treatment and side-effects but not maximal prevention of conversion on the one hand, versus maximal prevention of conversion but with maximal overtreatment and occurrence of side-effect, on the other. In the untreated strategy, patients were still seen every year for monitoring, because in clinical practice it would not be acceptable to completely withhold all surveillance in an untreated patient.

In both strategies, the OHT patients were monitored with an annual follow-up visit and a VF measurement every 3 years. In the strategy ‘direct treatment’, patients immediately received pressure lowering therapy with a 21 mmHg target pressure. When treated OHT patients converted to POAG, the target pressure was adjusted to 18 mmHg and the patient was treated according to ‘usual care’ for POAG with two follow-up visits and one VF test per year (Van Gestel et al. 2012). The target pressure was further reduced to 15 mmHg in case of progression. In the comparator strategy, OHT patients did not receive treatment until conversion to POAG was seen at a follow-up visit, in which case treatment was initiated with a target pressure of 21 mmHg. As of that moment, all settings for POAG treatment according to ‘usual care’ applied to the patient, and the target IOP was adjusted to 18 mmHg at the first occurrence of progression and to 15 mmHg at the second. Treatment consisted initially of medication (monotherapy and combination therapy up to triple therapy) and laser trabeculoplasty if maximal medication was insufficient to lower the IOP below target. Additional treatment options for POAG consisted of glaucoma surgery, with co-medication if necessary (Van Gestel et al. 2012).

In order to make predictions of the long-term consequences of different treatment approaches to manage OHT, we have developed a computer model in which the disease progression of individual patients with OHT or POAG can be simulated. The development and validation of the model itself have been described extensively elsewhere (Van Gestel et al. 2010a). In addition, a detailed description of the model structure and input sources has been provided in a previous publication concerning various treatment strategies for POAG in this journal (Van Gestel et al. 2012). Here, we provide a brief summary of the simulation process and the outlines of the model structure. An additional Appendix S1 specifies the sources we used to describe the characteristics of individual OHT populations and gives a brief account of the sources on which the input parameters of the base case model and their uncertainty distributions were based.

The model simulates the disease progression of an individual OHT patient and his/her contacts with healthcare providers through discrete event simulation. A simulated patient with OHT can develop a certain degree of VF damage and thus convert to glaucoma. With the progression of time, the VF damage deteriorates and may eventually drop below a degree that represents blindness. The risk of conversion and the rate of VF progression is determined in part by the patient's predisposition and in part by the IOP. The model simulates treatment decisions at ophthalmologist visits, as a result of which the IOP is lowered and the disease progression is delayed.

The benefit of the patient-level modelling is that every relevant characteristic of a patient is accounted for and simulated in detail as it changes in time. Also, every single treatment choice is simulated and can be adapted according to a patient's status or history. The discrete event time lapse ensures that all relevant moments in time are acknowledged and that the time interval between events is adjustable to the situation.

Model structure

A single simulation started with establishing the initial characteristics (attributes) of a virtual patient by random draws from distributions representing the variation in the real OHT patient population. The parameters of these distributions are specified under ‘simulated population’ below. The simulation timeline started with an initial event representing the patient's first visit to an ophthalmologist, and the model subsequently advanced to time-points of relevant events. In addition to an ophthalmologist visit, the other events in the model were conversion from OHT to POAG and death. The patient attributes were recalculated at each event and so were the time intervals to all possible future events. The latter were linked to changes in the patient's attributes. For example, the timing of the next ophthalmologist visit was shorter when a simulated patient received a new treatment than when treatment remained unaltered. The type and timing of the next event was governed by the event with the shortest time interval. Table 1 shows the simulation process for a fictive patient. The table is only for illustrative purposes and is a simplification of the actual attributes considered in the model. The simulation ended with the death of the patient, after which all relevant outcomes from the patient's disease and treatment history were collected in a database. This process was repeated 3000 times to generate a heterogeneous cohort of patients. The cohort size of 3000 patients was chosen based on stability research of the outcomes with increasing cohort size. At cohort sizes higher than 3000 patients, the improvement in the stability of the outcomes flattened out while the computation time kept increasing proportionally to the cohort size.

Table 1. Excerpt of time progression and attribute recalculation in the model for an example patient. Time and intervals are in months.
TimeEventAgeIOPGlaucomaTreatmentInterval to next visitInterval to conversionInterval to death
  1. IOP = intraocular pressure.

0Visit5528NoNone12336300
12Visit5628NoNone127288
19Conversion5628YesNone5n.a.281
24Visit5728YesTimolol3n.a.276
27Visit5721YesTimolol6n.a.273
etc.        

