Abstract
Duchenne Muscular Dystrophy (DMD) is an X-linked recessive neuromuscular disorder primarily affecting males, caused by mutations in the dystrophin gene. The absence of dystrophin protein leads to progressive skeletal muscle degeneration. Recent advances in the therapeutic landscape underscore the need to identify appropriate outcome measures to assess treatment efficacy in ambulant and non-ambulant DMD patients, across clinical and research settings. This is essential for accurately evaluating new treatments and attributing therapeutic benefits.
It is crucial to establish a robust correlation between outcome scores and disease progression patterns. This task is challenging since functional test performance may be influenced by different patient’s characteristics, including the physiological evolution of the neurodevelopment together with the disease progression. While widely used DMD outcomes such as the North Star Ambulatory Assessment, the 6-Minute Walking Test, the 4 stairs climbed, and the Performance of the Upper Limb exhibit reliability and validity, their clinical significance is influenced by the wide phenotype and progression variability of the disease.
We present and discuss the features (relevance, quantifiability, validity, objectivity, reliability, sensitivity, specificity, precision) of available DMD outcome measures, including new potential measures that may be provided by digital tools and artificial intelligence.
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Introduction
Duchenne Muscular Dystrophy (DMD), an X-linked recessive neuromuscular disorder due to the absence or deficiency of dystrophin protein, has a global incidence of 1 in 5000 live male births [1]. The absence of dystrophin produces progressive skeletal muscle degeneration and weakness, appearing before age 6 [2], leading to a loss of ambulation (LOA) and impairment of respiratory and cardiac muscles that is the main death cause [3].
Improvements of standard of care therapy, have significantly delayed LOA onset, from a mean age of 8.5 years before the advent of steroids, to 13 to 14 years in the last two decades [4]. Besides the administration of glucocorticoids the main treatment strategy for DMD currently involves a multidisciplinary approach [1].
Open therapeutic issues include cardiorespiratory complications, psychological and social disturbances, and the pediatric to adult care transition [5]. Indeed, DMD patients are at a high risk of developing anxiety and depression, cognitive function disorders, including intellectual disability, attention deficit hyperactivity disorder, obsessive-compulsive disorder [6, 7]. In addition, the transition to adult care is accompanied by challenges due to the complex healthcare needs associated with the progression of the disease [8, 9]. Notwithstanding the clinical variability, although the typical age of death was in late twenties, nowadays DMD patients have been surviving up to the 30s and beyond, with a median life expectancy of 28.1 years for patients born after 1990 [10]. Big therapeutic advancements, including gene therapy, are emerging, but the determination of the most suitable treatment for each patient remains challenging, given the disease high phenotypic variability and progressivity [11]. Understanding disease progression in research and clinical practice is also challenging because DMD can affect the normal growth and development of the brain, thus impacting physiological neurodevelopmental growth patterns. As a result, it is complicated to distinguish between impairments caused by the disease itself and those related to impaired developmental processes [1]. Indeed, mutated isoforms of dystrophin expressed in the brain cause modifications in the volume and organization of brain Purkinje cells, impacting synaptic neurotransmission with the possible development of cognitive deficits, impairment of learning abilities, and potential behavioral issues [1].
With the increased number of clinical trials in DMD over the past two decades [1], the dialogue between stakeholders (physicians, regulators, biostatisticians) has become crucial.
Much consideration has been given to outcome measures for treatment efficacy and safety, in clinical and research settings, in ambulant and non-ambulant DMD patients [12], to attribute appropriate value to the new therapies.
We aim to provide a comprehensive DMD outcome measure description and challenges related to their identification, optimization, validation and application, within the framework of clinical research methodology, taking into account the evolving therapeutic landscape and the more recent knowledge and technology advancements.
Current classification of outcomes
Effective outcome measures stand as a cornerstone in evaluating therapies. Clinical trials may significantly impact patient care, prioritizing outcomes aligned with the everyday patient experience. Drawing a parallel with Galileo Galilei’s groundbreaking scientific revolution, it becomes evident that achieving remarkable results requires looking in the right direction (the sky) with the appropriate tools (telescope). In clinical trials, the right direction encompasses clinically meaningful outcomes, while the right tools consist of valid and significant endpoints. Unfortunately, numerous trials fall short in this regard, due to inadequacies in outcome selection, data collection, and reporting practices [13].
To be effective, outcome measures must possess specific attributes, as outlined by Piantadosi in 2005 [14] (Table 1), and it is imperative to understand the potential pitfalls that can undermine their validity.
Outcome measures can be categorized as biomarkers that measure biological phenomena (e.g., blood metabolites, electrophysiological markers, or imaging results), or clinical measures that assess meaningful aspects. In therapeutic trials, selecting outcome measures aligned with the clinical development phase is essential. Biomarkers prove most valuable in exploratory trials (Phase I, IIa), while clinical measures are better suited for efficacy trials (Phase IIb or III) designed to evaluate phenomena of primary interest for patients, such as survival, disease progression, and quality of life (QoL). In some scenarios, specialized assessments addressing disease-specific issues may be favored (e.g., targeting respiratory function is essential when respiratory complications arise). In the case of infants, toddlers, and young children, age-validated tools are deemed essential. Remarkably, regulatory authorities like the EMA and FDA although providing details about outcome measures and their applicability (Table 2), have not provided specific recommendation for the choice of measurement tools for specific DMD trials, as the choice must be tailored to study population and design.
Challenges in selecting DMD Outcome measures
Several clinical challenges undermine the choice of outcome measures in DMD, including the clinical variability and stratification of phenotypes together with a lack of comprehensive knowledge on the disease natural history.
Variability in disease progression
In ambulant DMD patients, disease progression shows significant variability, ranging from potential improvements to rapid deterioration [15]. Genetic diversity among DMD patients is considerable and several cohort studies have sought to establish genotype-phenotype correlations with varying outcomes [15, 16]. Genotype differences influence long-term progression [17]; moreover, corticosteroids’ use further contributes to clinical heterogeneity, variably delaying the decline in muscular, pulmonary, and cardiac function [18, 19] with possible genotype-drug therapeutic interactions [20]. Additionally, several factors influence changes in functional tests over time, including age, baseline ambulatory function, variations in muscle load, stress, fiber-type composition and maturation [16, 17, 19, 21, 22]. Importantly, age per se offers limited prognostic value given the variability in disease progression and function with time [23].
