Baseline characteristics and feasibility of clinical outcome measures in CDKL5 deficiency disorder: The CANDID observational study.

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Learn more: PMC Disclaimer | PMC Copyright Notice Epilepsia . 2026 Jan 13;67(4):1703–1721. doi: 10.1002/epi.70095 Search in PMC Search in PubMed View in NLM Catalog Add to search Baseline characteristics and feasibility of clinical outcome measures in CDKL5 deficiency disorder: The CANDID observational study Xavier Liogier d'Ardhuy Xavier Liogier d'Ardhuy 1 Loulou Foundation, London, UK Find articles by Xavier Liogier d'Ardhuy 1, ✉ ; CANDID Partners , Tricia Cimms Tricia Cimms 2 Ultragenyx Pharmaceutical, Novato, California, USA Find articles by Tricia Cimms 2 , Kristina Lindsten Kristina Lindsten 3 Immedica Pharma, Stockholm, Sweden Find articles by Kristina Lindsten 3 , Marco Rizzo Marco Rizzo 4 Biogen, Cambridge, Massachusetts, USA Find articles by Marco Rizzo 4 , Alison Skrinar Alison Skrinar 2 Ultragenyx Pharmaceutical, Novato, California, USA Find articles by Alison Skrinar 2 , Peter St Wecker Peter St Wecker 5 UCB Biosciences, Morrisville, North Carolina, USA Find articles by Peter St Wecker 5 , Ana Mingorance Ana Mingorance 1 Loulou Foundation, London, UK Find articles by Ana Mingorance 1 , Orrin Devinsky Orrin Devinsky 6 Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA Find articles by Orrin Devinsky 6 Author information Article notes Copyright and License information 1 Loulou Foundation, London, UK 2 Ultragenyx Pharmaceutical, Novato, California, USA 3 Immedica Pharma, Stockholm, Sweden 4 Biogen, Cambridge, Massachusetts, USA 5 UCB Biosciences, Morrisville, North Carolina, USA 6 Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA * Correspondence , Xavier Liogier d'Ardhuy, Loulou Foundation, 4 Old Park Lane, Mayfair, London W1K1QW, UK. Email: [email protected] ✉ Corresponding author. Revised 2025 Dec 27; Received 2025 Aug 11; Accepted 2025 Dec 27; Issue date 2026 Apr. © 2026 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. PMC Copyright notice PMCID: PMC13075603  PMID: 41531035 Abstract Objective CDKL5 deficiency disorder (CDD) is a rare X‐linked developmental and epileptic encephalopathy caused by loss‐of‐function variants in the CDKL5 gene. Preclinical experiments using enzyme replacement or gene therapies show promise and could be transformative therapies. This precompetitive consortium sought to harmonize nonseizure clinical endpoint selection for efficacy trials. Clinical Assessment of Neurodevelopmental Measures in CDD (CANDID) is an ongoing study evaluating the feasibility and suitability of neurocognitive tests and functioning scales in CDD patients. Methods CANDID is a 3‐year, longitudinal, noninterventional global study involving children and adults with CDD. On‐site and remote visits include clinical, behavioral, developmental, and quality of life assessments. Results We enrolled 112 patients (111 included in analyses); mean age = 8.3 years (range <1–28); 93% female; 10 participants were ≥18 years old. In the first 28 days, 82% had >16 seizures; six were seizure‐free. Median seizure onset was at 1.5 months (range = 0–66). Patients used an average of 2.6 antiseizure medications at baseline. The most frequent comorbidities included gastrointestinal hypomotility, muscle tone abnormalities, and sleep disorders. Gross Motor Function Measure‐88 (GMFM‐88) scores indicated a floor effect in crawling, standing, and walking across all ages. Vineland‐3 and Bayley‐4 scores could be derived in most, with receptive language, interpersonal relationships, and fine and gross motor scores increasing with age. Bruni sleep questionnaire identified sleep initiation, sleep–awake transition, and excessive somnolence as the most disrupted components across all age groups. The mean Quality of Life Inventory–Disability total scores ranged from 53% to 64%, the independence domain being the most impacted. Significance The scales in the CANDID study capture disease‐related deficits and phenotype variability in CDD. Floor effects in subdomains aligned with disease severity. The GMFM‐88 lacks granularity, and its operational limitations make it unsuitable for CDD trials. Baseline analyses demonstrate the feasibility and potential value of most selected scales, supporting their use in optimizing trial design and endpoint selection for future CDD clinical trials. Keywords: CANDID, CDD, CDKL5, clinical trial readiness, natural history study Key points. Despite the use of 1–6 antiseizure medications, the median daily seizure frequency ranged from 1 to 3. Age‐related skill development was observed in receptive language, interpersonal relationships, and fine and gross motor functions, as evaluated by the Vineland‐3. GMFM‐88 scores are not appropriate for use in CDD clinical trials. Most of the selected measures effectively captured disease‐related deficits and the variability of the phenotype across different ages. 1. INTRODUCTION Identified in 2004 as a monogenic disorder, CDKL5 deficiency disorder (CDD) is a rare, neurodevelopmental disorder caused by X‐linked loss‐of‐function (LoF) mutations in the CDKL5 gene leading to a deficiency of functional CDKL5 protein. The CDKL5 protein is a kinase highly expressed in the brain, with roles in establishing neural circuits and synaptic signaling. 1 Diverse, often unique, LoF CDKL5 variants interrupt protein production or interfere with its catalytic function. 2 Genotype–phenotype correlations may occur, 3 , 4 but remain poorly defined. Females are fourfold more frequent than males; severe LoF CDKL5 gene variants lacking functional protein may not survive fetal life. 1 CDD affects approximately 1 in 42 000 live births, 5 half to one third the incidence of Dravet (1:16 000) or Rett (1:10 000 females) syndromes. 6 , 7 Diagnosis depends on genetic testing. Approximately 1800 diagnosed individuals live with CDD in the United States; most are younger than 30 years. Worldwide prevalence estimates vary and are challenging, because CDD can be a lifelong condition, and routine genetic testing is lacking in many countries. CDD is a devastating life‐limiting and life‐threatening disorder that impairs brain function, causing treatment‐resistant seizures, severe neurodevelopmental delays, cortical visual impairments, impaired speech and nonverbal communications, and gross motor function. 8 , 9 , 10 Most children cannot walk, talk, or feed or care for themselves. CDD typically presents with treatment‐resistant seizures—often infantile spasms without hypsarrhythmia—in the first 3 months of life. A developmental and epileptic encephalopathy (DEE) ensues, in which seizures and abundant epileptiform activity contribute to severe cognitive and behavioral impairments. 