Advertisement

Outcome Measures and Quality of Life in Mitochondrial Diseases

  • S. Koene
  • C. Jimenez-Moreno
  • G. S. GormanEmail author
Chapter

Abstract

Mitochondrial diseases are a group of rare neurometabolic disorders that are extremely complex, not least due to their clinical and genetic heterogeneity. The management of these patients remains difficult, because there are currently no interventions that provide a realistic prospect of cure. This represents a compelling unmet need. And while this is recognised as a significant challenge, there remains another significant barrier to robustly assess and measure disease status and progression for each patient and to measure potential effects of any potential intervention. The tools and instruments used to quantify clinical aspects of disease and its impact on someone’s life are referred to as outcome measures. Recent efforts have been made to identify the most appropriate outcome measures that can overcome the inherent challenges of mitochondrial disease characteristics, such as clinical heterogeneity, unpredictability of disease progression rate and the spectrum of ages that may be affected. Still, there is a need for further research in the field. The different paradigms of these outcomes may vary in their nature and purpose, but all should agree in the fact that they reflect clinically relevant aspects of the health and quality of life of those affected by the disease. Certain outcomes may be measured by clinicians or researchers, while others may be scored directly by the patients or their proxies. Other outcomes may assess walking ability, while others may assess perceived fatigue or visual acuity; yet any of these endpoints may be equally valid. Indeed, it is most likely that a concise battery of outcomes would most likely capture the most clinically relevant and patient-centric measures. When designing a clinical trial in patients with mitochondrial diseases, it is imperative that all stakeholders involved should understand the relevance of selecting a valid outcome measure that promises to measure the characteristic(s) of the disease in which the intervention is expected to reflect its benefit. This chapter endeavours to review current constructs around the assessment of outcome measures and quality of life that have been used in patients with mitochondrial disease to date and to discuss their potential benefits and limitations.

Keywords

Mitochondrial disease Outcome measure Endpoint Clinical trial Clinical outcome assessment Quality of life Patient-reported outcome Patient preferences Patient-centred outcomes 