The time to the next visit was determined by a cross table, in which the appropriate intervals were defined according to the treatment status and the number of visits that passed since the last treatment change. The time of death was established by a random draw at the start of the simulation from a survival curve based on survival rates in the Dutch population and the patient's initial age. The time to conversion was randomly drawn from a distribution that was based on the patient's conversion risk, which was in turn recalculated at each event based on IOP, age and the presence of other risk factors for conversion. The rationale for using the latter is that various factors apart from IOP and age have been identified as potentially associated with the development of POAG, but the evidence regarding the associations is not always conclusive (Gordon et al. 2007; Coleman & Miglior 2008). Since the risk from these factors is generally not amendable and stays constant during the simulation, the aggregate magnitude of the risk is more important than the source. We chose to use one parameter to represent the additional risk (HRother) of conversion attributable to factors other than IOP and age. The total conversion risk was calculated according to eqns (1) and (1).

  • display math(1)
  • display math(2)

In which P = cumulative probability of conversion, S = conversion-free survival, hi = current hazard rate of individual i at current event, t = time interval, HRi = total hazard ratio of individual i at current event, HRAge = hazard ratio of age, HRIOP = hazard ratio of IOP, ‹Age›i = age of individual i at current event, Ageav = average age of reference OHT population, ‹IOP›i = IOP of individual i at current event, IOPav = average IOP in the reference OHT population, HRother = hazard ratio of other risk factors, h = hazard rate in the reference OHT population.

In this equation, the hazard ratio of age was 1.26 per decade older, and the hazard ratio of IOP was 1.09 per mmHg higher (Gordon et al. 2007). The hazard rate of conversion in the reference OHT population was based on the Kaplan–Meier estimate of conversion in the OHTS, which was 9.5% in 5 years (Kass et al. 2002). The reference age and IOP in this population were 55 years and 24 mmHg, respectively.

We quantified glaucomatous damage using standard automated perimetry global index mean deviation (MD). At a conversion event, the model assigned a degree of VF loss to the converted patient by making a random draw from a distribution representing the variation in glaucomatous damage in early glaucoma patients (see ‘Simulated population’). Once a patient had VF damage, the model simulated its deterioration in time based on the personal progression rate and IOP. The fact that conversion occurred did not affect the interval until the next ophthalmologist visit, as conversion usually does not trigger care seeking behaviour. We simulated the possibility that the conversion would go undetected in an ophthalmologist visit depending on whether a VF measurement was performed or not (see ‘Treatment strategies compared’). The probability to detect conversion was 65% without a VF measurement and 100% otherwise (Kass et al. 2002).

Simulated populations

The derivations of the distribution parameters we used to characterize the heterogeneous OHT population are described in the Appendix S1. The initial IOP of each new patient was randomly drawn from a normal distribution with mean 22 mmHg, standard deviation (SD) four and truncated at 22 mmHg, which resulted in an average of 25 mmHg (Kass et al. 2002; Heeg & Jansonius 2009). The natural logarithm of the conversion risk attributable to factors other than IOP and age was also randomly drawn from a normal distribution (mean 0.0, SD 0.7). We used a normal distribution for the age of OHT patients (mean 55, SD 10) and a gamma distribution for the MD after conversion (α = 6, β = 0.5) that resulted in skewed distribution with an average of −3 dB (Kass et al. 2002; Heeg & Jansonius 2009). The percentage of men in the population was 40% (Kass et al. 2002).

We anticipated that direct treatment of OHT would be more beneficial in patients with a higher risk of conversion, so we repeated the analyses in eighteen subgroups each consisting of 3000 patients that were heterogeneous except for their initial IOP and their risk of conversion from factors other than IOP and age. We defined three levels of the latter: low, neutral and high with an HRother of 0.5, 1.0 and 2.0, respectively. For example, an HRother of 0.5 could be the result of a thicker central cornea (613 μm rather than 573 μm), and an HRother of 2.0 could result from some disc cupping (cup/disc ratio 0.56 rather than 0.36) and a thinner cornea (545 μm; Gordon et al. 2002). A further distinction in patient subgroups was made based on the initial IOP, which was set at 22, 24, 26, 28, 30 or 32 mmHg. In the subgroups, the values of the initial IOP and HRother were fixed to one value for all patients in the simulated cohort, but all other attributes were randomly drawn from distributions similar to the simulation of the heterogeneous cohort.

Cost input

All direct medical, direct non-medical and indirect non-medical costs were taken into account (societal perspective). This included costs for ophthalmologist visits, VF measurements, medication, surgery, home care (household, grooming and nursing), visual impairment rehabilitation and aids, retirement and nursing home, transportation to healthcare providers, informal care and production losses as a result of POAG based on the friction cost method. The latter entails that the period over which the production loss is calculated is limited to the time an employer needs to replace a sick employee (Koopmanschap et al. 1995). Costs were calculated as the product of cost prices and resource use. Cost prices were derived from a number of different sources and are listed in Table 2. A detailed description of the derivation of these parameter values is provided elsewhere (Van Gestel et al. 2010a). The cost year was 2006. Cost prices from sources earlier than 2006 were indexed with the healthcare specific consumer price index (http://statline.cbs.nl/StatWeb/publication/?VW=T&DM=SLNL&PA=71905NED&D1=a&D2=a&HD=081218-1319&HDR=T&STB=G1). Resource use related to ophthalmologist care, such as visits, medication and surgery, was simulated directly by the model. On the other hand, resource use related to long-term care, such as home care and rehabilitation, was estimated by linking the degree of VF loss of the simulated patient to the average resource use observed in a study with patients representing various stages of glaucoma severity (Van Gestel et al. 2010b). The costs of care included cost of paid help to take over tasks at home, care for the patient at home, cost of retirement home when moved to it because of glaucoma, cost of nursing home when moved to it because of glaucoma.