11 CDD patients characteristically suffer multiple seizure types, varying in severity, duration, and frequency (daily seizures are common). Many children experience a seizure “honeymoon” period at 1 or 2 years of age, which typically lasts 2–18 months and does not usually coincide with development improvement. The clinical features and diagnostic criteria were recently summarized. 1 The wide spectrum and severity of CDD symptoms dramatically impacts patients and families. Intellectual disabilities are profound and complicated by severe language impairments. Fewer than 20% of individuals with CDD speak single words by age 7 years. One fourth of females produce some spoken language, signs, or abstract symbols, but no males with CDD do so; 7.5% of females speak in sentences. 12 Several therapeutic agents are being developed to treat CDD, and ganaxolone was a recently approved treatment for epilepsy in CDD patients in the United States, Europe, and China. Studies have used “countable” motor seizure frequency as the primary endpoint, similar to most clinical trials in DEEs, with treatment durations of 12–20 weeks. Several gene replacement programs are in early development to restore CDKL5 gene expression and protein production in the 50% of neurons with only a dysfunctional copy of the gene. These CDD disease‐modifying therapies could improve cognition, behavior, and sensorimotor functions as well as seizure control. Therefore, clinical trials for disease‐modifying therapies should also include nonseizure outcomes. Functioning outcome measures are not yet ready for use in CDD clinical trials, which have primarily focused on seizure counts or global impressions of severity. Outcome measures in clinical trials for other neurodevelopmental disorders may not apply to CDD. It is imperative to evaluate measures and inform the outcome selection targeting neurodevelopmental symptoms. Several important efforts are currently underway, contributing to our understanding of CDD, including the International CDKL5 Disorder Database ( https://rett.thekids.org.au/about/cdkl5‐disorder/ ), the CDKL5 Registry ( https://www.cdkl5registry.org/ ), and the United States‐based Rett Natural History Study ( https://www.rettsyndrome.org/rett‐syndrome‐registry/ ). These registries and databases primarily collect longitudinal clinical and caregiver‐reported information to define disease trajectories. The CDKL5 Clinical Severity Assessment, developed in 2019, 13 represented a first attempt to develop a comprehensive CDD‐specific tool incorporating both parent and clinician perspectives. However, none of these efforts has yet examined standardized performance scales that could serve as potential endpoints in clinical trials. The Clinical Assessment of Neurodevelopmental Measures in CDD (CANDID) study ( ClinicalTrials.gov : NCT05373719 ) is a precompetitive collaboration supported by seven biotech and pharmaceutical companies. This study sought to identify the best motor, cognitive, and behavioral outcome measures in assessing their suitability and adaptability across countries and languages to include in future international trials. Study measures assessed a wide range of skills, including cognition, adaptive behavior, and global functioning, as well as parents' perception of their child's independence and quality of life (QOL). This interim analysis includes data from the baseline study visit only. 2. MATERIALS AND METHODS 2.1. Study design This 3‐year, observational, longitudinal, 22‐center study enrolled children and adults with CDD. Enrollment began in September 2022 with participants from the United States, Canada, United Kingdom, Spain, France, Italy, Germany, and United Arab Emirates. The centers were selected based on experience in diagnosing and treating CDD, DEE clinical trial experience, and access to patients. Local patient advocacy groups (CDKL5 Alliance and the International Foundation for CDKL5 Research) participated in clinical site selection. The study aimed to enroll and follow ≥100 participants over 3 years. Participants and caregivers who met the inclusion criteria were evaluated at baseline, and at 3 (remotely), 6, 9 (remotely), 12, 18, 24, 30 (remotely), and 36 months. The study uses an all‐comers design and includes males and females with CDD, without minimum seizure count requirements. Inclusion criteria were as follows: diagnosis of CDD with pathogenic or likely pathogenic CDKL5 variant, newborn to 55 years old, caregiver willing and capable of providing written informed consent, and caregiver living with or has daily contact with study participant and can provide consistent information throughout the study. Participants were not included if they had a clinically significant neurocognitive deficit unrelated to CDD or another disorder that could confound interpretation of results. 2.2. Study procedures Written informed consent was obtained from the parents/caregivers before study enrollment. The study was conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice, and all required approvals were obtained from independent ethics committees/institutional review boards prior to the start of the study. A paper seizure diary was provided to the parents to record the daily seizure frequency for the first year of study participation. The first 28 days were used as baseline. Seizure types were classified by the site investigator and verified by the independent Epilepsy Study Consortium ( https://www.epilepsyconsortium.org/ ). Focal seizures progressing to bilateral tonic–clonic were classified as a focal seizures. Concomitant medications and therapies (e.g., occupational or physiotherapies) were recorded at the baseline visit, and any new medications or other therapies initiated during the study were recorded. Standardized rater training procedures were implemented to minimize interrater variability. Only raters with ≥10 administrations were selected and trained before being qualified for the study. As far as possible, the same rater and caregiver were asked to complete the scales for each participant throughout the study. Table S1 summarizes the schedule of assessments and visit windows. 2.3. Outcome measures In 2019, a disease conceptual model for CDD was developed to help identify meaningful clinical outcome measures. This model was informed by a qualitative literature review to generate insights on the core symptoms and their impact on patients and families (Figure S1 ). 