The onset and progression of DMD outcomes do not consistently follow a stereotypical pattern. Cardiomyopathy, for instance, may occur earlier or later in some patients, and it does not always correlate with muscular weakness [24].
Efforts have been made to categorize DMD trajectories [23], and researchers are exploring magnetic resonance imaging (MRI) for muscle composition and other biomarkers to categorize the disease progression and to predict clinical outcomes [25].
Additionally, appropriate outcome measures to assess upper limb function and functional abilities across the ambulant to non-ambulant spectrum are required for older patients [26]. Signs of upper limb weakness can manifest early in ambulant DMD patients, while non-ambulant patients exhibit variability due to the LOA timing and the onset of scoliosis, osteoporosis, obesity, disuse atrophy, contractures, and psychosocial challenges [18, 27, 28].
Importantly, it has emerged that combining multiple functional measures yields a more accurate prognosis then relying on a single measure alone [22].
Challenges in biomarkers development
DMD clinical trials have encountered substantial hurdles, including the lack of reliable biomarkers of disease progression [29]. Biomarkers may be classified as: (i) susceptibility/risk, (ii) diagnostic, (iii) prognostic, (iv) disease progression monitoring, (v) predictive, (vi) pharmacodynamic, (vii) safety [30]. For the development of drugs, biomarkers are particularly relevant when they are validated as surrogate endpoints, and accordingly they may substitute a clinical endpoint [31]. Importantly, biomarkers that works as surrogate endpoints may contribute to speed up the definition of a drug benefit/risk balance thanks to a decreased variability in comparison to functional tests [29]. Similar to other rare diseases, the identification and validation of biomarkers for DMD is challenged from different points of view, including technical criticalities associated with the DMD multi-faceted pathophysiology, the limited number of samples mirroring the low number of patients, the limited applicability of statistical models developed for non-rare diseases, and, in the case of more advanced technology, the increasing costs and the complex management of high-throughput data [29]. Notwithstanding the abovementioned challenges, much attention continues to biomarker development for DMD and some of them, including muscular injury biomarkers, microdystrophin and MRI measurement, are commonly included as endpoints in clinical trials. Creatine kinase (CK) and other muscle proteins, initially increased, decline over time as the disease progresses, primarily reflecting early muscle mass loss. Hence, their utility in monitoring progression and therapy response diminishes as the disease advances [29]. Furthermore, CK specificity is limited due to its possible modification in different muscle-related disorders, age, metabolic changes, trauma, and physical exercise significantly impact its levels [29].
Muscle injury biomarkers that are stable over time could be explorative for assessing the effectiveness of dystrophin replacement and sarcolemma-stabilizing therapies in younger DMD patients [32]. Microdystrophin expression is another surrogate endpoint proposed for monitoring drug activity in trials investigating medications that target (micro)dystrophin expression [33]. However, there is the urgent need to identify clinically meaningful biomarkers applicable for drugs acting by different mechanisms of action.
MRI-based measurement of fat fraction has emerged as a valuable and objective non-invasive tool to monitor progression. Disease progression can include muscle necrosis and dysfunctional regeneration with the substitution of muscle tissue with fat and fibrotic material. Importantly, muscle fat fraction correlates with functional outcomes, predicting mobility decline before any functional test [34].
To enhance biomarker accuracy, some studies recommend transitioning from single biomarkers to biomarker “panels/signatures,” a key step toward precision medicine [29].
Current scenario
In the assessment of motor dysfunction, the distinction between ambulatory and non-ambulatory patients’ performances is crucial, considering also that most ambulatory patients will later become non-ambulatory. Currently, measurement tools suitable for multicentric trials and, hence, widely employed include the Northern Star Ambulatory Assessment (NSAA) [35], the 6-minute walk test (6MWT) [36], the 4 stairs climb test (4SC) [37], and the performance of upper limb (PUL) which is the sole tool for both ambulatory and non-ambulatory patients [38]. Some additional timed functional assessments (Ten-Meter Walk-Run Test, Four-Square Step Test, Timed up and go test) are available [39] for evaluating neuromuscular conditions. Importantly, according to EMA guidelines [40], clinical trials should choose one of the abovementioned endpoints, often NSAA, as primary, nonetheless including one or more of the others as secondary or tertiary endpoints. Table 3 classifies the main available outcome measures according to Piantadosi’s criteria.
North star ambulatory assessment (NSAA)
The NSAA is a validated and reliable DMD-specific assessment scale [35, 41] (Table 4), recognized as a reference outcome measure for assessing the course of the disease and, in the last years, frequently chosen as the primary endpoint in clinical studies [41].
The NSAA, a practical test that can be easily completed in 10 min, allows an extensive assessment of motor performance domains, finding wide use in clinical trials, both blinded and open-label, and observational studies [34, 41, 42]. NSAA is quick, clear, easy to implement and specific, but per definition applicable only in ambulant patients. Some disagreement or inconsistency among the observers’ ratings or measurements has been reported, indicating that the scoring can be, at least partially, subjective. However, an appropriate training period for the observers is suggested to reduce interobserver variability [41]; no difficulties in performing each item and in obtaining adequate videos with a hand-held camera are reported, even after a short training session. It is worth noting that the extensive application of the NSAA, with its several items for different motor domains, may allow the identification of meaningful item subsets and contribute to the improvement of the area.
Six minute walk test (6MWT)
The 6MWT (Table 4), although not specific for DMD, has demonstrated validity, sensitivity and reliability, is accurate, simple, well-tolerated (Table 3), and may be used as a primary clinical endpoint in ambulatory DMD trials [43, 44]. However, the results can be affected by several factors, such as age, height, and weight; in addition, the test do not provide information on the specific muscle groups affected by DMD.