Table 2. Costs (in 2006 Euro's) associated with attributes and events in the simulation model.
ResourceCostsSource
  1. Costs for laser trabeculoplasty (LT) and surgery were doubled to account for the same procedure in the other (i.e. worse) eye. Costs for visual field measurement were doubled if progression was observed to account for a confirmatory test. Transport costs to the pharmacy were incurred once in 3 months if the patient received medication, and transport costs to the ophthalmologist/hospital were added for each visit and for each procedure (LT, surgery).

  2. Sources: (1) Health Care Insurance Board (CVZ); (2) Foundation for pharmaceutical statistics (Stichting Farmaceutische Kengetallen) (2007); (3) Oostenbrink et al. (2004); (4) Oostenbrink et al. (1999); (5) Peeters et al. (2008); (6) Van Gestel et al. (2010a); (7) Dutch Healthcare Authority (Nederlandse Zorgautoriteit) (2007); (8) Drug information system.

  3. HA = hospital administration.

  4. a

    Friction costs.

Β-blocker€ 6.00/month1, 2
Prostaglandin analogue€ 20.20/month1, 2
Carbonic anhydrase inhibitor€ 13.90/month1, 2
α2-adrenergic agonist€ 14.00/month1, 2
Ophthalmologist consultation€ 653, 4
Visual field measurement€ 133 (€ 266 in case of progression)3, 4
LT€ 754, 5
Trabeculectomy€ 1,214 (+1 ophthalmologist consultation)3, 4
Tube implantation€ 1,714 (+1 ophthalmologist consultation)3, 4
Cataract surgery€ 1,4003, HA
Paid household help€ 37/month (if MD < −10 dB)3, 6
Homecare nursing€ 159/month (if MD < −10 dB)3, 6
Family help€ 56/month (if MD < −15 dB)3, 6
Homecare grooming€ 103/month (if MD < −15 dB)3, 6
Retirement home€ 80/month (if MD < −20 dB)3, 6
Nursing home€ 130/month (if MD < −20 dB)3, 6
Informal care€ 20/month (if MD < −5 dB)3, 6
Low-vision services€ 1-5/month6, 7
Transport to ophthalmologist

€ 4.90/visit (if MD > −10 dB)

€ 8.90/visit (if MD < −10 dB)

3, 6
Transport to pharmacy

€ 1.50/visit (if MD > −10 dB)

€ 2.60/visit (if MD < −10 dB)

3, 6
Low-vision aids€ 325 (once) if MD progresses below −15 dB6, 8
Productivity loss€ 3,029 (once) if MD progresses below −15 dB while the patients is younger than 65 yearsa4, 6

Utility input

Quality-adjusted life years (QALYs) are the preferred outcomes measure when improvement in quality-of-life is an important effect of the intervention under investigation (Rodenburg-Van Dieten 2005). In order to calculate QALYs, the life years of the simulated patients were multiplied by the utility during these life years. Utility is an aggregate measure of health-related quality-of-life, which values a health state on a scale from 0 (death) to 1 (best imaginable health state). In the calculation of the utility of a simulated patient during the intervals between events, the model took three attributes into account: the presence of side effects from medication, the presence of cataract and the amount of VF loss. The initial utility value was 0.88, which was lowered by 0.101 for side effects, by 0.065 for cataract and by 0.011 for each dB loss in MD (Van Gestel et al. 2010b).

The relationship between VF and quality-of-life was based on a cross-sectional study among 537/654 (response rate 81%) of random stratified samples of OHT and POAG patients from seven hospitals in The Netherlands. Clinical information was obtained from medical files. MD values of an HFA VF within 1 year were available for 72%. In another 8% of the cases, the MD values were calculated with existing formulas from the Octopus of VF within 1–2 years. For another 7% of the patients, MD values were calculated from older HFA 30-2/24-2 tests and/or recent 10-2 tests. Seven per cent of the patients had MD values estimated based on other VF measurements, like 76 point screening or peritest. Four per cent had no VF. MD values for these patients (all POAG patients) were imputed based on worst-case imputation, as leaving out patients with missing MD values was not an option since this might have biased the results. Patients completed a questionnaire containing generic health-related quality-of-life instruments (EQ-5D and Health Utilities Index mark 3), vision-specific National Eye Institute Visual Functioning Questionnaire (VFQ-25) and glaucoma-specific Glaucoma Quality-of-Life Questionnaire (GQL-15) and questions on medication side effects. The impact of VF loss on HRQOL scores was analysed with multiple linear regression analyses including VF, cataract and side effects. More information can also be found in Van Gestel et al. (2010a).

