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Mapping analysis to predict SF-6D utilities from health outcomes in people with focal epilepsy

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Abstract

Background

Focal-onset seizures (FOS) are commonly experienced by people with epilepsy and have a significant impact on quality of life (QoL). This study aimed to develop a mapping algorithm to predict SF-6D values in adults with FOS for use in economic evaluations of a new treatment, cenobamate.

Methods

An online survey, including questions on disease history, SF-36, and an epilepsy-specific measure (QOLIE-31-P) was administered to people with FOS in the UK, France, Italy, Germany, and Spain. A range of regression models were fitted to SF-6D scores including direct and response mapping approaches.

Results

361 individuals were included in the analysis. In the previous 28 days, the mean number of FOS experienced was 3, (range 0–43) and the mean longest period of consecutive days without experiencing a seizure was 14 days (range 1–28 days or more). Mean responses on all SF-36 dimensions were lower than general population norms. Mean SF-6D and QOLIE-31-P scores were 0.584 and 45.72, respectively. The best performing model was the ordinary least squares (OLS), with root mean squared error and mean absolute error values of 0.0977 and 0.0742, respectively. Explanatory variables which best predicted SF-6D included seizure frequency, severity, freedom, and age.

Conclusion

People with uncontrolled FOS have poor QoL. The mapping algorithm enables the prediction of SF-6D values from clinical outcomes in people with FOS. It can be applied to outcome data from clinical trials to facilitate cost-utility analysis.

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References

  1. World Health Organization. Epilepsy: a public health imperative: summary. World Health Organization, (2019). https://apps.who.int/iris/handle/10665/325440

  2. Scharfman, H.T.: The neurobiology of epilepsy. Curr. Neurol. Neurosci. Rep. 7(4), 348–354 (2007). https://doi.org/10.1007/s11910-007-0053-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fisher, R.S.: The new classification of seizures by the international league against epilepsy. Curr. Neurol. Neurosci. Rep. 17(6), 48 (2017). https://doi.org/10.1007/s11910-017-0758-6

    Article  PubMed  Google Scholar 

  4. Birbeck, G.L., et al.: Seizure reduction and quality of life improvements in people with epilepsy. Epilepsia 43(5), 535–538 (2002)

    Article  PubMed  Google Scholar 

  5. Choi, H., Hamberger, M.J., Munger, C.H., et al.: Seizure frequency and patient-centered outcome assessment in epilepsy. Epilepsia 55(8), 1205–1212 (2014). https://doi.org/10.1111/epi.12672

    Article  PubMed  Google Scholar 

  6. Luoni, C., et al.: Determinants of health-related quality of life in pharmacoresistant epilepsy: results from a large multicenter study of consecutively enrolled patients using validated quantitative assessments. Epilepsia 52(12), 2181–2191 (2011). https://doi.org/10.1111/j.1528-1167.2011.03325.x

    Article  PubMed  Google Scholar 

  7. Mukuria, C., Young, T., Keetharuth, A., et al.: Sensitivity and responsiveness of the EQ-5D-3L in patients with uncontrolled focal seizures: an analysis of phase III trials of adjunctive brivaracetam. Qual. Life Res. 26(3), 749–759 (2017). https://doi.org/10.1007/s11136-016-1483-3

    Article  PubMed  Google Scholar 

  8. Velez, F.F., Bond, T.C., Anastassopoulos, K.P., et al.: Impact of seizure frequency reduction on health-related quality of life among clinical trial subjects with refractory partial-onset seizures: a pooled analysis of phase III clinical trials of eslicarbazepineacetate. Epilepsy Behav. 68, 203–207 (2017). https://doi.org/10.1016/j.yebeh.2016.10.027

    Article  PubMed  Google Scholar 

  9. Cramer, J.A., Perrine, K., Devinsky, O., et al.: Development and cross-cultural translations of a 31-item quality of life in epilepsy inventory. Epilepsia 39(1), 81–88 (1998). https://doi.org/10.1111/j.1528-1157.1998.tb01278.x