14 We conducted endpoint selection workshops to evaluate potential clinical outcome measures previously used in clinical trials involving individuals with DEEs such as Rett syndrome or Angelman syndrome, as well as other neurodevelopmental disorders. The final prioritized measures were those whose concepts of interest (i.e., what is specifically measured by the scale) best aligned with key CDD domains. We explored communication, cognition, behavior, functioning level, cortical visual impairment (CVI), sleep quality, and QOL through nine scales described in Table S2 . The CVI‐range scale was used as an exploratory tool only, to investigate its performance in CDD patients. Growth scale values (GSVs) are available for the Vineland‐3 and the Bayley‐4 scales. GSVs were preferred over raw or standard scores, as they reflect the examinee's absolute rather than relative performance level. Raw scores are not on equal interval scales, making a growth or change evaluation challenging. Comparing standard scores over time indicates whether the examinee has improved faster than, slower than, or at the same rate as their peers in the normative sample. Comparing GSV over time indicates whether the examinee's performance, or skill level, has changed relative to their prior performance and is more suitable for clinical trials. 2.4. Statistical methods Statistical analyses were performed using data from all enrolled patients. Data were descriptively summarized and stratified by age groups (0–2, 3–5, 6–12, and ≥13 years.) Analyses were performed using R version 4.2.1 (or higher). 3. RESULTS The first patient was enrolled in September 2022; 80% of the sample size was recruited between August and November 2023. 3.1. Baseline characteristics We obtained informed consents from the parents of 112 CDD patients; all were enrolled. However, one site had to be closed for quality concerns and its participant excluded from the analyses. Of the 111 subjects, 103 were females (93%). The median age was 8.3 years (range = 2 months to 28 years; 10 patients were ≥18 years old). The median age at seizure onset was 1.5 months (range = 0–66 months). Patients used a mean (SD) of 2.6 (1.19) antiseizure medications (ASMs), with no significant differences among the four age groups. The three most frequently used ASMs were valproate (18%), clobazam (15%), and cannabidiol or vigabatrin (10% each); 16% were on a ketogenic diet at enrollment (Table 1 ). TABLE 1. Demographics. Characteristic Age at enrollment, years:months Total, n = 111 0:0 to 2:11, n = 22 3:0 to 5:11, n = 26 6:0 to 12:11, n = 35 13:0 to 45:11, n = 28 Age at enrollment, years Mean 1.4 4.1 8.8 17.1 8.3 Median 1 4 9 16 7 Range <1–2 3–5 6–12 13–28 <1–28 Sex, n (%) Male 0 (0) 1 (7) 2 (6) 5 (18) 7% Female 22 (100) 25 (93) 33 (94) 23 (82) 93% First seizure onset, months n – – – – 110 Mean 3.4 Median 1.5 Range 0–66 Caregiver impression of severity Mean 4.3 4.6 4.8 4.8 4.6 Median 4.5 4.5 5.0 5.0 5.0 Range 2–7 2–7 2–7 1–7 1–7 Ketogenic diet, n (%) 3 (14) 5 (19) 5 (14) 5 (17) 18 (16) Antiseizure medications at screening a Number of patients with ASMs 21 26 33 26 106 Mean per patient (SD) 3.1 (1.37) 2.4 (1.06) 2.5 (1.03) 2.6 (1.30) 2.6 (1.19) Minimum to maximum 1–6 1–4 1–4 1–5 1–6 Open in a new tab a Excluding rescue or pro re nata medications. Parents rated their children's severity with the Caregiver Global Impression‐Severity scale. Median scores indicated “significant impairment,” with severity trending higher in older age groups. 3.2. Seizures Seizure diaries from 82 patients were available; Table 2 summarizes the seizure types and frequencies over 28 days (Figure 1 ). TABLE 2. Twenty‐eight‐day seizure types by age group (%). Seizure type 0–2, years, n = 18 3–5, years, n = 16 6–12, years, n = 25 ≥13, years, n = 23 Total, n = 82 Major motor a 23.4 47.7 40.1 23.6 33.1 Spasms 49.4 22.1 21.6 21.0 28.3 Focal 1.0 – 15.9 18.5 10.3 Myoclonic 11.9 16.1 7.6 4.5 9.3 Atonic 6.5 6.7 11.7 10.2 9.1 Absence – 7.2 3.0 15.3 6.1 Other 3.8 .9 – 6.8 2.8 Tonic/atonic [unknown] 4.0 – .1 – 1.0 Seizure‐free patients, n 0 2 2 2 6 Open in a new tab a Includes clonic bilateral, tonic bilateral, generalized tonic–clonic. FIGURE 1. Open in a new tab Twenty‐eight‐day seizure frequency. Boxplot shows counts for all seizure types per age group over the first 28 days after the baseline visit. The medians vary from 25 (13+ years) to circa 100 (3–5 years) seizures per 28 days. Six patients were seizure‐free, none before age 3 years. The median seizure count ranged from 25 to 100 seizures within 28 days, corresponding to 1–5 daily. The most frequent seizure types were major motor seizures (33%), epileptic spams (28%), and focal seizures (10%). Seizure patterns varied by age, with spasms (including infantile spasms) predominant in younger children. Myoclonic seizures comprised 12%–16% of seizures before age 6 years and declined to 8% in the 6–12‐year and 4.5% in the ≥13‐year age groups. Focal seizures (i.e., focal to bilateral tonic–clonic, focal motor, or nonmotor) increased with age, being infrequent before age 6 years and comprising 16%–18.5% after age 6 years. 3.3. Co‐occurring conditions Gastrointestinal, neurologic, and neuropsychiatric/behavioral disorders were the most frequent types of comorbidities reported. Hypomotility comprised 60% of the gastrointestinal problems. Beyond core symptoms of seizures and intellectual disability, muscle tone abnormalities were reported in 40% of concurrent neurologic disorders, followed by impaired coordination and dyskinesias (27%). Sleep disorders (i.e., mainly disturbances in initiating and maintaining sleep) were reported in 39% and stereotypies in 20% (classified as neuropsychiatric and behavioral disorders; data not shown). 3.4. Gross Motor Function Measure‐88 We administered the Gross Motor Function Measure‐88 (GMFM‐88) in 110 participants at baseline. Table 3 summarizes the GMFM level achievements for each age group. Up to age 12 years, most of the GMFM scores were at floor for the crawling, standing, and walking GMFM levels. In the youngest age group, only six patients (27%) achieved the lying level and three (14%) the sitting level. Overall, only six participants (5%) achieved 70%–100% for the walking (i.e., close to normal walking), jumping, or running abilities. TABLE 3. Gross Motor Function Measure achievements level by age group (%). Age range Domain Floor, score = 0 Impaired, score < 70 Achieved, score ≥ 70 0–2 years, n = 22 Lying 0 73 27 Sitting 18 68 14 Crawling 64 27 9 Standing 86 14 0 Walking 95 5 0 3–5 years, n = 25 Lying 0 40 60 Sitting 0 56 44 Crawling 36 48 16 Standing 48 48 4 Walking 60 36 4 6–12 years, n = 35 Lying 3 54 43 Sitting 11 57 31 Crawling 54 31 14 Standing 57 34 9 Walking 60 31 9 13–55 years, n = 28 Lying 0 39 61 Sitting 0 54 46 Crawling 46 36 18 Standing 39 47 14 Walking 25 68 7 Open in a new tab 3.5. Bayley‐4 GSVs did not differ between age groups for the cognition, receptive/expressive language, and fine motor subdomains, with mean scores of approximately 475 (age equivalent = ~6 months). Gross motor scores were higher in the oldest group, suggesting improvements over time (Table 4 , Figure S2A ). TABLE 4. Scale summary statistics by age group. Scale Age at enrollment, years:months 0:0 to 2:11, n = 22 3:0 to 5:11, n = 26 6:0 to 12:11, n = 35 13:0 to 45:11, n = 28 Total, n = 111 Caregiver GI‐S n 22 26 35 28 111 Mean (SD) 4.3 (1.6) 4.6 (1.5) 4.8 (1.5) 4.8 (1.7) 4.6 (1.6) Minimum–maximum 2–7 2–7 2–7 1–7 1–7 SDSC score, % Total n 22 26 35 28 111 Mean (SD) 16.8 (7.2) 17.7 (11.0) 17.9 (10.2) 20.5 (10.8) 18.3 (10.0) Minimum–maximum 4–27 3–47 4–46 6–51 3–51 Arousal n 22 26 35 28 111 Mean (SD) 1.9 (4.4) 11.3 (18.4) 10.3 (18.6) 6.5 (15.3) 7.9 (16.0) Minimum–maximum 0–17 0–67 0–75 0–67 0–75 Excessive somnolence n 22 26 35 28 111 Mean (SD) 21.6 (13.3) 21.3 (20.8) 30.0 (23.9) 35.6 (18.6) 27.9 (20.8) Minimum–maximum 0–45 0–70 0–80 0–70 0–80 Hyperhidrosis n 22 26 35 28 111 Mean (SD) 17.6 (23.4) 9.1 (16.1) 5.7 (13.0) 4.0 (10.3) 8.4 (16.3) Minimum–maximum 0–75 0–50 0–50 0–38 0–75 Sleep/awake n 22 26 35 28 111 Mean (SD) 22.5 (14.8) 27.6 (17.9) 24.1 (16.5) 28.5 (18.1) 25.8 (16.9) Minimum–maximum 0–54 8–67 0–67 0–71 0–71 Sleep breathing n 22 26 35 28 111 Mean (SD) 13.6 (21.9) 6.4 (8.9) 7.1 (12.5) 9.5 (10.3) 8.8 (13.8) Minimum–maximum 0–100 0–25 0–50 0–33 0–100 Sleep initiation n 21 26 35 28 111 Mean (SD) 33.3 (15.2) 28.4 (16.9) 32.2 (19.5) 37.2 (21.1) 32.7 (18.5) Minimum–maximum 7–68 4–86 0–82 11–96 0–96 Bayley‐4, GSV Cognition n 21 23 32 22 98 Mean (SD) 465 (13.5) 479 (24.3) 472 (20.6) 472 (14.7) 472 (19.4) Minimum–maximum 445–496 439–531 446–526 449–495 439–531 Receptive language n 21 23 31 22 97 Mean (SD) 468 (10.0) 483 (21.6) 471 (15.2) 477 (14.9) 475 (16.7) Minimum–maximum 455–490 446–538 446–519 453–509 439–538 Expressive language n 21 23 32 22 98 Mean (SD) 472 (10.9) 487 (19.2) 476 (19.1) 480 (15.4) 479 (17.5) Minimum–maximum 455–490 455–537 450–531 455–529 450–537 Fine motor n 21 23 31 22 97 Mean (SD) 466 (13.2) 480 (25.9) 472 (23.2) 477 (17.4) 474 (21.2) Minimum–maximum 449–496 437–533 437–523 451–507 437–533 Gross motor n 20 23 31 22 96 Mean (SD) 475 (12.5) 492 (20.3) 478 (21.9) 495 (12.7) 485 (19.7) Minimum–maximum 451–510 441–531 441–522 477–521 441–531 Bayley‐4, raw Cognition n 21 23 32 22 98 Mean (SD) 18.3 (15.6) 40.4 (39.1) 28.7 (32.1) 25.7 (18.7) 28.6 (29.3) Minimum–maximum 2–59 1–134 2–124 3–58 1–134 Receptive language n 21 23 31 22 97 Mean (SD) 13.4 (7.0) 24.3 (16.6) 15.8 (10.7) 19.0 (9.9) 18.0 (12.1) Minimum–maximum 4–26 1–74 1–53 4–42 1–74 Expressive language n 21 23 32 22 98 Mean (SD) 8.9 (4.7) 19.0 (16.0) 12.7 (13.7) 13.7 (11.7) 13.6 (12.8) Minimum–maximum 2–18 2–71 1–63 3–61 1–71 Fine motor n 21 23 31 22 97 Mean (SD) 12.6 (10.0) 25.3 (21.0) 18.7 (18.7) 21.7 (13.9) 19.6 (17.1) Minimum–maximum 2–37 0–75 0–65 3–48 0–75 Gross motor n 20 23 31 22 96 Mean (SD) 28 (17.6) 54.6 (27.8) 35.9 (29.2) 58.3 (18.0) 43.9 (27.1) Minimum–maximum 2–77 0–109 0–95 27–92 0–109 Bayley‐4, AE‐months Cognition n 21 23 32 22 98 Mean (SD) 2.8 (2.6) 7.9 (9.7) 5.4 (7.6) 4.2 (3.3) 5.1 (6.8) Minimum–maximum .7–10.0 .7–34.0 .7–30.0 .7–10.0 .7–34.0 Receptive language n 21 23 31 22 97 Mean (SD) 2.8 (2.1) 8.8 (9.1) 4.1 (4.9) 5.3 (4.7) 5.1 (6.1) Minimum–maximum .7–8.0 .7–38.0 .7–25 .7–19.0 .7–38.0 Expressive language n 21 23 32 22 98 Mean (SD) 3.7 (2.8) 9.4 (9.1) 5.8 (7.1) 6.2 (6.1) 6.3 (6.9) Minimum–maximum .7–9.0 .7–41.3 .7–31.0 .7–30.0 .7–41.3 Fine motor n 21 23 32 22 97 Mean (SD) 2.8 (2.4) 8.2 (9.6) 5.4 (7.2) 5.2 (3.9) 5. (6.7) Minimum–maximum .7–9.0 .7–38.0 .7–30.0 .7–16.0 .7–38.0 Gross motor n 20 23 32 22 96 Mean (SD) 4.9 (3.0) 10.9 (8.3) 6.6 (6.1) 10.2 (4.4) 8.1 (6.3) Minimum–maximum .7–15.0 .7–38.0 .7–24.0 4.7–22.0 .7–38.0 Vineland‐3, GSV Receptive language n 22 26 35 27 110 Mean (SD) 36 (16.0) 63 (25.8) 59 (24.3) 65 (24.3) 57 (25.3) Minimum–maximum 10–69 10–117 10–119 10–128 10–128 Expressive language n 22 26 35 27 110 Mean (SD) 57 (13.1) 77 (28.4) 62 (28.1) 71 (27.6) 67 (26.4) Minimum–maximum 35–85 29–162 10–138 10–139 10–162 Written n 17 25 35 27 104 Mean (SD) 12 (6.5) 17 (13.8) 16 (12.3) 20 (16.5) 16 (13.2) Minimum–maximum 10–36 10–65 10–56 10–71 10–71 Personal care n 22 26 35 27 110 Mean (SD) 54 (14.0) 63 (23.7) 56 (25.9) 69 (22.8) 61 (23.1) Minimum–maximum 10–75 10–106 10–108 10–109 10–109 Domestic n 17 26 35 27 105 Mean (SD) 11 (4.1) 14 (10.7) 15 (10.7) 13 (8.2) 13 (9.3) Minimum–maximum 10–27 10–54 10–61 10–44 10–61 Community n 17 26 35 27 105 Mean (SD) 12 (6.5) 15 (10.8) 14 (7.6) 18 (12.1) 15 (9.7) Minimum–maximum 10–37 10–47 10–34 10–46 10–47 Interpersonal relationships n 22 26 35 27 110 Mean (SD) 45 (14.3) 61 (20.7) 56 (17.6) 66 (16.5) 57 (18.7) Minimum–maximum 10–68 10–106 10–95 42–107 10–107 Play and leisure skills n 22 26 35 27 110 Mean (SD) 33 (13.3) 55 (24.5) 43 (22.6) 50 (21.2) 46 (22.3) Minimum–maximum 10–58 10–108 10–96 10–99 10–108 Coping skills n 21 26 35 27 109 Mean (SD) 31 (11.3) 40 (12.7) 37 (12.0) 42 (12.3) 38 (12.5) Minimum–maximum 10–46 10–67 10–66 10–70 10–70 Fine motor n 22 26 34 27 109 Mean (SD) 27 (21.8) 56 (35.0) 45 (35.2) 66 (27.2) 49 (33.5) Minimum–maximum 10–72 10–123 10–161 10–126 10–161 Gross motor n 22 26 34 27 109 Mean (SD) 43 (18.2) 74 (36.9) 62 (41.0) 86 (36.5) 67 (38.0) Minimum–maximum 10–84 10–142 10–143 28–152 10–152 Vineland‐3, raw Receptive language n 22 26 35 27 110 Mean (SD) 4.4 (4.1) 17.6 (16;3) 15 (14.3) 18 (14.1) 14.2 (14.2) Minimum–maximum 0–16 0–60 0–62 0–69 0–69 Expressive language n 22 26 35 27 110 Mean (SD) 7.3 (3.0) 16.3 (17.8) 11 (11.9) 12 (12.3) 11.7 (12.8) Minimum–maximum 3–14 2–87 0–56 0–58 0–87 Written n 17 25 35 27 104 Mean (SD) .1 (.3) 1.7 (4.4) 1.3 (3.3) 2.4 (5.0) 1.5 (3.9) Minimum–maximum 0–1 0–18 0–14 0–21 0–21 Personal care n 22 26 35 27 110 Mean (SD) 6.6 (3.4) 13 (13.9) 9.8 (9.5) 14 (10.9) 11 (10.5) Minimum–maximum 0–13 0–58 0–46 0–48 0–58 Domestic n 17 26 35 27 105 Mean (SD) .1 (.5) 1.0 (4.3) 1.3 (5.4) .5 (2.1) .8 (3.9) Minimum–maximum 0–2 0–22 0–32 0–11 0–32 Community n 17 26 35 27 105 Mean (SD) 0 (.0) 1.4 (3.4) .6 (1.4) 2.3 (4.8) 1.1 (3.2) Minimum–maximum 0–0 0–13 0–5 0–16 0–16 Interpersonal relationships n 22 26 35 27 110 Mean (SD) 9.9 (6.6) 20 (14.6) 16 (10.2) 22 (13.0) 17 (12.2) Minimum–maximum 0–22 0–60 0–45 7–62 0–62 Play and leisure skills n 22 26 35 27 110 Mean (SD) 3.1 (1.9) 12 (13.7) 6.3 (7.8) 7.7 (8.4) 7.3 (9.4) Minimum–maximum 0–7 0–50 0–35 0–39 0–50 Coping skills n 21 26 35 27 109 Mean (SD) 3.9 (2.8) 8.7 (8.0) 6.9 (5.9) 10 (7.8) 7.5 (6.8) Minimum–maximum 0–11 0–36 0–34 0–39 0–39 Fine motor n 22 26 34 27 109 Mean (SD) 2.7 (4.1) 11 (11.4) 8.5 (14.8) 12 (9.7) 8.6 (11.6) Minimum–maximum 0–12 0–37 0–80 0–40 0–80 Gross motor n 22 26 34 27 109 Mean (SD) 6.3 (7.3) 21 (20.2) 16 (19.3) 25 (20.4) 17 (19.0) Minimum–maximum 0–33 1–67 0–68 2–75 0–75 ABC‐C, % Hyperactivity n 22 26 35 28 111 Mean (SD) 12.8 (15.5) 21.9 (19.7) 22.7 (19.9) 20.3 (19.3) 19.9 (19.0) Minimum–maximum 0–50 0–67 0–69 0–60 0–69 Inappropriate speech n 22 26 35 28 111 Mean (SD) 3.1 (7.7) 9.6 (15.2) 11.7 (20.8) 12.5 (19.9) 9.8 (17.6) Minimum–maximum 0–25 0–50 0–83 0–75 0–83 Irritability n 22 26 35 28 111 Mean (SD) 8.9 (11.2) 17.5 (15.2) 21.0 (23.2) 20.8 (22.4) 17.8 (19.7) Minimum–maximum 0–29 0–49 0–76 0–76 0–76 Lethargy n 22 26 35 28 111 Mean (SD) 18.1 (17.2) 16.8 (20.0) 20.1 (15.5) 23.1 (19.9) 19.7 (18.0) Minimum–maximum 0–54 0–71 0–54 