Side effect score was entered into the regression model as a continuous number between 0 and 320, which is the cumulative score of two rating scales, pertaining to frequency (0–5) and severity (0–4), over 16 common side effects of glaucoma medication. For the disease progression model, the continuous side effect score was converted into a binary parameter (yes/no) representing the presence of a level of side effects that would prompt a change of medication. The cut-off point was set at a side effect score of 50, as this was the average side effect score of patients in the observational study that indicated that side effects of medication impacted their quality-of-life ‘Much’ or ‘Very much’. The coefficient for the continuous side effects score was multiplied by 50, which resulted in the 0.101 decrement in utility as a result of side effects in the model. Since medication is changed when side-effects occur, they do not last for a long period of time implying that their influence on QALY is limited.

Outcomes

Each patient was simulated according to both direct treatment strategy and the watchful waiting strategy. From both strategies, we collected the clinical outcomes for each simulated patient, like whether the patient converted to POAG, whether the patient progressed to blindness, which types of procedures were applied and what the average IOP was during the simulation. Additionally, we collected health economic outcomes, like the lifetime costs of treatment and care, and the QALYs. The latter were calculated as a product of the length of each interval between events and the quality-of-life expressed in utility during that interval. The health economic outcomes were collected after 10 years and after the patient died (i.e. lifelong). The future effects and costs were discounted with 1.5 and 4.0%, respectively (Rodenburg-Van Dieten 2005).

Sensitivity analyses

The variation in the incremental outcomes due to uncertainty surrounding the input parameters was assessed with a probabilistic sensitivity analysis (PSA), in which the simulation of the heterogeneous cohort was repeated multiple times with different combinations of input parameter values that were varied within their limits of uncertainty. The values of the input parameters were drawn from probability distributions representing their uncertainty. The base case values, the uncertainty distributions and the sources of information used to estimate both are described in the Appendix S1. Because the PSA was computationally intensive, we ran clusters of 25 cohort simulations. After each cluster, we evaluated how the new simulations affected the population mean incremental QALYs and costs over all cohort simulations in the PSA so far. The expectation is that once the random draws in the PSA start to form a representative sample of the joint probability, the effect of new cohort simulations on the population mean incremental QALY's and costs decreases. We observed that after 100 cohort simulations, the average incremental QALY's and costs started to stabilize, which was continued over the subsequent 50 simulations. Therefore, we ended the PSA calculations after 150 cohort simulations.

With the PSA outcomes, we calculated the probability of an acceptable balance between effects and costs at increasing thresholds of willingness-to-pay for an extra QALY (Van Hout et al. 1994). Additionally, we calculated the expected value of perfect information (EVPI) assess the value of further research to reduce uncertainty in any of the model parameters (Felli & Hazen 1998).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Funding
  8. References
  9. Supporting Information

The simulated lifetime of the patients in the heterogeneous cohort covered on average 25.7 ± 12.9 years. A comparison of the outcomes in both treatment strategies is presented in Table 3. With watchful waiting, the occurrence of conversion from OHT to POAG was 14.6% within the first 5 years and 25.2% after 10 years. Ultimately, 57.0% of the patients not treated for OHT conversed to POAG somewhere during their lifetime and 1.5% went blind. With direct treatment, the occurrence of conversion from OHT to POAG was 7.7% after 5 years, 14.7% after 10 years and 36.5% in the patients’ lifetime. Blindness occurred in 0.4% of the simulated patients. The lifetime use of medication was higher when OHT patients were treated directly, but the incidence of LT and surgery was lower.

Table 3. Average lifetime clinical outcomes of simulated patients in a heterogeneous cohort of ocular hypertension patients.
 Watchful waitingDirect treatment
  1. POAG = primary open-angle glaucoma; LT = laser trabeculoplasty; IOP = intraocular pressure; TE = trabeculectomy; ReTE = second trabeculectomy; CE = cataract extraction; MD = mean deviation; dB = decibels.

  2. a

    Percentage of the cohort in which the event occurred during the simulated life time.

IOP in follow-up (mmHg)23.617.7
Occurrence of POAGa5737
Occurrence of blindnessa1.50.4
Average number of medications0.51.3
Occurrence of LTa2321
Occurrence of TEa1512
Occurrence of ReTEa4.83.2
Occurrence of tube implanta2.92.3
Occurrence of CEa2828
End-of-life MD (dB)−5.5−2.8

The health economic outcomes are listed in Table 4. Within a time horizon of 10 years, direct treatment of OHT resulted on average in slight health gains and additional costs at an incremental cost-effectiveness ratio (ICER) of € 33,645. However, over a lifelong horizon, direct treatment resulted on average in 0.27 QALY's gained and cost reductions of € 649 per patient compared to watchful waiting.