    Article  CAS  PubMed  Google Scholar 

  10. Viteva, E.L.: Seizure frequency and severity: How really important are they for the quality of life of patients with refractory epilepsy. Ann. Indian Acad. Neurol. 17(1), 35–42 (2014). https://doi.org/10.4103/0972-2327.128544

    Article  PubMed  PubMed Central  Google Scholar 

  11. Guyatt, G.H., Feeny, D.H., Patrick, D.L.: Measuring health-related quality of life. Ann. Intern. Med. 118(8), 622–629 (1993). https://doi.org/10.7326/0003-4819-118-8-199304150-00009

    Article  CAS  PubMed  Google Scholar 

  12. Longworth, L., Yang, Y., Young, T., Mulhern, B., Hernandez, A.M., Mukuria, C., Rowen, D., Tosh, J., Tsuchiya, A., Evans, P., Davianee, K.A., Brazier, J.: Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: a systematic review, statistical modelling and survey. Health Technol. Access 18(9), 1–224 (2014). https://doi.org/10.3310/hta18090

    Article  Google Scholar 

  13. Longworth, L., Rowen, D.: Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value Health 16(1), 202–210 (2013). https://doi.org/10.1016/j.jval.2012.10.010

    Article  PubMed  Google Scholar 

  14. Maresh, M.J., Holmes, V.A., Patterson, C.C., Young, I.S., Pearson, D.W.M., Walker, J.D., McCance, D.R.: Glycemic targets in the second and third trimester of pregnancy for women with type 1 diabetes. Diabetes Care 38(1), 34–42 (2015). https://doi.org/10.2337/dc14-1755

    Article  PubMed  Google Scholar 

  15. Krauss, G.L., Klein, P., Brandt, C., Lee, S.K., Milanov, I., Milovanovic, M., Kamin, M., Steinhoff, B.J.: Safety and efficacy of adjunctive cenobamate (YKP3089) in patients with uncontrolled focal seizures: a multicentre, double-blind, randomised, placebo-controlled, dose-response trial. Lancet Neurol. 19(1), 38–48 (2020). https://doi.org/10.1016/s1474-4422(19)30399-0

    Article  CAS  PubMed  Google Scholar 

  16. Chung, S.S., French, J.A., Kowalski, J., Krauss, G.L., Lee, S.K., Maciejowski, M., Rosenfeld, W.E., Sperling, M.R., Mizne, S., Kamin, M.: Randomized phase 2 study of adjunctive cenobamate in patients with uncontrolled focal seizures. Neurology 94(22), e2311–e2322 (2020). https://doi.org/10.1212/WNL.0000000000009530

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Brazier, J., Roberts, J., Deverill, M.: The estimation of a preference-based measure of health from the SF-36. J. Health Econ. 21(2), 271–292 (2002). https://doi.org/10.1016/s0167-6296(01)00130-8

    Article  PubMed  Google Scholar 

  18. Optum PROCoRE 1.5 Smart Measurement® System December 2019. Optum, Inc 1301 Atwood Avenue, Suite 311 N Johnston, R.I. 02919, U.S.A (2018)

  19. Brazier, J.E., Roberts, J.: The estimation of a preference-based measure of health from the SF-12. Med. Care (2004). https://doi.org/10.1097/01.mlr.0000135827.18610.0d

    Article  PubMed  Google Scholar 

  20. Brazier, J.E., Hanmer, J., Rowen, D.: Revised SF-6D scoring programmes: a summary of improvements. PRO Newslett. 40, 14–15 (2008)

    Google Scholar 

  21. Guide to the methods of technology appraisal. National Institute for Health and Clinical Excellence (NICE) London, UK. (2013). http://www.nice.org.uk/process/pmg9. Accessed 16th Mar 2021

  22. Sanghera, S., Coast, J.: Measuring quality-adjusted life-years when health fluctuates. Value Health 23(3), 343–350 (2020). https://doi.org/10.1016/j.jval.2019.09.2753