References

  1. 1.
    Koopman WJ, et al. Mitochondrial disorders in children: toward development of small-molecule treatment strategies. EMBO Mol Med. 2016;8(4):311–27.PubMedPubMedCentralCrossRefGoogle Scholar
  2. 2.
    Gorman GS, et al. Mitochondrial diseases. Nat Rev Dis Primers. 2016;2:16080.PubMedCrossRefGoogle Scholar
  3. 3.
    Pfeffer G, et al. New treatments for mitochondrial disease-no time to drop our standards. Nat Rev Neurol. 2013;9(8):474–81.PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Mancuso M, et al. International Workshop:: Outcome measures and clinical trial readiness in primary mitochondrial myopathies in children and adults. Consensus recommendations. 16–18 November 2016, Rome, Italy. Neuromuscul Disord. 2017;27(12):1126–37.PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Koene S, et al. Outcome measures for children with mitochondrial disease: consensus recommendations for future studies from a Delphi-based international workshop. J Inherit Metab Dis. 2018;41(6):1267–73.  https://doi.org/10.1007/s10545-018-0229-5.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Brunner HI, Ravelli A. Developing outcome measures for paediatric rheumatic diseases. Best Pract Res Clin Rheumatol. 2009;23(5):609–24.PubMedCrossRefGoogle Scholar
  7. 7.
    Adminstration, F.F.a.D. Guidance for Industry: Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. U.S. Department of Health and Human Services. 2009.Google Scholar
  8. 8.
    Tugwell P, Boers M. OMERACT conference on outcome measures in rheumatoid arthritis clinical trials: introduction. J Rheumatol. 1993;20(3):528–30.PubMedGoogle Scholar
  9. 9.
    Organisation, W.W.H. International Classification of Functioning, Disability and Health. World Health Organization Press. 2001.Google Scholar
  10. 10.
    Ronen GM, Fayed N, Rosenbaum PL. Outcomes in pediatric neurology: a review of conceptual issues and recommendations. The 2010 Ronnie Mac Keith Lecture. Dev Med Child Neurol. 2011;53(4):305–12.PubMedCrossRefGoogle Scholar
  11. 11.
    Merlini L, et al. Motor function-muscle strength relationship in spinal muscular atrophy. Muscle Nerve. 2004;29(4):548–52.PubMedCrossRefGoogle Scholar
  12. 12.
    Kierkegaard M, et al. Perceived functioning and disability in adults with myotonic dystrophy type 1: a survey according to the International Classification of Functioning, Disability and Health. J Rehabil Med. 2009;41(7):512–20.PubMedCrossRefGoogle Scholar
  13. 13.
    Mokkink LB, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol. 2010;63(7):737–45.PubMedCrossRefGoogle Scholar
  14. 14.
    Schiariti V, et al. Comparing contents of outcome measures in cerebral palsy using the international classification of functioning (ICF-CY): a systematic review. Eur J Paediatr Neurol. 2014;18(1):1–12.PubMedCrossRefGoogle Scholar
  15. 15.
    Mercuri E, Mazzone E. Choosing the right clinical outcome measure: from the patient to the statistician and back. Neuromuscul Disord. 2011;21(1):16–9.PubMedCrossRefGoogle Scholar
  16. 16.
    Etzioni R, Gulati R, Lin DW. Measures of survival benefit in cancer drug development and their limitations. Urol Oncol. 2015;33(3):122–7.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Fernandez-Martos C. Clinically relevant study end points in rectal cancer. Eur J Cancer. 2012;48:S1.CrossRefGoogle Scholar
  18. 18.
    Preiss D, Sattar N, McMurray JJ. A systematic review of event rates in clinical trials in diabetes mellitus: the importance of quantifying baseline cardiovascular disease history and proteinuria and implications for clinical trial design. Am Heart J. 2011;161(1):210–219.e1.PubMedCrossRefGoogle Scholar
  19. 19.
    Lee SK, et al. Initial experiences with proton MR spectroscopy in treatment monitoring of mitochondrial encephalopathy. Yonsei Med J. 2010;51(5):672–5.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Koene S, et al. Serum FGF21 levels in adult m.3243A>G carriers: clinical implications. Neurology. 2014;83(2):125–33.PubMedCrossRefGoogle Scholar
  21. 21.
    Koene S, et al. Serum GDF15 levels correlate to mitochondrial disease severity and myocardial strain, but not to disease progression in adult m.3243A>G carriers. JIMD Rep. 2015;24:69–81.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Koene S, et al. Natural disease course and genotype-phenotype correlations in complex I deficiency caused by nuclear gene defects: what we learned from 130 cases. J Inherit Metab Dis. 2012;35(5):737–47.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Koene S, et al. Is 2D speckle tracking echocardiography useful for detecting and monitoring myocardial dysfunction in adult m.3243A>G carriers? - a retrospective pilot study. J Inherit Metab Dis. 2017;40(2):247–59.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Augustine EF, Adams HR, Mink JW. Clinical trials in rare disease: challenges and opportunities. J Child Neurol. 2013;28(9):1142–50.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Kang PB. Beyond the Gowers sign: measuring outcomes in Duchenne muscular dystrophy. Muscle Nerve. 2013;48(3):315–7.PubMedCrossRefGoogle Scholar
  26. 26.