Four stairs climb (4SC)
The timed 4 stairs climb (4SC), is considered a valid, reliable and feasible non DMD-specific measure [45] of motor function useful to assess dynamic balance, functional abilities and falling risk in children with DMD [37, 43] (Table 4). It is used both in clinical trials and in clinical routine as it is cost-effective, although it may be not specific for muscle strength assessment and influenced by conditions such as weight gain [44, 46].
Performance of the upper limb (PUL)
The Performance of the Upper Limb module (PUL), often used as the primary endpoint for non-ambulant patients [47] (Table 4), was developed and validated to evaluate upper limb function across the spectrum of ambulant and non-ambulant DMD patients [38]. This module, in its original PUL 1.2 version, has shown reliability and a correlation with the 6MWT in ambulant patients [48, 49]. It is also sensitive to differences in steroid regimes in non-ambulant DMD boys [47].
A revised version known as PUL 2.0 has been developed, improving the scoring system and item linearity, reducing the ceiling effect, and detecting significant changes over a 2-year period [50]. In ambulant boys, decreasing functional ability measured by the 6MWT correlates with PUL 2.0 changes [51].
MRI outcomes
The use of imaging techniques has been increasingly explored [52], as mentioned above for MRI applied to detect and track early muscle-related alterations [25].
Cardiac magnetic resonance imaging (CMRI) detects myocardial fibrosis. Although further validation is required, CMRI is a promising tool to evaluate cardiac involvement in DMD [53]. CMRI, due to increased sensitivity, outperforms echocardiography in the early detection of DMD-related cardiomyopathy [54], that being clinically silent in the initial stages of the disease, progressively worsens and can ultimately lead to death [54, 55]. Despite the recognition of the importance of cardiovascular involvement, regulatory agencies have not yet approved any cardiac endpoint [56].
As mentioned above, the distinctive shift from skeletal muscle tissue to fat, accompanied by a decline in overall performance can be assessed through MR fat fraction analysis [34, 57]. MR application is less valuable in younger boys, as they have low fat fractions in early disease stages [34], and efforts to enhance reliability in this condition are ongoing [58]. A proposed correlation between MR fat fraction data and clinical outcomes such as 6MWT can provide information about how muscle characteristics influence disease trajectories [59]. This analysis highlights the potential strength of imaging biomarkers for diagnosis and prognosis, and pave the road for further application of algorithms and artificial intelligence (AI) to the existing data [59]. While MRI findings still require validation, their potential to advance the comprehension and management of DMD is unquestionable.
Patient reported outcomes measures (PROMs)
Patient-reported outcome measures (PROMs) are gaining significance in clinical studies, reflecting the growing emphasis on the patient perspective.
Many PROMs assessing QoL in DMD patients lack robust evidence supporting their validity [60]. Only three validated and reliable Health-Related Quality of Life (HRQoL) instruments are available for DMD: the Pediatric Quality of Life Inventory (PedsQL), Personal Adjustment and Role Skills Scale, 3rd edition (PARS-III) [61, 62] and Pediatric Outcomes Data Collection Instrument (PODCI) [63].
While some studies have shown correlations between HRQoL measures and functional tests, this relationship may not hold when considering changes over 12 months [64]. Also, for PedsQL and PARS-III, some issues have been raising. The PedsQL shows limitations as it contains only few items specifically addressing how children adjust to chronic illness. Moreover, several items in the PedsQL are not related to specific behaviors, meaning that this tool might fail in capturing the factors relevant to a child’s experience with chronic illness [62]. A Rasch analysis has indicated that the PedsQL does not effectively quantify HRQoL in patients with DMD [62, 65]. In contrast, the PARS-III while being specifically designed to gauge psychosocial adjustment in individuals with chronic physical illnesses like DMD, lacks a specific cutoff score [62].
Digital tools and artificial intelligence (AI)
Traditional clinical trial endpoints have not kept pace with the recent progress in therapies. Many of these endpoints, such as the 6MWT, have remained unchanged for decades, despite advances in medical technology [66].
However, digital biomarkers, defined as quantifiable data measured by digital devices, are increasingly being used as they can provide objective and reproducible data from patients’ daily lives, reducing errors associated with subjective assessments [58, 67]. These biomarkers often utilize digital sensors to monitor both lower and upper limb movements [67], making them suitable for assessing also non-ambulant patients [66]. A wearable device to quantify patient’s ambulation ability by the measure of the stride velocity 95th Centile has been recently validated by the EMA through comparison with 6MWT and NSAA. Stride velocity 95th Centile is now considered acceptable as a secondary endpoint for ambulant DMD patients aged 5 and above in clinical trials [68].
Despite their potential, many digital biomarkers are limited in scope, primarily focusing on lower-body performance. Additionally, they often replicate human observer biases and do not harness the full power of AI in healthcare [69].
Ricotti et al. recently developed the KineDMD biomarker, which leverages AI to collect and analyze whole-body movement data of patients with DMD. It focuses on natural movement behavior, providing a comprehensive and robust measure of motor capability. This biomarker can predict disease progression and potentially track therapy response [69], however, full validation in large population is still lacking.
Discussion
Evaluating disease progression in DMD is a significant challenge due to the number of variables in action, including the heterogeneity in the severity and symptoms affecting this population, and the involvement of children and adults, as this implies that developmental maturation, puberty, and old age need to be considered.
Additionally, patients with DMD often exhibit neuropsychiatric issues that include cognitive impairment, deficit in attention and/or hyperactivity disorder, autism spectrum disorder, anxiety, and obsessive-compulsive disorder. These neuropsychiatric manifestations add complexity to the assessment and management of disease, significantly impacting the QoL of the patients [1] but also the sensitivity and precision of functional tests. Comprehensive neuropsychological evaluations and tailored interventions are then required to support both physical and mental health needs of these patients, but also to develop appropriate tools to monitor treatment outcomes.