Table 4. Health-economic outcomes of simulated patients in a heterogeneous OHT cohort after 10 years and after a lifelong horizon. Average per patient.
 Watchful waitingDirect treatmentIncrementalICER
  1. QALY = quality-adjusted life-years; OHT = ocular hypertension; ICER = incremental cost-effectiveness ratio.

10-year horizon
Costs€ 2,302€ 3,415€ 1,113€ 35,573
QALY's8.158.180.03 
Discounted costs€ 1,891€ 2,844€ 957€ 33,645
Discounted QALY's7.627.650.03 
Lifetime (mean 26 years)
Costs€ 18,327€ 14,343−€ 3,984Dominant
QALY's21.7922.170.38 
Discounted costs€ 7,722€ 7,073−€ 649Dominant
Discounted QALY's17.5517.810.27 

A breakdown of the incremental costs is provided in Fig. 1. Differences in costs between the two treatment strategies occurred mainly in two cost categories: medication and care. Direct treatment was associated with higher costs for medication, but lower costs for (informal) care. The figure also illustrates how costs further in the future are discounted more heavily. In particular, the relative contribution of costs for care is much larger in the undiscounted incremental costs than in the discounted incremental costs of direct treatment compared to watchful waiting. Figure 2 illustrates the uncertainty surrounding the ICER as a cloud of possible cost-effectiveness outcomes resulting from the PSA. The cost-effectiveness acceptability curve (Fig. 3) showed that at a willingness-to-pay threshold of € 0 per QALY, the probability that direct treatment is cost-effective was 83%. At thresholds of € 10,000 per QALY and higher, this probability had increased to 100%. Likewise, the EVPI decreased from € 96 per patient to € 0 per patient between the thresholds of € 0 and € 10,000 per QALY.

image

Figure 1. Distribution of the total costs in eight cost categories in both treatment scenarios (grey) and incremental (black) in a heterogeneous ocular hypertension population. The total height of the bars indicates the undiscounted costs; the solid bars indicate the discounted costs and the dotted portion indicates the amount that is discounted away.

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image

Figure 2. Cost-effectiveness plane showing the average incremental cost-effectiveness of direct treatment in all patients compared to watchful waiting in a heterogeneous ocular hypertension population, both in the base case model as in each of the cohort simulations in the probabilistic sensitivity analysis.

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image

Figure 3. Cost-effectiveness acceptability curve (solid black line) of direct treatment compared to watchful waiting in ocular hypertension patients, and the expected value of perfect information (dashed grey line) at increasing acceptability thresholds for the incremental cost-effectiveness ratio.

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The outcomes of the comparison of direct treatment versus watchful waiting in subgroups of OHT patients are listed in Table 5. Direct treatment resulted in health gains irrespective of the initial IOP and additional risk. The health gains were larger as the total risk of conversion in the subgroup increased. The health gains came at additional cost in the subgroups with low additional risk of conversion and in the subgroup with neutral additional risk and an initial IOP of 22 mmHg. In the other subgroups, direct treatment resulted in cost savings.

Table 5. Incremental discounted cost-effectiveness outcomes of direct treatment versus watchful waiting in subgroups of OHT patients based on initial IOP and additional risk of conversion (HRother).
 Average 5-year risk of conversionaIncremental costs (€)Incremental QALY'sICER (€ per QALY)
  1. OHT = ocular hypertension; IOP = intraocular pressure; QALY = quality-adjusted life-years; ICER = incremental cost-effectiveness ratio.

  2. a

    Calculated from age, IOP and HRother of the simulated patient population.

Low risk (HRother = 0.5)
22 mmHg4€ 1,2590.082€ 15,425
24 mmHg5€ 8510.122€ 6,954
26 mmHg6€ 6240.175€ 3,563
28 mmHg7€ 1,1270.221€ 5,088
30 mmHg8€ 8070.303€ 2,660
32 mmHg10€ 490.403€ 121
Neutral risk (HRother = 1.0)
22 mmHg8€ 5410.149€ 3,629
24 mmHg10-€ 1930.214Dominant
26 mmHg11-€ 7650.293Dominant
28 mmHg13-€ 1,0850.374Dominant
30 mmHg16-€ 1,7880.469Dominant
32 mmHg18-€ 2,8260.571Dominant
High risk (HRother = 2.0)
22 mmHg16-€ 3270.231Dominant
24 mmHg18-€ 1,2760.300Dominant
26 mmHg22-€ 1,9950.370Dominant
28 mmHg25-€ 3,1680.497Dominant
30 mmHg29-€ 4,0450.583Dominant
32 mmHg33-€ 6,0460.728Dominant

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Funding
  8. References
  9. Supporting Information

In this study, we have used a patient-level simulation model of OHT and POAG to simulate the disease progression of patients with OHT, and used its output to estimate the additional health and costs that can be expected from direct pressure-lowering treatment compared to watchful waiting. The modelling approach provides an efficient method to generate new information from available evidence, without the need to conduct clinical studies. Direct treatment turned out to be a dominant strategy over watchful waiting in a heterogeneous population of OHT patients over the lifetime horizon. Over a shorter time horizon, the cost-effectiveness of direct treatment was less favourable, with a discounted ICER of € 30,597. Although this amount may still be acceptable, it is clear that the time horizon plays an important role in the cost-effectiveness of OHT treatment. Pressure-lowering treatment in OHT is a preventative measure involving current and long-term investments to prevent health loss at a much later time period. The time horizon should therefore be long enough to capture future effects, or the ICER will overstate the contribution of short-term investments.