    Friedman LS, et al. Measuring the rate of progression in Friedreich ataxia: implications for clinical trial design. Mov Disord. 2010;25(4):426–32.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Klopstock T, et al. A randomized placebo-controlled trial of idebenone in Leber’s hereditary optic neuropathy. Brain. 2011;134(Pt 9):2677–86.PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Schaefer AM, et al. Mitochondrial disease in adults: a scale to monitor progression and treatment. Neurology. 2006;66(12):1932–4.PubMedCrossRefGoogle Scholar
  29. 29.
    Phoenix C, et al. A scale to monitor progression and treatment of mitochondrial disease in children. Neuromuscul Disord. 2006;16(12):814–20.PubMedCrossRefGoogle Scholar
  30. 30.
    Campolina-Sampaio GP, et al. The Newcastle pediatric mitochondrial disease scale: translation and cultural adaptation for use in Brazil. Arq Neuropsiquiatr. 2016;74(11):909–13.PubMedCrossRefGoogle Scholar
  31. 31.
    Enns GM, et al. Initial experience in the treatment of inherited mitochondrial disease with EPI-743. Mol Genet Metab. 2012;105(1):91–102.PubMedCrossRefGoogle Scholar
  32. 32.
    Koene S, et al. International paediatric mitochondrial disease scale. J Inherit Metab Dis. 2016;39(5):705–12.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Busner J, Targum SD. The clinical global impressions scale: applying a research tool in clinical practice. Psychiatry (Edgmont). 2007;4(7):28–37.Google Scholar
  34. 34.
    Bonnemann CG, et al. 173rd ENMC International Workshop: congenital muscular dystrophy outcome measures 5-7 March 2010, Naarden, The Netherlands. Neuromuscul Disord. 2011;21(7):513–22.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Patrick DL, Deyo RA. Generic and disease-specific measures in assessing health status and quality of life. Med Care. 1989;27(3 Suppl):S217–32.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Houwen-van Opstal SL, et al. Health-related quality of life and its relation to disease severity in boys with Duchenne muscular dystrophy: satisfied boys, worrying parents—a case-control study. J Child Neurol. 2014;29(11):1486–95.PubMedCrossRefGoogle Scholar
  37. 37.
    Juniper EF, et al. Clinically important improvements in asthma-specific quality of life, but no difference in conventional clinical indexes in patients changed from conventional beclomethasone dipropionate to approximately half the dose of extrafine beclomethasone dipropionate. Chest. 2002;121(6):1824–32.PubMedCrossRefGoogle Scholar
  38. 38.
    Davis SE, et al. The PedsQL in pediatric patients with Duchenne muscular dystrophy: feasibility, reliability, and validity of the pediatric quality of life inventory neuromuscular module and generic core scales. J Clin Neuromuscul Dis. 2010;11(3):97–109.PubMedCrossRefGoogle Scholar
  39. 39.
    Hahn EA, et al. Precision of health-related quality-of-life data compared with other clinical measures. Mayo Clin Proc. 2007;82(10):1244–54.PubMedCrossRefGoogle Scholar
  40. 40.
    Elson JL, et al. Initial development and validation of a mitochondrial disease quality of life scale. Neuromuscul Disord. 2013;23(4):324–9.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Barczak P, et al. Patterns of psychiatric morbidity in a genitourinary clinic - a validation of the hospital anxiety depression scale (HAD). Br J Psychiatry. 1988;152:698–700.PubMedCrossRefGoogle Scholar
  42. 42.
    Emslie GJ, et al. Depressive symptoms by self-report in adolescence: phase I of the development of a questionnaire for depression by self-report. J Child Neurol. 1990;5(2):114–21.PubMedCrossRefGoogle Scholar
  43. 43.
    Beck AT, et al. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4(6):561–71.PubMedCrossRefGoogle Scholar
  44. 44.
    Fisk JD, et al. Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. Clin Infect Dis. 1994;18(Suppl 1):S79–83.PubMedCrossRefGoogle Scholar
  45. 45.
    Krupp LB, et al. The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol. 1989;46(10):1121–3.PubMedCrossRefGoogle Scholar
  46. 46.
    Vercoulen JH, et al. Dimensional assessment of chronic fatigue syndrome. J Psychosom Res. 1994;38(5):383–92.PubMedCrossRefGoogle Scholar
  47. 47.
    Borg G. Psychophysical scaling with applications in physical work and the perception of exertion. Scand J Work Environ Health. 1990;16(Suppl 1):55–8.PubMedCrossRefGoogle Scholar
  48. 48.
    Janssen MCH, et al. The KHENERGY study: safety and efficacy of KH176 in mitochondrial m.3243A>G spectrum disorders. Clin Pharmacol Ther. 2019;105(1):101–11.PubMedCrossRefGoogle Scholar
  49. 49.
    Hagstromer M, Oja P, Sjostrom M. The international physical activity questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9(6):755–62.PubMedCrossRefGoogle Scholar
  50. 50.
    Cella D, et al. The patient-reported outcomes measurement information system (PROMIS): progress of an NIH roadmap cooperative group during its first two years. Med Care. 2007;45(5 Suppl 1):S3–S11.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Benjamin K, et al. Patient-reported outcome and observer-reported outcome assessment in rare disease clinical trials: an ISPOR COA emerging good practices task force report. Value Health. 2017;20(7):838–55.PubMedCrossRefGoogle Scholar
  52. 52.
    International Rare Diseases Research Consortium (IRDiRC). Patient-Centered Outcome Measures Initiatives in the Field of Rare Diseases. 2016.Google Scholar
