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It is difficult to overestimate the importance of the UK National Health Service (NHS) policy to structurally assess the cost effectiveness of novel treatments. This policy serves as an example for policy makers in many developed countries, and the outcomes of the analyses are made available to fellow researchers in the field. The recent publication by Salmon et al1 regarding the cost effectiveness of crosslinking for progressive keratoconus is an excellent example of this. The authors concluded that crosslinking is likely to be cost effective, with an incremental cost of £3174 per quality-adjusted life year (QALY), supporting the NHS' decision to reimburse this treatment.
We would like to address the methods used in this study, specifically the authors' calculation of QALYs in keratoconus. QALYs represent the value of the impact of disease on quality of life measured over a lifetime. The concept is based on the measurement of utilities. A utility is represented on a scale anchored at 0 (representing death) and 1 (representing full health) and can be assessed using specific questionnaires (eg, the Euroqol EQ-5D (Euroqol group http://www.euroqol.org/about-eq-5d.html)) or calculated from patient-reported health surveys (eg, SF-6D2 derived from Short From 36 Health (SF-36) survey questionnaires3). QALYs and utilities are the preferred outcome measures used when performing a cost effectiveness analysis. The authors state that direct measures of utilities in keratoconus are not available and therefore estimated utilities based on expected visual acuity (VA) in various stages of keratoconus, leading to decreased utilities in advanced keratoconus.
However, the Collaborative Longitudinal Evaluation of Keratoconus (CLEK) study measured SF-36 in more than 1200 keratoconus patients, including appropriate descriptions of the patients' VA, keratometry, and subsequent staging using the Amsler–Krumeich classification.4 Using the CLEK database, we classified all of the included subjects according to their keratometry readings, and we linked these results to SF-6D-derived utilities, following the method developed by Brazier et al.2 To our surprise, we found virtually no difference in utilities among the various disease stages in keratoconus; strikingly, the utilities in patients with bilateral stage I keratoconus were identical to the utilities in patients with bilateral stage IV keratoconus (Table 1). Similar results were obtained when the results were stratified based on age and gender. Thus, if perceived quality of life does not deteriorate as the disease progresses, hardly any therapy will be cost effective.
We hypothesize that either SF-36-derived utilities lack the sensitivity to detect the apparent differences per disease stage that subjects adjust to their disease stage over time, or that a keratometry-based classification is not appropriate. Keratometry is not a clinical endpoint, and its relationship with VA is multifactorial and complex. Both VA and the patient's dependence upon visual aids are arguably more relevant for determining quality of life in keratoconus patients. Although vision-related quality of life is related to VA in the better eye,5 we investigated the correlation between (LogMAR) VA in the better eye and utilities, and found a significant relation (P<0.001, Pearson's r=−0.113). The utilities obtained for various VA groups are summarized in Table 2. The largest decrease in utilities occurs when LogMAR VA in the better eye is 0.6 or larger (Snellen equivalent <0.25), particularly in patients who underwent either unilateral or bilateral corneal transplantation.
In conclusion, quality of life as measured by SF-6D in keratoconus patients is related to VA in the better eye, whereas no correlation could be identified between quality of life and keratometry values or disease stage. We postulate that VA may be a better intermediate outcome to base QALYs on than either keratometry or disease stage.
We gratefully acknowledge access to the Collaborative Longitudinal Evaluation in Keratoconus database. The CLEK study was supported by awards from the National Eye Institute, the National Center on Minority Health and Health Disparities, National Institutes of Health (grants EY10419, EY10069, EY12656, EY02687, and EY10077), and unrestricted grants from Research to Prevent Blindness, Inc., NY, USA.
DAG and RW are supported by unrestricted grants from the Dr FP Fischer stichting, facilitated by stichting Vrienden van het UMC. The remaining authors declare no conflict of interest.