0–65 0–71 Stereotypy n 22 26 35 28 111 Mean (SD) 25.6 (26.1) 27.4 (23.5) 29.3 (24.3) 36.8 (24.3) 30.1 (24.5) Minimum–maximum 0–76 0–76 0–86 0–100 0–100 GMFM‐88, % Lying n 22 26 35 28 111 Mean (SD) 56.3 (29.1) 71.8 (27.8) 59.7 (33.9) 74.3 (25.1) 65.6 (30.1) Minimum–maximum 1–100 0–100 0–100 6–100 0–100 Sitting n 22 26 35 28 111 Mean (SD) 24.3 (29.4) 50.9 (34.1) 45.7 (38.6) 65.1 (26.6) 47.6 (35.4) Minimum–maximum 0–100 0–100 0–100 8–100 0–100 Crawling n 22 26 35 28 111 Mean (SD) 11.0 (25.3) 25.8 (30.9) 27.3 (34.7) 28.8 (35.2) 24.0 (32.5) Minimum–maximum 0–93 0–100 0–100 0–100 0–100 Standing n 22 26 35 28 111 Mean (SD) 3.5 (14.7) 19.0 (25.5) 19.8 (32.2) 28.7 (32.7) 18.5 (29.0) Minimum–maximum 0–69 0–87 0–100 0–100 0–100 Walking n 22 26 35 28 111 Mean (SD) 1.5 (6.8) 14.4 (23.2) 17.3 (29.8) 24.1 (26.2) 15.1 (25.1) Minimum–maximum 0–32 0–71 0–100 0–100 0–100 CVI‐Range, % n 22 26 35 28 111 Mean (SD) 40 (13.1) 32 (15.2) 38 (12.7) 40 (14.7) 37 (14.1) Minimum–maximum 23–59 14–64 9–68 9–73 9–73 QI‐Disability (%) Total n 22 26 34 28 110 Mean (SD) 53 (15.2) 64 (18.7) 62 (14.1) 64 (16.0) 61 (16.4) Minimum–maximum 25–80 24–92 30–85 28–97 24–97 Health well‐being n 22 26 34 28 110 Mean (SD) 66 (13.5) 70 (20.4) 73 (17.3) 71 (17.4) 70 (17.4) Minimum–maximum 38–94 44–100 25–100 38–100 19–100 Independence n 22 26 34 28 110 Mean (SD) 22 (16.9) 40 (27.3) 35 (25.9) 43 (24.2) 36 (25.2) Minimum–maximum 0–65 5–95 0–100 5–95 0–100 Leisure n 22 26 34 28 110 Mean (SD) 50 (25.9) 74 (23.4) 67 (24.5) 68 (23.4) 65 (25.3) Minimum–maximum 0–90 20–100 5–100 15–100 0–100 Negative emotion n 22 26 34 28 110 Mean (SD) 78 (13.1) 72 (15.0) 75 (22.5) 76 (17.9) 75 (17.9) Minimum–maximum 57–100 29–96 25–100 36–100 25–100 Positive emotion n 22 26 34 28 110 Mean (SD) 57 (28.8) 69 (26.6) 65 (23.1) 66 (22.2) 65 (25.0) Minimum–maximum 0–100 19–100 19–100 25–100 0–100 Social interaction n 22 26 34 28 110 Mean (SD) 43 (28.3) 61 (29.8) 58 (26.8) 59 (23.2) 56 (27.4) Minimum–maximum 0–89 11–100 0–96 21–100 0–100 SF‐12 HS, normed Mental component n 22 25 32 28 107 Mean (SD) 41 (10.0) 46 (9.4) 45 (9.6) 46 (8.6) 45 (9.4) Minimum–maximum 17–57 31–62 23–68 28–63 17–68 Physical component n 22 25 32 28 107 Mean (SD) 53 (12.5) 55 (6.9) 50 (8.3) 52 (7.9) 52 (9.0) Minimum–maximum 22–64 36–68 30–64 30–65 22–68 Open in a new tab Abbreviations: ABC‐C, Aberrant Behavior Checklist; AE, Age Equivalent; Caregiver GI‐S, Caregiver Global Impression‐Severity; CVI, cortical visual impairment; GMFM‐88, Gross Motor Function Measure‐88; GSV, growth scale value; QI‐Disability, Quality of Life Inventory–Disability; SF‐12 HS, Short Form 12 Health Survey; SDSC, Sleep Disturbance Scale for Children. 3.6. Vineland‐3 Figure 2 presents the individual GSVs in the Vineland‐3 subdomains. Very low scores were observed in the written, domestic, and community subdomains (floor effects). Consistent with the Bayley‐4, gross motor GSVs increased with age, with a mean of 43 for the youngest age group and 86 for the oldest (Table 4 ). Although the Bayley‐4 did not capture similar trends in other subdomains, the Vineland‐3 GSVs improved across age groups for the receptive language, interpersonal relationships, and fine motor subdomains. This supports some level of skills acquisition over time. FIGURE 2. Open in a new tab Vineland growth scale values (GSVs) by chronological age. Individual GSVs are shown per Vineland subdomain, with predicted GSVs (blue lines) and 95% confidence intervals (in gray). GSVs from three subdomains were at the floor for most participants (75% for written, 85% for domestic, and 75% for community). 3.7. Other scales The Sleep Disturbance Scale for Children (SDSC) indicated that sleep initiation, sleep/awake transitions, and excessive somnolence were the most impaired domains (i.e., higher scores). Sleep difficulties were consistent across ages and aligned with clinical history (Table 4 ). Mean Quality of Life Inventory–Disability (QI‐Disability) total score ranged from 53% to 64% (100% is best possible score), suggesting a major impact of the disease on participants' QOL, as seen by their parents. The least impacted subdomain was negative emotions, whereas the most impacted domain was independence, especially for the youngest age group (Table 4 ). 4. DISCUSSION The CANDID study is a large observational CDD trial conducted to industry quality standards and informed by a Critical Path Innovation Meeting with the US Food and Drug Administration in 2021. It provides the largest dataset to date evaluating motor, cognitive, and behavioral functions using available outcome measures in CDD. Baseline data from CANDID have already provided important insights into developmental trajectories across multiple domains and revealed strengths and limitations of selected tools. Table S3 summarizes the key findings for each outcome measure used in CANDID and preliminary evaluations for CDD clinical trials. The GMFM‐88 and the Bayley‐4 scales were the only assessments requiring direct participant involvement. GMFM‐88 showed floor effects in crawling, standing, and walking domains, across all age groups, particularly in the youngest. Although this aligns with CDD symptomatology, the scale lacks granularity in assessing patients' capacities and revealed operational limitations. For example, refusal to perform a task results in a “0” score, and subsequent tasks are not evaluated, even if the child can often perform them. These limitations led to its discontinuation in subsequent visits. A shorter version, the GMFM‐66, may be a more suitable alternative, using motor development milestones in addition to scale scores. This version allows for continued assessments even when a score of “0” is recorded, and has been used in other DEEs (e.g., STXBP1‐ and SYNGAP1‐related disorders 15 ), although many of these children are less severely impaired. The Bayley‐4 is validated for children up to 42 months, but given the level of impairment in CDD, the scale was administered regardless of participants' age. Analyses of raw scores and GSVs showed no floor or ceiling effects. However, scores remained within the same range across age groups, indicating limited skills development but no regression, particularly in gross motor function. GSVs are developmental scores that provide a way to track change over time within each domain. Unlike scaled or composite scores, GSVs are not age‐normed, meaning they reflect raw developmental progress rather than a comparison to age‐matched peers. This makes GSVs particularly useful for monitoring longitudinal changes, either age‐related growth and/or changes after treatment. Bayley‐4 GSVs generally start in the 400s for infants and exceed 550 for older toddlers. None of our participants reached this level. Similar domains were assessed using the Vineland‐3. The most impacted subdomains were written, domestic, and community, all of which showed floor effects. Given the known severity of impairment in this population, these results were expected. The remaining eight subdomains showed reasonable variability in GSVs, with observable age‐related skill development, particularly in receptive language, interpersonal relationships, and fine and gross motor subdomains. However, performance remained well below that of the typically developing peers; by age 18 years, GSVs would generally be expected to approach 200. Very similar results were observed in a recent study assessing psychometric properties of Vineland‐3 GSVs for clinical trial readiness in SCN2A. 16 Floor effects were also observed on the same subdomains, and the results provide strong evidence for the psychometric properties in SCN2A. The Vineland‐3 and Bayley‐4 scales are being evaluated as potential clinical outcome measures for DEEs through the Inchstone Project. 