  53. 53.
    Holsbeeke L, et al. Capacity, capability, and performance: different constructs or three of a kind? Arch Phys Med Rehabil. 2009;90(5):849–55.PubMedCrossRefGoogle Scholar
  54. 54.
    Beenakker EA, et al. Functional ability and muscle force in healthy children and ambulant Duchenne muscular dystrophy patients. Eur J Paediatr Neurol. 2005;9(6):387–93.PubMedCrossRefGoogle Scholar
  55. 55.
    Parreira SL, et al. Comparison of motor strength and function in patients with Duchenne muscular dystrophy with or without steroid therapy. Arq Neuropsiquiatr. 2010;68(5):683–8.PubMedCrossRefGoogle Scholar
  56. 56.
    Liu Y, et al. [Heterogeneous phenotypes, genotypes, treatment and prevention of 1 003 patients with methylmalonic acidemia in the mainland of China]. Zhonghua Er Ke Za Zhi. 2018;56(6):414-420.Google Scholar
  57. 57.
    Maalej M, et al. Clinical, molecular, and computational analysis in two cases with mitochondrial encephalomyopathy associated with SUCLG1 mutation in a consanguineous family. Biochem Biophys Res Commun. 2018;495(2):1730–7.PubMedCrossRefGoogle Scholar
  58. 58.
    El-Hattab AW, Scaglia F. SUCLA2-Related Mitochondrial DNA Depletion Syndrome, Encephalomyopathic Form with Methylmalonic Aciduria. In: Adam MP, et al., editors. GeneReviews((R)). Seattle, WA: University of Washington, Seattle; 1993.Google Scholar
  59. 59.
    Carrozzo R, et al. Succinate-CoA ligase deficiency due to mutations in SUCLA2 and SUCLG1: phenotype and genotype correlations in 71 patients. J Inherit Metab Dis. 2016;39(2):243–52.PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Carrozzo R, et al. SUCLA2 mutations are associated with mild methylmalonic aciduria, Leigh-like encephalomyopathy, dystonia and deafness. Brain. 2007;130(Pt 3):862–74.PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Tort F, et al. Exome sequencing identifies a new mutation in SERAC1 in a patient with 3-methylglutaconic aciduria. Mol Genet Metab. 2013;110(1-2):73–7.PubMedCrossRefGoogle Scholar
  62. 62.
    Wortmann SB, et al. 3-Methylglutaconic aciduria—lessons from 50 genes and 977 patients. J Inherit Metab Dis. 2013;36(6):913–21.PubMedCrossRefGoogle Scholar
  63. 63.
    Barth PG, Wanders RJ, Vreken P. X-linked cardioskeletal myopathy and neutropenia (Barth syndrome)-MIM 302060. J Pediatr. 1999;135(3):273–6.PubMedCrossRefGoogle Scholar
  64. 64.
    Stojanovic V, Doronjski A. Mild form of 3-methylglutaconic aciduria type IV and mutation in the TMEM70 genes. J Pediatr Endocrinol Metab. 2013;26(1-2):151–4.PubMedCrossRefGoogle Scholar
  65. 65.
    Tort F, et al. Screening for nuclear genetic defects in the ATP synthase-associated genes TMEM70, ATP12 and ATP5E in patients with 3-methylglutaconic aciduria. Clin Genet. 2011;80(3):297–300.PubMedCrossRefGoogle Scholar
  66. 66.
    Shchelochkov OA, et al. Milder clinical course of type IV 3-methylglutaconic aciduria due to a novel mutation in TMEM70. Mol Genet Metab. 2010;101(2-3):282–5.PubMedCrossRefGoogle Scholar
  67. 67.
    Suomalainen A, et al. FGF-21 as a biomarker for muscle-manifesting mitochondrial respiratory chain deficiencies: a diagnostic study. Lancet Neurol. 2011;10(9):806–18.PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Ji X, et al. Growth differentiation factor 15 is a novel diagnostic biomarker of mitochondrial diseases. Mol Neurobiol. 2017;54(10):8110–6.PubMedCrossRefGoogle Scholar
  69. 69.
    Suomalainen A. Fibroblast growth factor 21: a novel biomarker for human muscle-manifesting mitochondrial disorders. Expert Opin Med Diagn. 2013;7(4):313–7.PubMedCrossRefGoogle Scholar
  70. 70.
    Davis RL, Liang C, Sue CM. A comparison of current serum biomarkers as diagnostic indicators of mitochondrial diseases. Neurology. 2016;86(21):2010–5.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Lehtonen JM, et al. FGF21 is a biomarker for mitochondrial translation and mtDNA maintenance disorders. Neurology. 2016;87(22):2290–9.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Tukey JW. Some thoughts on clinical trials, especially problems of multiplicity. Science. 1977;198(4318):679–84.PubMedCrossRefGoogle Scholar
  73. 73.
    Schuller Y, et al. Factors contributing to the efficacy-effectiveness gap in the case of orphan drugs for metabolic diseases. Drugs. 2017;77(13):1461–72.PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Mayhew JE, et al. Reliable surrogate outcome measures in multicenter clinical trials of Duchenne muscular dystrophy. Muscle Nerve. 2007;35(1):36–42.PubMedCrossRefGoogle Scholar
  75. 75.
    Filipovic Pierucci A, et al. Quantifiable evaluation of cerebellar signs in children. Neurology. 2015;84(12):1225–32.PubMedCrossRefGoogle Scholar
  76. 76.
    ISPOR Good Practices for Outcomes Research Index. 2018. https://www.ispor.org/workpaper/practices_index.asp.
  77. 77.
    Morel T, Cano SJ. Measuring what matters to rare disease patients - reflections on the work by the IRDiRC taskforce on patient-centered outcome measures. Orphanet J Rare Dis. 2017;12(1):171.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Radboud Center for Mitochondrial MedicineNijmegenThe Netherlands
  2. 2.Wellcome Centre for Mitochondrial Research, Institute of NeuroscienceNewcastle UniversityNewcastle Upon TyneUK

Personalised recommendations