Assessing a meaningful change in the employed outcome measures, which genuinely reflects the aspects of disease progression important to the patients and their families, is urgent and crucial for comprehending the results of clinical trials and designing well-powered studies [70, 71]. A critical point for DMD trials is certainly the duration of observation, as for many studies only short-term results (e.g., 48 weeks) are available. Importantly, given the established DMD variability, longer time is likely needed to adequately measure benefit. Time is a primary determinant of DMD clinical features as, although with somehow unpredictable patterns, it matters for functional loss. Importantly, as mentioned above, a significant number of individuals with DMD have cognitive and behavior issues that contribute to existing limitations in functional outcomes and act as confounders for the functional assessment. Considering the complexity of DMD natural history and limitations in the DMD drug assessment, innovative approaches are more than needed. The Critical Path Institute (c-path - DRSC consortia) has launched a clinical trial simulation platform, created from a series of disease progression models derived from the data in the database [72]. This international platform will allow clinical trial sponsors to forecast changes in clinically-meaningful endpoints, which would inform clinical trial protocol development with respect to inclusion criteria, endpoints, as well as the size and length and statistical analysis of clinical trials. Importantly, the Collaborative Trajectory Analysis Project has been extensively working around consensus models from aggregated data and pooled modeling in order to improve clinical trial design [42]. Further efforts are needed to improve the background knowledge needed to properly inform the design of clinical trials and improve their ability to detect the value of new treatments.
Meaningful differences in clinical presentation or disease progression and response to treatment involves two distinct approaches: Minimal Clinically Important Differences (MCID) and Minimal Detectable Change (MDC) methods. These two approaches are used in clinical research. Nonetheless, only a limited number of investigations have examined meaningful changes in various DMD outcomes [70, 71]. This scarcity might be attributed to the fact that many clinical trials primarily aim to decelerate disease progression rather than enhance motor function. Table 5 summarizes the approaches used in clinical research to determine meaningful differences in clinical presentation and response to treatment.
Given the considerable variability in disease progression and phenotype, and the differences in patients’ ability to perform functional tests, it is imperative to establish a strong correlation between changes in outcome scores and the pattern of disease progression. Indeed, as the disease progresses and motor function declines, some of the traditional outcome measures become less effective at detecting functional changes. On the other hand, PROMs which may not detect differences in the early stages of the disease, become more valuable in assessing changes as the disease advances. Digital tools, also supported by AI, and bioimaging, based on objective measures, should provide an evaluation that is not influences by the progression of the disease (Fig. 1).
Some of the most used outcomes need improvements, as they were validated when the disease scenario was different. For NSAA, a linearized scoring system has been introduced to convert raw scores into a linear scale (0 to 100) [73], enhancing the psychometric robustness of scoring across a broader range of disease stages and is particularly useful for studies over 18–24 months [58].
Integrating patient’s perspectives with functional scores may increase the clinical meaningfulness of outcomes changes, differentiating between a reduced ability and complete loss of function [70]. Notably, this optimization of NSAA analysis reinforces the intrinsic potential of a multi-item score as outcome measure for complex disease like DMD.
The influence of age is significant for several outcomes: children < 7 years old may show increases in 6MWT over a year despite muscular impairment [74]. For older DMD subjects, rates of decline in 6MWT usually vary. Normative data across age groups, genders, and body sizes facilitate comparisons, acknowledging the fact that performance in healthy volunteers changes with development and aging [23]. Patient stratification according to age, baseline 6MWT, and corticosteroid use is crucial in the trials that use the 6MWT, and adjusting 6MWT to a percent predicted 6MWT could help distinguish normal growth from disease-related progression and treatment effects [75].
Additionally, maintaining a longitudinal history of outcome scores in each patient improves the disease progression evaluation. Muntoni et al. recently suggested that understanding individual NSAA skills and their scores at previous assessments can provide a more granular approach to assessing functional status [71].
Finally, when recording NSAA data, being valuable for any multi-item outcome [71], it is necessary to report explanations to clarify the score in case all the activities receive the “not obtainable” qualification, and to enhance the value of longitudinal data.
Linear outcomes for assessing upper limb function are strongly recommended, especially with the growing need to include older and non-ambulatory patients in clinical trials; importantly, factors like muscle contractures, Body Mass Index, and steroid variations may alter upper limb outcomes [58]. The relationship between PUL and overall functional abilities in ambulant patients is not linear, indicating that the rate of change is not constant across the three domains [51]. This non-linearity can be challenging for trial design, particularly when involving patients with varying functional abilities. Efforts are underway to predict the rate of progression for the individual domains and for the total score.
Many physical assessments heavily depend on a patient’s ability to cooperate with clinicians, follow instructions precisely, and execute the test. These assessments can be influenced by the patient’s motivation, level of physical activity, age, psychological well-being, and attention span, that may significantly impact the sensitivity and precision of functional tests. Ensuring consistency is of paramount importance, necessitating high standardization for the different evaluation tools, supported by top-quality training modules [58].
Despite its potential, imaging is not a routine part of the long-term clinical follow-up due to the lack of standardized protocols for image acquisition and data analysis, the high cost, and scan durations, which may hinder compliance in younger patients [58]. However, its role may increase in the future thanks to tech advancements. In this boosted tech scenario, digital sensors integrated with AI are emerging as promising tools for objectively and reproducibly capturing functional changes and for interpreting data from omics analyses. Advances in omics technologies generate unprecedented data for disease modeling. As proven by the AI-driven platform PandaOmics, which integrates multiple datasets to predict biomarkers and therapeutic targets based on disease relevance and clinical trajectory [76], AI and machine learning, aggregating disparate data types may identify complex interactions and characterize disease patterns with therapeutic potential beyond human capabilities [77].
In this scenario, PROMs are certainly valuable but they should not be expected to strictly correlate with functional changes, due to the possible adaptation of the individuals to their changed functional status and the high subjectivity of evaluation [58].
Conclusions
Developing and optimizing outcome measures for specific clinical needs, requires extensive landscaping work and focusing on relevance to patients.
A better understanding of the relationship between clinical progression in DMD and endpoints within clinical trial timeframe would be helpful for drug development, is required by regulators, and needed by all stakeholders.