Direct treatment resulted in better health outcomes in all simulated subgroups. The general tendency across the subgroups was that incremental costs decreased as the initial IOP in the subgroup increased. An exception to this tendency was seen in the subgroups with a low additional risk of conversion (table 5), which showed a local ‘peak’ of incremental costs in the 28 mmHg subgroup. This observation can be explained by a shift in the balance between the short-term costs of treatment and long-term savings in low-vision-related care. Up to an initial IOP of 26 mmHg, monotherapy is sufficient to get most patients below the target pressure of 21 mmHg, but higher initial IOPs will mostly require combination therapy. The marginal costs of extra medication cause a sudden increase in overall treatment costs, which is reflected in the total incremental costs. In the subgroups with neutral and high additional risk, a small deviation from the tendency was observed due to the same effect, but it was far less pronounced because the contribution of treatment costs to the overall costs was smaller in these subgroups. Direct treatment was dominant in all subgroups, except for the subgroups with a conversion risk lower than 10% in 5 years. The latter had ICER's in the range of € 100 to € 15,500 per QALY. The implications of these ICER's for decisions regarding the desirability of direct treatment in low-risk subgroups depend on the way ICER's are used to aid decision-making. In the net monetary benefit calculations, we have assumed an acceptability threshold of € 30,000 per QALY based on ranges mentioned in literature and authority reports, although the threshold may be lower (€ 20,000 per QALY) for preventive care [Raad voor Volksgezondheid en Zorg (Council for Public Health & Health Care) (2006); Cleemput et al. 2008; Verweij et al. 2008]. The method of comparing the ICER to an acceptability threshold to gauge the relative value-for-money of the intervention has been criticized though, and if it were employed, it is very likely that the acceptability threshold varies between jurisdictions, between disease severities, and in time (Gafni & Birch 2006; Cleemput et al. 2008). We can therefore only report the value of the ICER of direct treatment in low-risk OHT patients and not speculate on its acceptability.

The outcomes of the PSA showed that even if the input parameter values are randomly varied within their uncertainty margins, the outcome of the analysis shows dominance for direct treatment initiation in the majority of cases. This implies that even though there is uncertainty about the exact value of the model's input parameters, this does not result in decision uncertainty. In addition, the EVPI dropped to zero at willingness-to-pay thresholds higher than € 10,000 per QALY, which suggests that there is no value in further research to reduce uncertainty surrounding any of the population parameters in the model. In addition to parameter uncertainty, we have considered the impact of structural uncertainty. An issue of structural uncertainty in our model is the way both eyes of the patient are handled. In the base case model, we have simulated patients rather than individual eyes and simulated that both eyes underwent similar treatment and disease progression. This structural choice involves uncertainty, as not all patients in clinical practice will present with symmetrically affected eyes. To assess the impact of this assumption, we have performed an additional analysis in which we modelled only the worse eye of the patient and assumed that the other eye remained completely unaffected. The lifetime discounted outcomes with watchful waiting in a heterogeneous OHT population were 18.04 QALY's and € 4,580, whereas the strategy with direct treatment resulted in 18.15 QALY's and € 5,830. The ICER of direct treatment was therefore € 11,523 per QALY gained. The outcomes of the base case model (dominance) and this univariate sensitivity analysis represent the two boundaries of the uncertainty spectrum regarding the symmetry of disease progression in both eyes, and all realistic scenarios encountered in clinical practice will fall within these boundaries. Structural uncertainty also played a role in the way costs related to visual impairment were accounted for. The major difference in cost is due to costs of medication use and cost of care. The latter depended on the amount of VF loss. Early treatment leads to less VF loss and thereby savings of the costs involved with this VF loss since less help and care is needed to support a glaucoma patient. Cost for care for glaucoma patients may differ between countries. Since the costs of care differ most between the two treatment strategies, the results of our analyses showed that low-vision-related costs played an important role in the overall cost-effectiveness of treatment, while there is a considerable degree of uncertainty about the size of these costs and how they increase with progressing disease. Previously, authors investigating health economics of OHT and glaucoma treatment have not included such costs in the analysis (Stewart et al. 2008), considered only nursing home costs (Rein et al. 2009) or assumed resource use in this category only in case of blindness (Kymes et al. 2006; Burr et al. 2007; Peeters et al. 2008). In our study, we have assumed a gradual increase in low-vision-related costs with increasing loss of VF, which was based on measurements in our study in 531 patients representing all levels of OHT and POAG severity and MD values ranging from 0 to −32 dB in the better eye (Van Gestel et al. 2010b). The PSA showed that even when the low-vision-related costs were varied between a factor 0 (i.e. no costs) and 2, the dominance of direct treatment was not affected. On the same note, the EVPI analysis indicated that, despite the uncertainty about low-vision-related costs, there is no value in additional research to reduce that uncertainty in the context of the currently investigated treatment decision. Although the EVPI is expressed as a value for an individual patient to make comparisons between studies, it should also be seen in relation to the total population of OHT patients and in fact future OHT patients. This is because savings by perfect information will be gained by changing the management strategy for the total, future population.