    Article  PubMed  Google Scholar 

  23. Wijnen, B.F.M., Mosweu, I., Majoie, M.H.J.M., Ridsdale, L., de Kinderen, R.J.A., Evers, S.M.A.A., McCrone, P.: A comparison of the responsiveness of EQ-5D-5L and the QOLIE-31P and mapping of QOLIE-31P to EQ-5D-5L in epilepsy. Eur. J. Health Econ. 19(6), 861–870 (2018). https://doi.org/10.1007/s10198-017-0928-0

    Article  PubMed  Google Scholar 

  24. Longworth, L., et al.: Estimating utility data from clinical indicators for patients with stable angina. Eur. J. Health Econ. 6(4), 347–353 (2005)

    Article  PubMed  Google Scholar 

  25. Poole, C.D., et al.: A comparison of physician-rated disease severity and patient reported outcomes in mild to moderately active ulcerative colitis. J. Crohns Colitis 4(3), 275–282 (2010)

    Article  PubMed  Google Scholar 

  26. Abdin, E., et al.: Mapping the positive and negative syndrome scale scores to EQ-5D-5L and SF-6D utility scores in patients with schizophrenia. Qual. Life Res. 28(1), 177–186 (2019)

    Article  PubMed  Google Scholar 

  27. Ghatnekar, O., Eriksson, M., Glader, E.-L.: Mapping health outcome measures from a stroke registry to EQ-5D weights. Health Qual. Life Outcomes 11(1), 34 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  28. Chavez, L.J., et al.: Preference weights for the spectrum of alcohol use in the U.S. population. Drug Alcohol. Depend. 161, 206–213 (2016)

    Article  PubMed  Google Scholar 

  29. Cramer, J.A., Rajagopalan, K., Anastassopoulos, K.P., Blum, D., et al.: Health-related quality of life in patients treated with eslicarbazepine acetate monotherapy: pooled analysis from two registered clinical trials. Epilepsy Behav. 92, 31–35 (2019). https://doi.org/10.1016/j.yebeh.2018.12.003

    Article  PubMed  Google Scholar 

  30. Deleo, F., et al.: The impact of perampanel treatment on quality of life and psychiatric symptoms in patients with drug-resistant focal epilepsy: an observational study in Italy. Epilepsy Behav. 99, 106391 (2019). https://doi.org/10.1016/j.yebeh.2019.06.034

    Article  PubMed  Google Scholar 

  31. Nakhutina, L., Kunnakkat, S.D., Coleman, M., Lushbough, C., Arnedo, V., Soni, N., Grant, A.C.: Effects of adjunctive lacosamide on mood and quality of life in patients with epilepsy. Epilepsy Behav. 73, 90–94 (2017). https://doi.org/10.1016/j.yebeh.2017.05.001

    Article  PubMed  Google Scholar 

  32. Sancho, J., et al.: Changes in seizure severity and quality of life in patients with refractory partial epilepsy. Epilepsy Behav. 19(3), 409–413 (2010). https://doi.org/10.1016/j.yebeh.2010.08.011

    Article  PubMed  Google Scholar 

  33. Gray, L.A., Alava, M.H.: A command for fitting mixture regression models for bounded dependent variables using the beta distribution. Stand. Genomic Sci. 18(1), 51–75 (2018)

    Google Scholar 

  34. Hernández-Alava, M., Wailoo, A.: ALDVMM: a command for fitting adjusted limited dependent variable mixture models to EQ-5D. Stand. Genomic Sci. 15(3), 737–750 (2015). https://doi.org/10.1177/1536867X1501500307

    Article  Google Scholar 

  35. Abellán Perpiñán, J.M., Sánchez Martínez, F.I., Martínez Pérez, J.E., Méndez, I.: Lowering the “floor” of the Sf-6d scoring algorithm using a lottery equivalent method. Health Econ. 21(11), 1271–1285 (2012)

    Article  PubMed  Google Scholar 

  36. Maruish, M.E. (ed.) User’s manual for the SF-36v2 Health Survey (3rd edn). Lincoln, RI: QualityMetric Incorporated

  37. Browne, C., Brazier, J., Carlton, J., Alavi, Y., Jofre-Bonet, M.: Estimating quality-adjusted life years from patient-reported visual functioning. Eye (Lond) 26(10), 1295–1301 (2012). https://doi.org/10.1038/eye.2012.137