To this aim, establishing robust links between outcome scores and disease progression patterns is essential and may be favored by expressing scores as a percentage of healthy peers’ achievements, maintaining longitudinal score histories, and avoiding temporary null scores within multi-item outcomes.
The quote in the title is commonly cited as a summary of a more elaborate sentence by Lord KelvinFootnote 1, which ultimately attributes the meaning of scientific knowledge to both the measurability of phenomena and the ability to express, in numerical terms, what has been measured. This reflection suits perfectly with clinical research also in the DMD. Our ability to properly measure disease manifestations and to detect clinically meaningful changes related to treatments, will be the proof of a satisfactory knowledge that may open the road to further improvements in patients’ lives.
Data availability
No datasets were generated or analysed during the current study.
Change history
02 September 2024
A Correction to this paper has been published: https://doi.org/10.1007/s13760-024-02619-5
Notes
“I often say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science, whatever the matter may be” Thomson W. Constitution of matter. Electrical Units of Measurement. In: Company Ma, editor. Popular lectures and addresses in three volumes. London (UK)1889. p. 73 − 4.
References
Duan D, Goemans N, Takeda S, Mercuri E, Aartsma-Rus A (2021) Duchenne muscular dystrophy. Nat Rev Dis Primers 7(1):13. https://doi.org/10.1038/s41572-021-00248-3
Bushby KM, Hill A, Steele JG (1999) Failure of early diagnosis in symptomatic duchenne muscular dystrophy. Lancet 353(9152):557–558. https://doi.org/10.1016/s0140-6736(98)05279-9
Van Ruiten H, Bushby K, Guglieri M (2017) State-Of-The-art advances in Duchenne muscular dystrophy. Eur Med J 2(1):90–99. https://doi.org/10.33590/emj/10311993
Matthews E, Brassington R, Kuntzer T, Jichi F, Manzur AY (2016) Corticosteroids for the treatment of Duchenne muscular dystrophy. Cochrane Database Syst Rev 2016(5):CD003725. https://doi.org/10.1002/14651858.CD003725.pub4
Birnkrant DJ, Bello L, Butterfield RJ, Carter JC, Cripe LH, Cripe TP et al (2022) Cardiorespiratory management of Duchenne muscular dystrophy: emerging therapies, neuromuscular genetics, and new clinical challenges. Lancet Respir Med 10(4):403–420. https://doi.org/10.1016/S2213-2600(21)00581-6
Ozer U, Tufan A (2019) Psychiatric comorbidities in cases with Duchenne muscular dystrophy: a case series. Dusunen Adam J Psychiatry Neurol Sci 32:359–364. https://doi.org/10.14744/DAJPNS.2019.00052
Pane M, Lombardo ME, Alfieri P, D’Amico A, Bianco F, Vasco G et al (2012) Attention deficit hyperactivity disorder and cognitive function in Duchenne muscular dystrophy: phenotype-genotype correlation. J Pediatr 161(4):705–9e1. https://doi.org/10.1016/j.jpeds.2012.03.020
Wasilewska E, Malgorzewicz S, Sobierajska-Rek A, Jablonska-Brudlo J, Gorska L, Sledzinska K et al (2020) Transition from Childhood to Adulthood in patients with Duchenne muscular dystrophy. Med (Kaunas) 56(9). https://doi.org/10.3390/medicina56090426
Paramsothy PPDMPH, Herron Ar PhD MA, Lamb MMP, Kinnett K, Msn A, Wolff JP, Yang MM et al (2018) Health Care Transition experiences of males with childhood-onset Duchenne and Becker muscular dystrophy: findings from the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet) Health Care transitions and other Life experiences Survey. PLoS Curr 10. https://doi.org/10.1371/currents.md.7de8a1c6798d7a48d38ea09bd624e1cd
Broomfield J, Hill M, Guglieri M, Crowther M, Abrams K (2021) Life Expectancy in Duchenne muscular dystrophy: reproduced individual Patient Data Meta-analysis. Neurology 97(23):e2304–e14. https://doi.org/10.1212/WNL.0000000000012910
FDA Duchenne Muscular Dystrophy and Related Dystrophinopathies: Developing Drugs for Treatment 2018 [ https://www.fda.gov/media/92233/download
Koeks Z, Bladen CL, Salgado D, Zwet E, Pogoryelova O, McMacken G et al (2017) Clinical outcomes in Duchenne muscular dystrophy: a study of 5345 patients from the TREAT-NMD DMD Global Database. J Neuromuscul Dis 4(4):293–306. https://doi.org/10.3233/JND-170280
Heneghan C, Goldacre B, Mahtani KR (2017) Why clinical trial outcomes fail to translate into benefits for patients. Trials 18(1):122. https://doi.org/10.1186/s13063-017-1870-2
Piantadosi S (2005) Translational clinical trials: an entropy-based approach to sample size. Clin Trials 2(2):182–192. https://doi.org/10.1191/1740774505cn078oa
Bushby K, Connor E (2011) Clinical outcome measures for trials in Duchenne muscular dystrophy: report from International Working Group meetings. Clin Investig (Lond) 1(9):1217–1235. https://doi.org/10.4155/cli.11.113
Gambetta KE, McCulloch MA, Lal AK, Knecht K, Butts RJ, Villa CR et al (2022) Diversity of Dystrophin Gene mutations and Disease Progression in a contemporary cohort of Duchenne muscular dystrophy. Pediatr Cardiol 43(4):855–867. https://doi.org/10.1007/s00246-021-02797-6
Bello L, Morgenroth LP, Gordish-Dressman H, Hoffman EP, McDonald CM, Cirak S et al (2016) DMD genotypes and loss of ambulation in the CINRG Duchenne Natural History Study. Neurology 87(4):401–409. https://doi.org/10.1212/WNL.0000000000002891
