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Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis

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Abstract

Background

Clinical trials and observational studies lacking measures of health-related quality of life (QoL) are often inapplicable when conducting cost-effectiveness analyses using quality-adjusted life-years (QALYs). The only solution is to map QoL ex post from additionally collected clinical outcomes and generic QoL instruments. Nonetheless, mapping studies are absent in psoriatic arthritis (PsA).

Methods

In this 2-year, prospective, multicentre, non-interventional study of PsA patients, EQ-5D and key clinical parameters such as Disease Activity in PsA (DAPsA), clinical DAPsA (cDAPsA; DAPsA without C-reactive protein [CRP]), and Health Assessment Questionnaire disability index (HAQ) were collected. We employed a linear mixed-effect regression model (ME) of the longitudinal dataset to explore the best predictors of QoL.

Results

A total of 228 patients were followed over 873 appointments/observations. DAPsA, cDAPsA and HAQ were stable and highly significant predictors of EQ-5D utilities in both cross-sectional and longitudinal analyses. The best prediction was provided using a linear ME with HAQ and cDAPsA or DAPsA. A HAQ increase of 1 point represented a decrease in EQ-5D by −0.204 or −0.203 (p < 0.0001); a one-point increase in cDAPsA or DAPsA dropped EQ-5D equally by −0.005 (p < 0.0001). The ME revealed steeper and more accurate association compared with cross-sectional regressions or non-linear models/transformations.

Conclusions

This is the first mapping study conducted in PsA and we hope that our study will encourage further mapping studies in PsA. The results showed that in cases where CRP is absent, cDAPsA provides similar results to DAPsA in predicting QoL.

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Notes

  1. 1.

    For example, patients enrolled into the study in July 2015 could not have completed the following visit as the planned 6-monthly visit falls after the data-lock point.

  2. 2.

    In our study, DAPsA was calculated using the definition valid at the time of the patient enrolment (2012–2015). These were further refined in 2015 by Schoels et al. [28], who proposed changing the global health assessment by physician (GHA) via visual analogue scale (VAS) to patient’s assessment of pain on VAS. Nonetheless, there is some evidence showing that physician’s GHA-VAS might be more precise than pain assessment due to lower variability and higher objectivity of the physician’s point of view [29].

  3. 3.

    Working productivity, and thus WPAI, was proportionally decreased according to the stage of disability since disability in the Czech Republic is defined in stages: 1st stage is defined by law as a decrease in working productivity by 35–49%, 2nd stage by 50–69%, and 3rd stage by 70–100%.

  4. 4.

    A majority of patients present low values of BSA. At baseline, 62% of patients had BSA ≤ 1 and 76% had BSA ≤ 3. Extrapolation beyond these values cannot be considered reliable due to very low number of observations. Being the second reason for omitting BSA from the ultimate regressions, high skewness is an inherent character of this parameter in any PsA population.

  5. 5.

    Interestingly, the most recent cost-effectiveness analyses (CEA) of new PsA treatments (e.g. see ampremilast or ustekinumab NICE guidance [43, 44]) use the mapping algorithm by Rodgers et al. despite its drawbacks. Another solution is to present specific utility mapping using data from in-house RCTs; however, the results are usually not further available for other researchers (sometimes are even of commercial confidence) and/or the methods employed are not thoroughly discussed (e.g. see certolizumab pegol and secukinumab NICE guidance [45]).

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Acknowledgements

L. Sedova, J. Stolfa, M. Urbanova and K. Pavelka were supported by a general grant from the Czech Ministry of Health IGA MZ CZ: No. 000 000 23, 728. K. Pavelka received honoraria for lectures and consultations from AbbVie, BMS, UCB, MSD, Amgen, Egis, Pfizer, and Roche. The authors acknowledge careful proofreading and insightful suggestions from Klara Lamblova MSc. (iHETA) and excellent English proofreading by Thomas O. Secrest.

Disclosure

Tomas Dolezal is owner of iHETA and also consultancy company Value Outcomes. Tomas Mlcoch, Jan Tuzil, Jitka Jircikova and Tereza Hrnciarova work for both iHETA and Value Outcomes. TD and TM work mainly as health economists. Their goal is to improve the healthcare system in the Czech Republic. JJ and TH design clinical trials and work as statisticians and data analysts. JT specialises in medical data interpretation in clinical trials. Liliana Sedova, Jiri Stolfa, David Suchy and Andrea Smrzova work as rheumatologists in respective specialised rheumatology clinics in which highly specialised medical technologies are prescribed (such as new biological treatment). Karel Pavelka is a director of the Institute of Rheumatology. None of these activities influenced the content or processing of this manuscript.

Author information

TD, LS, JS and KP designed the research. LS, JS, MU, DS, AS and KP collected the data in clinical practice. TM, JT and TD wrote the manuscript and TM had primary responsibility for final content. TM, JT, JJ and TH prepared and analysed the data. JT performed statistical analysis. LS, JS and TH conducted repeated and throughout revisions of the manuscript.

Correspondence to Tomas Mlcoch.

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Mlcoch, T., Tuzil, J., Sedova, L. et al. Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis. Patient 11, 329–340 (2018). https://doi.org/10.1007/s40271-017-0285-1

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