17 In CANDID, the semistructured interview form from the most recent version of the scale was used. Vineland‐3 allows derivation of GSVs, offering better granularity and potential sensitivity to change in future clinical trials. A previous prospective CDD study that included more than one time point described participant's trajectories in disease severity and QOL, identifying both developmental improvements and regressions. 18 That study, however, was limited to participants younger than 18 years and lacked standardized follow‐up time points. In contrast, we did not observe regressions in Vineland/Bayley or QOL scores with age within our cohort. This observation, however, will need to be confirmed with subsequent longitudinal data over the planned 3 years. Sleep disturbances in CDD can result from the disease itself, nocturnal seizures, or the effects of ASMs. Across all age groups, sleep initiation (also called “disorder of maintaining sleep”) was found to be the most impacted domain. This finding was also supported by the medical history evaluations. Our SDSC data confirmed previous findings by Downs et al. in a big cohort of 129 children with CDD aged >3 years. 19 Variability in SDSC scores was low, indicating that even small improvements in sleep following treatment could be detected, particularly when analyzing subdomains. Assessing the QOL of both patients and parents/caregivers is essential in drug development. Due to the severity of neurodevelopmental delays in CDD, patients' QOL can only be evaluated through parental or other caregivers' (e.g., therapists) perception. The QI‐Disability questionnaire revealed a significant impact on overall QOL, especially in the independence component where the lowest scores were found for completing routine activities (e.g., dressing, feeding), enjoying making things with hands, and enjoying using technology (e.g., computers, tablets). Variability in scores was low, with the potential for capturing overall improvements in QOL. The pattern and scores collected in CANDID closely matched prior reports, with total scores ranging between 50% and 70%, 19 , 20 also confirming that independence was the most impacted subdomain. Seizures were typically observed shortly after birth, with a median age of 6 weeks, in line with previous findings. 4 , 21 Infantile spasms counted for approximately 50% of the seizures by 2 years of age, whereas epileptic spasms remained at approximately 20% beyond 2 years. This partial persistence of epileptic spasms may represent a CDD clinical feature. 8 , 10 A similar pattern has also been described in a STXBP1‐related disorder natural history study, where most individuals presented with neonatal or early infantile seizures prior to spasms onset. 22 Consistent with other CDD cohorts, infantile spasms and myoclonic seizures represented more than half of all seizure types observed in infancy. In our cohort, the frequency of myoclonic seizures decreased with age, unlike the findings of Demarest et al. 10 , who observed myoclonic seizures in 39% of participants at any time point. However, a long‐term, longitudinal, electroclinical study of 22 CDD cases also demonstrated a diminution of myoclonic seizures over time, 23 with a median age at onset of 3 years. 24 Overall, seizure burden was highest in the 3–5‐year and 6–12‐year age groups, primarily driven by major motor seizures, including bilateral clonic or tonic and generalized tonic–clonic types (data not shown). Our data are consistent with other reports showing a high seizure burden during early and middle childhood, followed by a reduced burden. Our data also confirm observations from a recent review by Hong et al. regarding current treatment use for CDD, with valproate, clobazam, cannabidiol, and vigabatrin being among the most used ASMs. 25 , 26 Ganaxolone was approved as adjunctive treatment of epileptic seizures associated with CDD by the US and European health authorities in 2022 and 2023, respectively. However, only six participants (5%), primarily from the United States, were receiving ganaxolone at the screening visit, which occurred shortly after the approval for the European participants. Evaluating the continued prescription of these ASMs throughout the CANDID study duration will be of interest. Seizure frequency was variable (six participants were seizure‐free, without ongoing ASMs, none with ketogenic diet), yet the vast majority of the participants experienced multiple seizures per day, with no significant differences across age groups. This characteristic makes CDD an ideal DEE for using seizure‐related outcome measures in clinical trials, particularly when compared to more seizure‐heterogenous DEEs such as SYNGAP1‐related disorders (where seizures like absence seizures and eyelid myoclonia are more difficult to quantify 27 ) or DEEs in which many patients outgrow epilepsy, such as STXBP1‐ 28 and KCNQ2‐related disorders. 29 As an X‐linked dominant condition, CDD is more frequently observed in females, with a varying report of female‐to‐male ratio ranging from 4:1 up to 12:1. 30 Our cohort confirmed the higher end of this range, with a ratio of 103:8 (approximately 12:1). Baseline characteristics and comorbid conditions were consistent with previous reports on CDD, including gastrointestinal issues, muscle tone abnormalities, and sleep disorders. 