    Article  CAS  PubMed  Google Scholar 

  38. Collado-Mateo, D., Chen, G., Garcia-Gordillo, M.A., et al.: Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments. Health Qual Life Outcomes 15(1), 114 (2017). https://doi.org/10.1186/s12955-017-0690-0

    Article  PubMed  PubMed Central  Google Scholar 

  39. Siani, C., de Peretti, C., Millier, A., Boyer, L., Toumi, M.: Predictive models to estimate utility from clinical questionnaires in schizophrenia: findings from EuroSC. Qual Life Res 25, 925–934 (2016). https://doi.org/10.1007/s11136-015-1120-6

    Article  PubMed  Google Scholar 

  40. Langfitt, J.T., et al.: Validity and responsiveness of generic preference-based HRQOL instruments in chronic epilepsy. Qual Life Res 15(5), 899–914 (2006)

    Article  CAS  PubMed  Google Scholar 

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Funding

PHMR received financial support from Arvelle Therapeutics GmbH for conducting this study, including the development, administration and data collection of the online survey, development of the mapping algorithms and preparation of the manuscript.

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Authors and Affiliations

  1. PHMR Limited, Berkeley Works, Berkley Grove, London, NW1 8XY, UK

    India Flint, Aurelie Meunier & Louise Longworth

  2. Arvelle Therapeutics GmbH, Zug, Switzerland

    Jasmina Medjedovic, Ewa Drogon O’Flaherty, Elena Alvarez-Baron, Karthinathan Thangavelu & Natasa Savic

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  1. India Flint
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Contributions

IF: Led the analysis and drafting of the manuscript, contributed to the study design and interpretation. JM: Contributed to the study design, analysis, interpretation and drafting of the manuscript. EDO’F: Contributed to the study design, analysis, interpretation and drafting of the manuscript. EAB: Contributed to the study design, analysis, interpretation and drafting of the manuscript. KT: Contributed to the study design, analysis, interpretation and drafting of the manuscript. NS: Contributed to the study design, analysis, interpretation and drafting of the manuscript. AM: Contributed to the analysis, interpretation and drafting of the manuscript. LL: Led the study design, contributed to the analysis, interpretation and drafting of the manuscript. Oversight of the project.

Corresponding author

Correspondence to India Flint.

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Conflict of interest

I. Flint, A. Meunier, and Dr L. Longworth are employees at PHMR. PHMR received financial support from Arvelle Therapeutics GmbH for the work, including the development, administration and data collection of the online survey, development of the mapping algorithms and preparation of the manuscript. J. Medjedovic; E. Drogon O’Flaherty; N. Savic and E. Alvarez-Baron are full-time employees of Arvelle Therapeutics GmbH who funded the work. K. Thangavelu of MeDaStats LLC is a consultant statistician to Arvelle Therapeutics GmbH.

Ethical approval

The study protocol and survey materials were approved by an independent ethical reviewer prior to the start of the interviews. The research ethics expert was working under the auspices of the Association of Research Managers and Administrators (https://arma.ac.uk/).

Consent to participate

Before the respondent started the online survey, an information sheet was provided which included a breakdown of each study component and explained why the individual’s input was important for the study’s success; how the data would be managed; and their data protection rights. Following this, participants were issued an electronic informed consent form which they had to read and agree to before proceeding to the survey. Individuals who did not provide consent at this stage were unable to proceed to the survey.

Consent for publication

Before the respondent started the survey, an information sheet was provided which informed them of the potential use of anonymised data for publication in medical journals, conference presentations or which would be provided to health care decision makers. All participants provided informed consent.

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Flint, I., Medjedovic, J., Drogon O’Flaherty, E. et al. Mapping analysis to predict SF-6D utilities from health outcomes in people with focal epilepsy. Eur J Health Econ 24, 1061–1072 (2023). https://doi.org/10.1007/s10198-022-01519-w

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  • DOI: https://doi.org/10.1007/s10198-022-01519-w

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