Secondary use of patient data within decentralized studies using the example of rare diseases in Germany: A data scientist's exploration of process and lessons learned.

Objective: Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance. Rare diseases are an important prototype research focus for decentralized data analyses, as patients are rare by definition and adequate cohort sizes can only be reached if data from multiple sites is combined. Methods: Within the project "Collaboration on Rare Diseases", decentralized studies focusing on four rare diseases (cystic fibrosis, phenylketonuria, Kawasaki disease, multisystem inflammatory syndrome in children) were conducted at 17 German university hospitals. Therefore, a data management process for decentralized studies was developed by an interdisciplinary team of experts from medicine, public health and data science. Along the process, lessons learned were formulated and discussed. Results: The process consists of eight steps and includes sub-processes for the definition of medical use cases, script development and data management. The lessons learned include on the one hand the organization and administration of the studies (collaboration of experts, use of standardized forms and publication of project information), and on the other hand the development of scripts and analysis (dependency on the database, use of standards and open source tools, feedback loops, anonymization). Conclusions: This work captures central challenges and describes possible solutions and can hence serve as a solid basis for the implementation and conduction of similar decentralized studies.

Efficacy and safety of crisaborole ointment, 2%, in participants aged ≥45 years with stasis dermatitis: Results from a fully decentralized, randomized, proof-of-concept phase 2a study.

BACKGROUND: Crisaborole ointment, 2%, is a nonsteroidal topical phosphodiesterase 4 inhibitor approved for the treatment of mild-to-moderate atopic dermatitis. OBJECTIVE: To evaluate the efficacy and safety of crisaborole in stasis dermatitis (SD). METHODS: In this randomized, double-blind, vehicle-controlled, decentralized phase 2a study (NCT04091087), 65 participants aged ≥45 years with SD without active ulceration received crisaborole or vehicle (1:1) twice-daily for 6 weeks. The primary end point was percentage change from baseline in total sign score at week 6 based on in-person assessment. RESULTS: Crisaborole-treated participants had significantly reduced total sign score from baseline versus vehicle based on in-person (nondermatologist) assessment (-32.4% vs -18.1%, P = .0299) and central reader (dermatologists) assessment of photographs (-52.5% vs -10.3%, P = .0004). Efficacy according to success and improvement per Investigator's Global Assessment score and lesional percentage body surface area reached statistical significance based on central reader but not in-person assessments. Skin and subcutaneous tissue disorders were common all-causality treatment-emergent adverse events with crisaborole. LIMITATIONS: Small sample size and short treatment duration were key limitations. In-person assessment was not conducted by dermatologists. CONCLUSION: Crisaborole improved signs and symptoms of SD and was well tolerated. Central reader assessment represents a promising approach for siteless clinical research.

Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones.

Introduction: Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones. Methods: A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming "selfie" videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model's performance for MRD1 estimation was evaluated on a separate test fold from the study dataset. Results: On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was -0.256 mm; and 95% limits of agreement (LOA) were -0.214-1.768 mm. Model performance showed no improvement when test data were gated to exclude "poor" quality images. Conclusions: On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1.

The Impact of COVID-19 Lockdown on Adults with Major Depressive Disorder from Catalonia: A Decentralized Longitudinal Study.

The present study analyzes the effects of each containment phase of the first COVID-19 wave on depression levels in a cohort of 121 adults with a history of major depressive disorder (MDD) from Catalonia recruited from 1 November 2019, to 16 October 2020. This analysis is part of the Remote Assessment of Disease and Relapse-MDD (RADAR-MDD) study. Depression was evaluated with the Patient Health Questionnaire-8 (PHQ-8), and anxiety was evaluated with the Generalized Anxiety Disorder-7 (GAD-7). Depression's levels were explored across the phases (pre-lockdown, lockdown, and four post-lockdown phases) according to the restrictions of Spanish/Catalan governments. Then, a mixed model was fitted to estimate how depression varied over the phases. A significant rise in depression severity was found during the lockdown and phase 0 (early post-lockdown), compared with the pre-lockdown. Those with low pre-lockdown depression experienced an increase in depression severity during the "new normality", while those with high pre-lockdown depression decreased compared with the pre-lockdown. These findings suggest that COVID-19 restrictions affected the depression level depending on their pre-lockdown depression severity. Individuals with low levels of depression are more reactive to external stimuli than those with more severe depression, so the lockdown may have worse detrimental effects on them.

Efficacy of the Digital Therapeutic sinCephalea in the prophylaxis of migraine in patients with episodic migraine: study protocol for a digital, randomized, open-label, standard treatment controlled trial.

BACKGROUND: The German government implemented the Digital Healthcare Act in order to bring Digital Therapeutics into standard medical care. This is one of the first regulatory pathways to reimbursement for Digital Therapeutics (DTx). The Digital Therapeutic sinCephalea is intended to act as a prophylactic treatment of migraine by reducing the migraine days. For this, sinCephalea determines personalized nutritional recommendations using continuous glucose monitoring (CGM) data and enables the patients to follow a personalized low-glycemic nutrition. Migraine is a headache disorder with the highest socioeconomic burden. Emerging evidence shows that CGM-based personalized nutritional recommendations are of prophylactic use in episodic migraine. However, prospective data are yet missing to demonstrate clinical effectiveness. This study is designed to fill this gap. METHODS: Patients between 18 and 65 years of age with proven migraine and a minimal disease severity of 3 migraine days per month are included. After a 4-week baseline phase as a pre-study, patients are randomized to the DTx intervention or a waiting-list control. The objective of the study is to show differences between the intervention and control groups regarding the change of migraine symptoms and of effects of migraine on daily life. DISCUSSION: To our knowledge, this is the first systematic clinical trial with a fully digital program to enable patients with migraine to follow a personalized low-glycemic nutrition in order to reduce their number of migraine days and the migraine-induced impact on daily life. Designing a clinical study using a digital intervention includes some obstacles, which are addressed in this study approach. TRIAL REGISTRATION: German Registry of Clinical Studies (Deutsches Register Klinischer Studien) DRKS-ID DRKS00024657. Registered on March 8, 2021.

A Decentralized Study Setup Enables to Quantify the Effect of Polymerization and Linkage of α-Glucans on Post-Prandial Glucose Response.

The complexity of the carbohydrate structure is associated with post-prandial glucose response and diverse health benefits. The aim of this study was to determine whether, thanks to the usage of minimally invasive glucose monitors, it was possible to evaluate, in a decentralized study setup, the post-prandial glycemic response (PPGR) of α-glucans differing systematically in their degree of polymerization (DP 3 vs. DP 60) and in their linkage structure (dextrin vs. dextran). Ten healthy subjects completed a double-blind, randomized, decentralized crossover trial, testing at home, in real life conditions, four self-prepared test beverages consisting of 25 g α-glucan dissolved in 300 mL water. The incremental area under the curve of the 120 min PPGR (2h-iAUC) was the highest for Dextrin DP 3 (163 ± 27 mmol/L*min), followed by Dextrin DP 60 (-25%, p = 0.208), Dextran DP 60 (-59%, p = 0.002), and non-fully caloric Resistant Dextrin (-68%, p = 0.002). These results show that a fully decentralized crossover study can be successfully used to assess the influence of both polymerization and structure of α-glucans on PPGR.