Birnkrant DJ, Bushby K, Bann CM, Apkon SD, Blackwell A, Brumbaugh D et al (2018) Diagnosis and management of Duchenne muscular dystrophy, part 1: diagnosis, and neuromuscular, rehabilitation, endocrine, and gastrointestinal and nutritional management. Lancet Neurol 17(3):251–267. https://doi.org/10.1016/S1474-4422(18)30024-3
Kourakis S, Timpani CA, Campelj DG, Hafner P, Gueven N, Fischer D et al (2021) Standard of care versus new-wave corticosteroids in the treatment of Duchenne muscular dystrophy: can we do better? Orphanet J Rare Dis 16(1):117. https://doi.org/10.1186/s13023-021-01758-9
Zhang S, Qin D, Wu L, Li M, Song L, Wei C et al (2021) Genotype characterization and delayed loss of ambulation by glucocorticoids in a large cohort of patients with Duchenne muscular dystrophy. Orphanet J Rare Dis 16(1):188. https://doi.org/10.1186/s13023-021-01837-x
Bello L, D’Angelo G, Villa M, Fusto A, Vianello S, Merlo B et al (2020) Genetic modifiers of respiratory function in Duchenne muscular dystrophy. Ann Clin Transl Neurol 7(5):786–798. https://doi.org/10.1002/acn3.51046
Muntoni F, Signorovitch J, Sajeev G, Lane H, Jenkins M, Dieye I et al (2023) DMD genotypes and motor function in Duchenne muscular dystrophy: a multi-institution Meta-analysis with implications for clinical trials. Neurology 100(15):e1540–e54. https://doi.org/10.1212/WNL.0000000000201626
Mercuri E, Signorovitch JE, Swallow E, Song J, Ward SJ, Group DMDI et al (2016) Categorizing natural history trajectories of ambulatory function measured by the 6-minute walk distance in patients with Duchenne muscular dystrophy. Neuromuscul Disord 26(9):576–583. https://doi.org/10.1016/j.nmd.2016.05.016
Birnkrant DJ, Carter JC (2021) Cardiopulmonary phenotypic variability and discordance in Duchenne muscular dystrophy: implications for new therapies. Pediatr Pulmonol 56(4):738–746. https://doi.org/10.1002/ppul.25111
Senesac CR, Barnard AM, Lott DJ, Nair KS, Harrington AT, Willcocks RJ et al (2020) Magnetic Resonance Imaging Studies in Duchenne muscular dystrophy: linking findings to the physical therapy clinic. Phys Ther 100(11):2035–2048. https://doi.org/10.1093/ptj/pzaa140
Mathews KD, Conway KM, Gedlinske AM, Johnson N, Street N, Butterfield RJ et al (2021) Characteristics of clinical trial participants with Duchenne muscular dystrophy: data from the muscular dystrophy surveillance, Tracking, and Research Network (MD STARnet). Child (Basel) 8(10):835. https://doi.org/10.3390/children8100835
Birnkrant DJ, Bushby K, Bann CM, Alman BA, Apkon SD, Blackwell A et al (2018) Diagnosis and management of Duchenne muscular dystrophy, part 2: respiratory, cardiac, bone health, and orthopaedic management. Lancet Neurol 17(4):347–361. https://doi.org/10.1016/S1474-4422(18)30025-5
Birnkrant DJ, Bushby K, Bann CM, Apkon SD, Blackwell A, Colvin MK et al (2018) Diagnosis and management of Duchenne muscular dystrophy, part 3: primary care, emergency management, psychosocial care, and transitions of care across the lifespan. Lancet Neurol 17(5):445–455. https://doi.org/10.1016/S1474-4422(18)30026-7
Fortunato F, Ferlini A (2023) Biomarkers in Duchenne muscular dystrophy: current status and future directions. J Neuromuscul Dis. https://doi.org/10.3233/JND-221666
Group F-NBW (2016-) BEST (biomarkers, EndpointS, and other tools) resource [Internet]. Bethesda (MD): National Institutes of Health (US): Silver Spring (MD). Food and Drug Administration (US)
Group BDW (2001) Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 69:89–95. https://doi.org/10.1067/mcp.2001
Hathout Y, Liang C, Ogundele M, Xu G, Tawalbeh SM, Dang UJ et al (2019) Disease-specific and glucocorticoid-responsive serum biomarkers for Duchenne muscular dystrophy. Sci Rep 9(1):12167. https://doi.org/10.1038/s41598-019-48548-9
Chamberlain JS, Robb M, Braun S, Brown KJ, Danos O, Ganot A et al (2023) Microdystrophin expression as a surrogate endpoint for Duchenne muscular dystrophy clinical trials. Hum Gene Ther 34(9–10):404–415. https://doi.org/10.1089/hum.2022.190
Sherlock SP, Zhang Y, Binks M, Marraffino S (2021) Quantitative muscle MRI biomarkers in Duchenne muscular dystrophy: cross-sectional correlations with age and functional tests. Biomark Med 15(10):761–773. https://doi.org/10.2217/bmm-2020-0801
Scott E, Eagle M, Mayhew A, Freeman J, Main M, Sheehan J et al (2012) Development of a functional assessment scale for ambulatory boys with Duchenne muscular dystrophy. Physiother Res Int 17(2):101–109. https://doi.org/10.1002/pri.520
McDonald CM, Henricson EK, Han JJ, Abresch RT, Nicorici A, Atkinson L et al (2010) The 6-minute walk test in Duchenne/Becker muscular dystrophy: longitudinal observations. Muscle Nerve 42(6):966–974. https://doi.org/10.1002/mus.21808
Ni M, Brown LG, Lawler D, Bean JF (2017) Reliability, validity, and minimal detectable change of four-step stair climb Power Test in Community-Dwelling older adults. Phys Ther 97(7):767–773. https://doi.org/10.1093/ptj/pzx039
Mayhew A, Mazzone ES, Eagle M, Duong T, Ash M, Decostre V et al (2013) Development of the performance of the Upper Limb module for Duchenne muscular dystrophy. Dev Med Child Neurol 55(11):1038–1045. https://doi.org/10.1111/dmcn.12213