This example illustrates how the fact that some model input is quite uncertain does not invalidate the entire model, and that it is more important to acknowledge uncertainty and assess its impact than negate the informative power of the aggregated evidence. It also demonstrates that the model input with the highest degree of uncertainty is not necessarily the one with the largest impact on the outcome and is therefore not the most likely candidate for future research. In fact, we have conducted analysis of variance with the PSA input and outcomes and found that uncertainty about the relative risk of IOP on conversion had the largest impact (see Appendix S1).

The dominance that we found for direct treatment relative to watchful waiting differs considerably from the $144,780 per QALY that has been reported previously in a US-based study (2004 € 1 ≈ $ 1.25; Kymes et al. 2006; The European Central Bank). The difference is caused by lower incremental costs (-€ 649 versus $ 7,239) and higher incremental QALY's (0.27 versus 0.05) in our study. We compared the methodology of both studies and identified several issues that might explain the differences. First, the setting of the studies affected the estimates for the cost price of medication, cataract surgery and POAG surgery, as cost prices in the United States are generally higher than those reported for European countries (Kobelt & Jönsson 1999; Oostenbrink et al. 2001; Traverso et al. 2005). Second, Kymes et al. (2006) attributed resource use associated with visual impairment only in case of blindness, not in preceding stages. These two factors are probably the main reason why treatment in the US study was associated with incremental costs rather than cost savings. Additionally, four issues may contribute to the differences in incremental effects. First, the estimated utility loss as a result of disease progression was smaller in the US study than in our study, particularly in advanced stages. Second, the horizon was much shorter in the US study. Kymes et al. (2006) do not report the actual duration of follow-up in their study, but considering the total QALY's reported (13.6) and the utility in early and moderate glaucoma (0.97 and 0.89) it is likely to be around 15 years, whereas the horizon was 26 years in our study. As the results of our study have shown, the length of the time horizon has a considerable impact on the ICER of OHT treatment. Third, the risk of conversion in the US study was distributed towards lower values than in our simulated population. The authors reported that 70% of the patients had an annual conversion risk lower than 2%, whereas this was 44% in our simulated population. Since the incremental effects of direct treatment are smaller with decreasing conversion risk (Table 5), a population with more low-risk patients will result in smaller average incremental effects of direct treatment. Finally, the future QALY gains in the US study were more heavily discounted, which reduces the net present value of future health gains (3% versus 1.5%). The combination of all factors may have resulted in the difference in outcomes of our study compared to those reported earlier. These issues do not necessarily concern ‘wrong’ choices in either of the studies but rather reflect the different decision-making contexts targeted by the two studies.

The study of Burr et al. (2012) encompasses several studies. One study was on the selection and validation of a prediction model to predict the risk of conversion to POAG in OHT patients. The second was on the validity and variability of IOP measurements by diverse instruments. The third study was on the public preferences for the type of surveillance or monitoring of OHT patients. The fourth study was on the cost-effectiveness of five monitoring strategies for OHT patients with an IOP > 21 mmHg. These five were the following: (1) treat-all OHT patients with a prostaglandin and refer if IOP lowering is <15% and measurement of IOP annually by an community optometrist; (2) surveillance for ocular hypertension (SOH) in a primary care (community) setting, monitoring is every 2 years and individuals would only be referred to secondary care if IOP was ‘off target’ or conversion to OAG being detected; (3) SOH in a hospital eye service setting; (4) monitoring according to the NICE intensive guidelines; and (5) monitoring according to NICE conservative guidelines. Treatment in monitoring strategies 2–5 was based on a calculated risk of conversion. Treatment intensity was to lower the IOP with 15% and to adjust if IOP was off target or occurrence of POAG had occurred. In the NICE strategies (4 and 5), treatment was limited until a certain age was achieved. Monitoring intervals in the NICE strategies was every 2 months to assess IOP and every 4–24 months for full assessment depending on the risk of conversion and strategy. The monitoring frequency was every 2 years in strategy 2 and 3. In the treat-all strategy 22.8% converted to POAG in 20 years. This was reduced to the lowest value achieved at 20.6%, which is a reduction of 2.2%. Burr et al. concluded that the strategy SOH in a hospital gave fewer POAG and progression, more QALY's and net benefit as compared to the treat-all strategy. However, in terms of cost-effectiveness, the gain of QALY's was not considered worth the costs.