1 , 4 Our study had several limitations. Some parents reported that visual impairments may have affected their child's task performance. We acknowledge that CVI severity was not considered when interpreting GMFM or Bayley‐4 scores. Currently, there are no gold standard tools to assess CVI; severity is typically evaluated using a combination of parental report functional abilities, neurologist's physical examination, and ophthalmological assessment. The development and validation of novel parent‐reported measures as proposed by the Inchstone Project, or systematic use of visual evoked potential in clinical trials, may help to better capture the impact of CVI. Most measures relied on caregiver‐reported outcome measures. Given the severe disability level associated with CDD—where nearly all patients are nonverbal or minimally verbal and unable to use assistive communication devices—parental input remains essential. Although direct evaluation of the motor function is feasible, identifying a valid and reliable alternative to the GMFM‐88 remains a priority. We were unable to identify a communication‐specific measure. The Observer‐Reported Communication Ability measure, recently developed for Angelman syndrome, and validated in Rett syndrome as well, has demonstrated suitable psychometrics properties and could potentially be used in CDD. 31 Additionally, 26% of seizure diaries were missing after 1 year of study conduct. Because of the high data entry burden, some sites deprioritized seizure data entry in this noninterventional study, leading to delays and gaps in data collection. Nonetheless, the 82 completed diaries were considered adequate to represent the study population. 5. CONCLUSIONS The CANDID study successfully recruited 112 participants in approximately 1 year and is currently collecting 3 years of prospective data. CANDID has already provided essential operational information, including an assessment of access to patient populations in different countries. Baseline data analyses demonstrated the feasibility and relevance of most selected outcome measures for use in CDD. However, the GMFM‐88 presented some limitations, making it unsuitable for CDD clinical trials. Overall, the scales effectively captured disease‐related deficits and the variability of the phenotype across different ages. Further exploration of correlations between the outcome measures may help confirm their suitability and clarify the impact of seizure profiles (e.g., atonic seizure frequency increasing with motor functions acquisition). AUTHOR CONTRIBUTIONS Xavier Liogier d'Ardhuy: Conceptualization (equal); writing—original draft (lead); data curation (lead); study design (supporting). Tricia Cimms: Study design (supporting); writing—review and editing (equal); data curation (supporting). Kristina Lindsten: Writing—review and editing (equal). Marco Rizzo: Writing—review and editing (equal), study design (equal). Alison Skrinar: Conceptualization (lead); writing—review and editing (equal); study design (equal). Peter St. Wecker: Writing—review and editing (equal), study design (equal). Ana Mingorance: Conceptualization (lead); writing—review and editing (equal); study design (lead). Orrin Devinsky: Writing—original draft (equal); writing—review and editing (equal); study design (supporting). FUNDING INFORMATION This work was funded by the CANDID precompetitive consortium (i.e., CANDID partners): Marinus Pharmaceuticals (now an indirect wholly owned subsidiary of Immedica Pharma AB), Ultragenyx Pharmaceutical, Amicus Therapeutics, PTC Therapeutics, Elaaj Bio, Biogen, and Zogenix (now UCB). CONFLICT OF INTEREST STATEMENT X.L.A. has received honoraria for presenting at educational events, serving on advisory boards, and consultancy work for the Loulou Foundation, Stalicla, Aelis Pharma, and Perha Pharma. T.C. is a full‐time employee of Ultragenyx Pharmaceutical. K.L. is a full‐time employee of Immedica Pharma AB and holder of equity in the company. M.R. is a full‐time employee of Biogen. A.S. is a full‐time employee of Ultragenyx Pharmaceutical. P.S.W. is a full‐time employee of UCB Pharma. A.M. has served as a paid consultant for Angelini Pharma, Biocodex, Encoded Therapeutics, Flux Therapeutics, Hoffmann‐La Roche, Merck Healthcare, Stoke Therapeutics, Tevard Bio, and Xenon Pharmaceuticals. OD has received grant support from the NINDS, NIMH, CDC, and NSF. He has equity and/or has received compensation from the following companies: Tevard Biosciences, Regel Biosciences, Praxis Precision Therapeutics, Script Biosciences, Actio Biosciences, Empatica, Ajna Biosciences, Blackrock Neurotech, Tennex, and California Cannabis Enterprises. He has received consulting fees from Emotiv, UCB Pharma, and Stoke Therapeutics. He holds patents in molecular biology, cannabis medicine, and biotechnology. He is the managing partner of PhiFund Ventures. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Supporting information Data S1. EPI-67-1703-s001.docx (1.5MB, docx) ACKNOWLEDGMENTS The authors thank the participants and their families for their courage and dedication. We also thank the site investigators and their teams who participate in the CANDID study. We thank key former members of CANDID partners: Alex Aimetti, PhD, Alessia di Nardo, PhD, and Gail Farfel, PhD. We thank Dr. Jacqueline French and the Epilepsy Study Consortium for their support during the conduct of the study. We thank Maria Makaroskaya for her operational oversight. We would also like to thank the members of the first “primary endpoint workshop” for their fruitful and helpful discussions: Dimitrios Arkilo, MD, Jess Conicelli, BS, BA, Paola Daly, MHS, Lisa Kammerman, PhD, Omar Khwaja, MD, PhD, Daniel Lavery, PhD, Mei Lu, MS, Andrew Mulberg, MD, Asif Paker, MD, Allen Reha, MS, Christina Theodore‐Oklota, PhD, Pamela Ventola, PhD, and Celia Zinger, MD. Liogier d’Ardhuy X, Cimms T, Lindsten K, Rizzo M, Skrinar A, St. Wecker P, et al. Baseline characteristics and feasibility of clinical outcome measures in CDKL5 deficiency disorder: The CANDID observational study. Epilepsia. 2026;67:1703–1721. 10.1002/epi.70095 Social Media and Article Promotion: The CANDID study evaluates neurocognitive, functional, and quality of life scales to optimize endpoints for CDKL5 deficiency disorder trials. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from CANDID partners. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors with the permission of CANDID partners. REFERENCES 1. Olson HE, Demarest ST, Pestana‐Knight EM, Swanson LC, Iqbal S, Lal D, et al. Cyclin‐dependent kinase‐like 5 deficiency disorder: clinical review. Pediatr Neurol. 2019;97:18–25. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Hector RD, Kalscheuer VM, Hennig F, Leonard H, Downs J, Clarke A, et al. CDKL5 variants. Neurol Genet. 2017;3(6):1–10. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732004/ [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. MacKay C, Wong K, Demarest S, Benke T, Downs J, Leonard H. Exploring genotype‐phenotype relationships in the CDKL5 deficiency disorder using an international dataset. Clin Genet. 2021;99(1):157–165. [ DOI ] [ PubMed ] [ Google Scholar ] 4. Mangatt M, Wong K, Anderson B, Epstein A, Hodgetts S, Leonard H, et al. Prevalence and onset of comorbidities in the CDKL5 disorder differ from Rett syndrome. Orphanet J Rare Dis. 2016;11:39. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Symonds JD, Zuberi SM, Stewart K, McLellan A, O'Regan M, MacLeod S, et al. Incidence and phenotypes of childhood‐onset genetic epilepsies: a prospective population‐based national cohort. Brain. 