Aldirmaz E, Uğur F, Yilmaz Ö, Karaduman A, Alemdaroğlu-Gürbüz İ (2023) A New Instrument to assess dynamic balance in children with Duchenne muscular dystrophy: four Square Step Test and its validity, reliability and feasibility. Dev Neurorehabil 26(1):27–36. https://doi.org/10.1080/17518423.2022.2143924
EMA Guideline on the clinical investigation of medicinal products for the treatment of Duchenne and Becker muscular dystrophy 2016 [ https://bit.ly/3S3JsXU
Mazzone ES, Messina S, Vasco G, Main M, Eagle M, D’Amico A et al (2009) Reliability of the North Star Ambulatory Assessment in a multicentric setting. Neuromuscul Disord 19(7):458–461. https://doi.org/10.1016/j.nmd.2009.06.368
Muntoni F, Signorovitch J, Sajeev G, Goemans N, Wong B, Tian C et al (2022) Real-world and natural history data for drug evaluation in Duchenne muscular dystrophy: suitability of the North Star Ambulatory Assessment for comparisons with external controls. Neuromuscul Disord 32(4):271–283. https://doi.org/10.1016/j.nmd.2022.02.009
McDonald CM, Henricson EK, Abresch RT, Florence J, Eagle M, Gappmaier E et al (2013) The 6-minute walk test and other clinical endpoints in duchenne muscular dystrophy: reliability, concurrent validity, and minimal clinically important differences from a multicenter study. Muscle Nerve 48(3):357–368. https://doi.org/10.1002/mus.23905
Wagner KR, Abdel-Hamid HZ, Mah JK, Campbell C, Guglieri M, Muntoni F et al (2020) Randomized phase 2 trial and open-label extension of domagrozumab in Duchenne muscular dystrophy. Neuromuscul Disord 30(6):492–502. https://doi.org/10.1016/j.nmd.2020.05.002
Schorling DC, Rawer R, Kuhlmann I, Muller C, Pechmann A, Kirschner J (2023) Mechanographic analysis of the timed 4 stair climb test - methodology and reference data of healthy children and adolescents. J Musculoskelet Neuronal Interact 23(1):4–25
McDonald CM, Campbell C, Torricelli RE, Finkel RS, Flanigan KM, Goemans N et al (2017) Ataluren in patients with nonsense mutation duchenne muscular dystrophy (ACT DMD): a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 390(10101):1489–1498. https://doi.org/10.1016/S0140-6736(17)31611-2
Pane M, Fanelli L, Mazzone ES, Olivieri G, D’Amico A, Messina S et al (2015) Benefits of glucocorticoids in non-ambulant boys/men with Duchenne muscular dystrophy: a multicentric longitudinal study using the performance of Upper Limb test. Neuromuscul Disord 25(10):749–753. https://doi.org/10.1016/j.nmd.2015.07.009
Pane M, Mazzone ES, Fanelli L, De Sanctis R, Bianco F, Sivo S et al (2014) Reliability of the performance of Upper Limb assessment in Duchenne muscular dystrophy. Neuromuscul Disord 24(3):201–206. https://doi.org/10.1016/j.nmd.2013.11.014
Pane M, Mazzone ES, Sivo S, Fanelli L, De Sanctis R, D’Amico A et al (2014) The 6 minute walk test and performance of upper limb in ambulant duchenne muscular dystrophy boys. PLoS Curr 6. ecurrents.md.a93d9904d57dcb 08936f2ea89bca6fe6
Mayhew AG, Coratti G, Mazzone ES, Klingels K, James M, Pane M et al (2020) Performance of Upper Limb module for Duchenne muscular dystrophy. Dev Med Child Neurol 62(5):633–639. https://doi.org/10.1111/dmcn.14361
Pane M, Coratti G, Brogna C, Bovis F, D’Amico A, Pegoraro E et al (2023) Longitudinal analysis of PUL 2.0 domains in Ambulant and Non-ambulant Duchenne muscular dystrophy patients: how do they change in relation to functional ability? J Neuromuscul Dis 10(4):567–574. https://doi.org/10.3233/JND-221556
Rott HD, Breimesser FH, Rodl W (1985) Imaging technics in muscular dystrophies. J Genet Hum 33(5):397–403
Kerstens TP, van Everdingen WM, Habets J, van Dijk APJ, Helbing WA, Thijssen DHJ et al (2023) Left ventricular deformation and myocardial fibrosis in pediatric patients with Duchenne muscular dystrophy. Int J Cardiol 388:131162. https://doi.org/10.1016/j.ijcard.2023.131162
Power LC, O’Grady GL, Hornung TS, Jefferies C, Gusso S, Hofman PL (2018) Imaging the heart to detect cardiomyopathy in Duchenne muscular dystrophy: a review. Neuromuscul Disord 28(9):717–730. https://doi.org/10.1016/j.nmd.2018.05.011
Bourke J, Turner C, Bradlow W, Chikermane A, Coats C, Fenton M et al (2022) Cardiac care of children with dystrophinopathy and females carrying DMD-gene variations. Open Heart 9(2). https://doi.org/10.1136/openhrt-2022-001977
Soslow JH, Xu M, Slaughter JC, Crum K, Kaslow JA, George-Durrett K et al (2023) Cardiovascular measures of all-cause mortality in Duchenne muscular dystrophy. Circ Heart Fail 16(8):e010040. https://doi.org/10.1161/CIRCHEARTFAILURE.122.010040
Barnard AM, Willcocks RJ, Triplett WT, Forbes SC, Daniels MJ, Chakraborty S et al (2020) MR biomarkers predict clinical function in Duchenne muscular dystrophy. Neurology 94(9):e897–e909. https://doi.org/10.1212/WNL.0000000000009012
Straub V, Mercuri E, group DMDoms (2018) Report on the workshop: meaningful outcome measures for Duchenne muscular dystrophy, London, UK, 30–31 January 2017. Neuromuscul Disord 28(8):690–701. https://doi.org/10.1016/j.nmd.2018.05.013
Kim S, Willcocks RJ, Daniels MJ, Morales JF, Yoon DY, Triplett WT et al (2023) Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures for Duchenne muscular dystrophy clinical trials. CPT Pharmacometrics Syst Pharmacol 12(10):1437–1449. https://doi.org/10.1002/psp4.13021