In comparing the study of Burr et al. with ours, it is important to notice that Burr et al. compare strategies in which at least a group, those with the highest risk or all patients are being treated. The difference is further in the setting and the criteria for follow-up. Moreover, their treatments were not based on target pressures to be achieved but based on achieving a reduction of at least 15%. In our studies, two extremes are compared. ‘Treat all’ or postpone treatment in all OHT patients untilled POAG has occurred. Moreover, we set a target pressure that implies that high IOP values need to be reduced to below 21, if that could be achieved with eye drops or laser treatment. Altogether, our two strategies are two extremes in effectiveness. Moreover, our utilities were based on the HUI-3 that is a better utility scale for ophthalmology because it encompasses visual impairment in its questionnaire. It could be that this gives larger differences between stages of OHT/POAG. The study of Burr et al. gives a refinement of the monitoring strategy when at least a (large) subgroup is treated.

Another difference from the study of Burr et al. is that they included a measure for compliance. We did not take that into account since we assumed that this was an implicit part of the outcome of treatments in other studies.

The results are findings that show the best management strategy from a cost-effectiveness point of view. Patient management is, however, influenced by other factors that can be identified by ophthalmologists and patients who may have their own preferences. The results are based in average findings, but still not all patients are at risk, leaving room for individualized care.

The average benefits of direct treatment initiation in the heterogeneous population were considerable, and the best chances of optimal health outcomes in an individual patient are therefore with treatment rather than watchful waiting. The subgroup analyses further indicated that it is not necessary to consider IOP or additional risk of conversion in this treatment decision. However, there is a considerable likelihood in OHT patients that, in hindsight, treatment was not necessary. Indeed, 43% of the simulated patients in the watchful waiting strategy did not convert to POAG during their entire lifetime. The problem is that one cannot tell in advance who these patients are going to be, even if one calculates the risk of conversion with high evidence-based risk calculators. The best chance of optimal health outcomes in OHT patients is with direct treatment initiation. The implication of this finding is that the attitude towards treatment initiation in OHT could change from ‘do not treat, unless the risk of glaucoma is too high’, which is basically up to the ophthalmologist's judgment, towards ‘treat, unless the burden of treatment is too high’, which is much more up to the patient's judgment. Moreover, these issues should be discussed with the patient. The results of our study may aid the ophthalmologist and patient in deciding on the best treatment.

Two strategies were compared. One in which treatment was started directly and IOP lowered in a stepwise fashion with the first target pressure of 21 and 18 and 15 when POAG occurred and progression occurred respectively. In the other strategy the same stepwise approach was postponed and started until after the period of no treatment conversion to POAG had occurred. In essence prevention is compared to ‘cure’ when the disease has occurred. To have a fair comparison to prove whether prevention is better, the same treatment strategies were compared. However, from a clinical point of view, other comparisons are possible. One could question, for example, whether early treatment with a stepwise approach is better as compared to direct intensive treatment and direct lowering of the IOP below 15 when glaucoma has occurred. The latter gives less VF loss and lower costs as compared to a stepwise lowering approach (van Gestel, 2012). Another strategy could be to look at the age of discovering OHT. In an older person of 85, this may not be very cost-effective. Others could be to include generic preparations in the model. It has been shown that generic preparations may cause adverse events different from brand preparations (Takada et al. 2012). Moreover, generic preparations could give lower IOP values (Narayanaswamy 2007). Although the generic preparations could be cheaper, it could lead to more burden for the patient and ophthalmologist and costs in future. It would be worth to calculate its effect on costs and effects in our model. In addition to this, the results can be different in other countries when there are no costs of care for visually impaired or blind glaucoma patients.In conclusion, we found that direct pressure lowering treatment is a dominant strategy compared to watchful waiting in a heterogeneous population of OHT patients, and that the efficiency of direct treatment increases with increasing initial IOP and the presence of additional risk factors for conversion. The implementation and consequences of the results should be discussed with ophthalmologists and individual patients.

In conclusion, we found that direct pressure lowering treatment is a dominant strategy compared to watchful waiting in a heterogeneous population of ocular hypertension patients, and that the efficiency of direct treatment increases with increasing initial IOP and the presence of additional risk factors for conversion. The implementation and consequences of the results should be discussed with ophthalmologists and individual patients

Funding

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Funding
  8. References
  9. Supporting Information

ZonMW, The Netherlands Organization for Health Research and Development. Health Care Efficiency Research Program: sub-program Effects & Costs. Grant number 945-04-451. The funding organization had no role in the design or conduct of this research.

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  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Funding
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Funding
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
aos12328-sup-0001-AppendixS1.docWord document146KAppendix S1. Suplemental material on the data used and model development as well as on additional results.

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