2019;142(8):2303–2318. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Laurvick CL, de Klerk N, Bower C, Christodoulou J, Ravine D, Ellaway C, et al. Rett syndrome in Australia: a review of the epidemiology. J Pediatr. 2006;148(3):347–352. [ DOI ] [ PubMed ] [ Google Scholar ] 7. Wu YW, Sullivan J, McDaniel SS, Meisler MH, Walsh EM, Li SX, et al. Incidence of Dravet syndrome in a US population. Pediatrics. 2015;136(5):1310–1315. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Bahi‐Buisson N, Kaminska A, Boddaert N, Rio M, Afenjar A, Gérard M, et al. The three stages of epilepsy in patients with CDKL5 mutations. Epilepsia. 2008;49(6):1027–1037. [ DOI ] [ PubMed ] [ Google Scholar ] 9. Benke TA, Demarest S, Angione K, Downs J, Leonard H, Saldaris J, et al. CDKL5 deficiency disorder. GeneReviews®. Seattle (WA): University of Washington, Seattle; 2024. Available from: https://www.ncbi.nlm.nih.gov/books/NBK602610/ [ Google Scholar ] 10. Demarest ST, Olson HE, Moss A, Pestana‐Knight E, Zhang X, Parikh S, et al. CDKL5 deficiency disorder: relationship between genotype, epilepsy, cortical visual impairment, and development. Epilepsia. 2019;60(8):1733–1742. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Scheffer I, Zuberi S, Mefford H, Guerrini R, McTague A. Developmental and epileptic encephalopathies. Nat Rev Dis Primers. 2024;10(1):61. Available from: https://pubmed.ncbi.nlm.nih.gov/39237642/ [ DOI ] [ PubMed ] [ Google Scholar ] 12. Fehr S, Downs J, Ho G, de Klerk N, Forbes D, Christodoulou J, et al. Functional abilities in children and adults with the CDKL5 disorder. Am J Med Genet A. 2016;170(11):2860–2869. [ DOI ] [ PubMed ] [ Google Scholar ] 13. Demarest S, Pestana‐Knight EM, Olson HE, Downs J, Marsh ED, Kaufmann WE, et al. Severity assessment in CDKL5 deficiency disorder. Pediatr Neurol. 2019;97:38–42. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Gibbons E, Sams L, Specchio N, Benke T, Cross JH, Utley K, et al. Development of a conceptual model for CDKL5 deficiency disorder. Default 2019. Available from: https://aesnet.org/abstractslisting/development‐of‐a‐conceptual‐model‐for‐cdkl5‐deficiency‐disorder 15. Pierce S. stxbp1 and syngap1 related disorders: assessing the feasibility of developmental outcome measures. Default 2024. Available from: https://aesnet.org/abstractslisting/stxbp1‐and‐syngap1‐related‐disorders‐assessing‐the‐feasibility‐of‐developmental‐outcome‐measures 16. Berg AT, Palac H, Wilkening G, Zelko F, Schust Meyer L. SCN2A‐developmental and epileptic encephalopathies: challenges to trial‐readiness for non‐seizure outcomes. Epilepsia. 2021;62(1):254–268. [ DOI ] [ PubMed ] [ Google Scholar ] 17. Hecker J, Conecker G, Chapman C, Hommer R, Ludwig NN, Sevinc G, et al. Patient‐advocate‐led global coalition adapting fit‐for‐purpose outcomes measures to assure meaningful inclusion of DEEs in clinical trials. Ther Adv Rare Dis. 2024;5:26330040241249762. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Leonard H, Junaid M, Wong K, Aimetti AA, Knight EP, Downs J. Influences on the trajectory and subsequent outcomes in CDKL5 deficiency disorder. Epilepsia. 2022;63(2):352–363. [ DOI ] [ PubMed ] [ Google Scholar ] 19. Downs J, Jacoby P, Saldaris J, Leonard H, Benke T, Marsh E, et al. Negative impact of insomnia and daytime sleepiness on quality of life in individuals with the cyclin‐dependent kinase‐like 5 deficiency disorder. J Sleep Res. 2022;31(5):e13600. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Leonard H, Whitehouse A, Jacoby P, Benke T, Demarest S, Saldaris J, et al. Quality of life beyond diagnosis in intellectual disability – latent profiling. Res Dev Disabil. 2022;129:104322. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Fehr S, Wong K, Chin R, Williams S, de Klerk N, Forbes D, et al. Seizure variables and their relationship to genotype and functional abilities in the CDKL5 disorder. Neurology. 2016;87(21):2206–2213. [ DOI ] [ PubMed ] [ Google Scholar ] 22. Thalwitzer KM, Xian J, de Campo D, Parthasarathy S, Magielski J, Sullivan KR, et al. Early life seizures and epileptic spasms in STXBP1‐related disorders. Epilepsia. 2024;65(3):805–816. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Darra F, Monchelato M, Loos M, Juanes M, Bernardina BD, Valenzuela GR, et al. CDKL5‐associated developmental and epileptic encephalopathy: a long‐term, longitudinal electroclinical study of 22 cases. Epilepsy Res. 2023;190:107098. [ DOI ] [ PubMed ] [ Google Scholar ] 24. Aledo‐Serrano A, Lewis‐Smith D, Leonard H, Bayat A, Junaid M, Hagebeuk E, et al. The natural history of CDKL5 deficiency disorder into adulthood. medRxiv ; 2025. p. 2025.01.12.24318239. Available from: https://www.medrxiv.org/content/10.1101/2025.01.12.24318239v1 25. Amin S, Majumdar A, Mallick AA, Patel J, Scatchard R, Partridge CA, et al. Caregiver's perception of epilepsy treatment, quality of life and comorbidities in an international cohort of CDKL5 patients. Hippokratia. 2017;21(3):130–135. [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Hong W, Haviland I, Pestana‐Knight E, Weisenberg J, Demarest S, Marsh E, et al. CDKL5 deficiency disorder‐related epilepsy: a review of current and emerging treatment. CNS Drugs. 2022;36(6):591–604. Available from: https://pubmed.ncbi.nlm.nih.gov/35633486/ [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Vlaskamp DR, Shaw BJ, Burgess R, Mei D, Montomoli M, Xie H, et al. SYNGAP1 encephalopathy: a distinctive generalized developmental and epileptic encephalopathy. Neurology. 2019;92(2):e96. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Freibauer A, Wohlleben M, Boelman C. STXBP1‐related disorders: clinical presentation, molecular function, treatment, and future directions. Genes. 2023;14(12):2179. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Miceli F, Soldovieri MV, Weckhuysen S, Cooper E, Taglialatela M. KCNQ2‐related disorders. GeneReviews®. Seattle: University of Washington; 2022. Available from: https://www.ncbi.nlm.nih.gov/books/NBK32534/ [ Google Scholar ] 30. Gürsoy S, Erçal D. Diagnostic approach to genetic causes of early‐onset epileptic encephalopathy. J Child Neurol. 2016;31(4):523–532. [ DOI ] [ PubMed ] [ Google Scholar ] 31. Reeve B, Lucas N, Chen D, McFatrich M, Jones H, Gordon K, et al. Validation of the observer‐reported communication ability (ORCA) measure for individuals with Rett syndrome. Eur J Paediatr Neurol. 2023;46:74–81. Available from: https://pubmed.ncbi.nlm.nih.gov/37536121/ [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Data S1. EPI-67-1703-s001.docx (1.5MB, docx) Data Availability Statement The data that support the findings of this study are available from CANDID partners. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors with the permission of CANDID partners. 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