Powell PA, Carlton J, Woods HB, Mazzone P (2020) Measuring quality of life in Duchenne muscular dystrophy: a systematic review of the content and structural validity of commonly used instruments. Health Qual Life Outcomes 18:263. https://doi.org/10.1186/s12955-020-01511-z
Varni JW, Seid M, Kurtin PS (2001) PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Med Care 39(8):800–812. https://doi.org/10.1097/00005650-200108000-00006
Hendriksen JGM, Poysky JT, Schrans DGM, Schouten EGW, Aldenkamp AP, Vles JSH (2009) Psychosocial adjustment in males with Duchenne muscular dystrophy: psychometric properties and clinical utility of a parent-report questionnaire. J Pediatr Psychol 34(1):69–78. https://doi.org/10.1093/jpepsy/jsn067
McDonald CM, McDonald DA, Bagley A, Sienko Thomas S, Buckon CE, Henricson E et al (2010) Relationship between clinical outcome measures and parent proxy reports of health-related quality of life in ambulatory children with Duchenne muscular dystrophy. J Child Neurol 25(9):1130–1144. https://doi.org/10.1177/0883073810371509
Messina S, Vita GL, Sframeli M, Mondello S, Mazzone E, D’Amico A et al (2016) Health-related quality of life and functional changes in DMD: a 12-month longitudinal cohort study. Neuromuscul Disord 26(3):189–196. https://doi.org/10.1016/j.nmd.2016.01.003
Landfeldt E, Iff J, Henricson E, Cooperative International Neuromuscular Research Group I (2021) Rasch Analysis of the Pediatric Quality of Life Inventory 4.0 generic core scales administered to patients with Duchenne muscular dystrophy. Value Health 24(10):1490–1498. https://doi.org/10.1016/j.jval.2021.05.016
Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7
Youn B-Y, Ko Y, Moon S, Lee J, Ko S-G, Kim J-Y (2021) Digital biomarkers for neuromuscular disorders: a systematic scoping review. Diagnostics (Basel) 11(7):1275. https://doi.org/10.3390/diagnostics11071275
Haberkamp M, Moseley J, Athanasiou D, Andres-Trelles F, Elferink A, Rosa MM et al (2019) European regulators’ views on a wearable-derived performance measurement of ambulation for Duchenne muscular dystrophy regulatory trials. Neuromuscul Disord 29(7):514–516. https://doi.org/10.1016/j.nmd.2019.06.003
Ricotti V, Kadirvelu B, Selby V, Festenstein R, Mercuri E, Voit T et al (2023) Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy. Nat Med 29(1):95–103. https://doi.org/10.1038/s41591-022-02045-1
Ayyar Gupta V, Pitchforth JM, Domingos J, Ridout D, Iodice M, Rye C et al (2023) Determining minimal clinically important differences in the North Star Ambulatory Assessment (NSAA) for patients with Duchenne muscular dystrophy. PLoS ONE 18(4):e0283669. https://doi.org/10.1371/journal.pone.0283669
Muntoni F, Guglieri M, Mah JK, Wagner KR, Brandsema JF, Butterfield RJ et al (2022) Novel approaches to analysis of the North Star Ambulatory Assessment (NSAA) in Duchenne muscular dystrophy (DMD): observations from a phase 2 trial. PLoS ONE 17(8):e0272858. https://doi.org/10.1371/journal.pone.0272858
project Cta The Coalition Model [ https://ctap-duchenne.org/coalition-model/
Mayhew AG, Cano SJ, Scott E, Eagle M, Bushby K, Manzur A et al (2013) Detecting meaningful change using the North Star Ambulatory Assessment in Duchenne muscular dystrophy. Dev Med Child Neurol 55(11):1046–1052. https://doi.org/10.1111/dmcn.12220
Mazzone E, Vasco G, Sormani MP, Torrente Y, Berardinelli A, Messina S et al (2011) Functional changes in Duchenne muscular dystrophy: a 12-month longitudinal cohort study. Neurology 77(3):250–256. https://doi.org/10.1212/WNL.0b013e318225ab2e
Henricson E, Abresch R, Han JJ, Nicorici A, Goude Keller E, Elfring G et al (2012) Percent-predicted 6-Minute Walk Distance in Duchenne muscular dystrophy to Account for Maturational influences. PLoS Curr 4:RRN1297. https://doi.org/10.1371/currents.RRN1297
Ozerov IV, Lezhnina KV, Izumchenko E, Artemov AV, Medintsev S, Vanhaelen Q et al (2016) In silico pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development. Nat Commun 7:13427. https://doi.org/10.1038/ncomms13427
Vera CD, Zhang A, Pang PD, Wu JC (2022) Treating Duchenne muscular dystrophy: the Promise of Stem cells, Artificial Intelligence, and Multi-omics. Front Cardiovasc Med 9:851491. https://doi.org/10.3389/fcvm.2022.851491
Mayhew A, Cano S, Scott E, Eagle M, Bushby K, Muntoni F et al (2011) Moving towards meaningful measurement: Rasch analysis of the North Star Ambulatory Assessment in Duchenne muscular dystrophy. Dev Med Child Neurol 53(6):535–542. https://doi.org/10.1111/j.1469-8749.2011.03939.x
Jaeschke R, Singer J, Guyatt GH (1989) Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials 10(4):407–415 10.1016/0197–2456(89)90005-6
Beaton DE, Bombardier C, Katz JN, Wright JG, Wells G, Boers M et al (2001) Looking for important change/differences in studies of responsiveness. OMERACT MCID Working Group. Outcome measures in Rheumatology. Minimal clinically important difference. J Rheumatol 28(2):400–405
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Benemei, S., Gatto, F., Boni, L. et al. “If you cannot measure it, you cannot improve it”. Outcome measures in Duchenne Muscular Dystrophy: current and future perspectives. Acta Neurol Belg (2024). https://doi.org/10.1007/s13760-024-02600-2
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DOI: https://doi.org/10.1007/s13760-024-02600-2