Introduction
Methods
Aim of the trial
Study description and study design
Arms and interventions
Outcome measures
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Inflammatory Rasch-built Overall Disability Scale (I-RODS)
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Inflammatory Neuropathy Cause and Treatment (INCAT) disability score
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Grip strength
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Medical Research Council (MRC)-Sumscore
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Subjective occurrence of end-of-dose phenomena/wearing off
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Abbreviated World Health Organization Quality of Life (WHOQOL-BREF)
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Pittsburgh Sleep Quality Index (PSQI)
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Levels of sNfL
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Serum proteomics and metabolomics
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Wearing time of smartwatch (daily)
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Longitudinal development of activity parameter: step count, approximate distance traveled (meter), duration of soft, moderate, and intense activity (seconds) defined by Withings, the sum of all active time (seconds), approximate calories burned
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Longitudinal development of sleep parameter: time awake (seconds), number of times user woke up, time to sleep (seconds), total time in bed (seconds), total time asleep (seconds), ratio of sleep/ time in bed, time spent in bed before falling asleep (seconds), time awake after first falling asleep (seconds), Withings Sleep score defined by Withings
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Longitudinal development of cardiovascular parameters: average heart rate, maximal heart rate, minimum heart rate, time in light, moderate, intense, and maximal heart rate zone (seconds) defined by Withings
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Light heart rate zone: from 0% inclusive to 50% exclusive of maximum heart rate. Moderate heart rate zone: from 50% included to 70% excluded of maximal heart rate. Intense heart rate zone: from 70% included to 90% excluded of maximal heart rate. Maximal heart rate zone: from 90% included to 100% included of maximal heart rate.
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Number and time of irregular 1-channel ECGs (according to Withings algorithm)
Eligibility criteria
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Age ≥ 18 years
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Probable or definite CIDP patients according to the European Federation of Neurological Societies/Peripheral Nerve Society 2010 criteria [16].
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IVIG treated as follows:1.Documented evidence of objective response to IVIG, with clinically meaningful improvement. Clinically meaningful improvement is defined as one of the following: ≥ 1 point decrease in adjusted INCAT score, ≥ 4 points increase in I-RODS total score, ≥ 3 points increase in MRC Sum score, ≥ 8 kilopascal improvement in mean grip strength (one hand), or an equivalent improvement based on information documented in medical records and per the principal investigator's (PI) judgement.2.Must be on stable IVIG therapy, defined as no change greater than 10% in frequency or dose of immunoglobulin therapy or corticosteroids within 8 weeks prior to screening.3.Evidence of clinically meaningful deterioration on interruption or dose reduction of IVIG therapy within 24 months prior to screening, determined by clinical examination or medical records. Clinically meaningful deterioration is defined as one of the following: ≥ 1 point increase in adjusted INCAT score, decrease in I-RODS total score ≥ 4 points, decrease in MRC Sum score ≥ 3, mean grip strength worsening of ≥ 8 kilopascals (one hand), or an equivalent deterioration based on information from medical records and at the PI's judgement.
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unable to use smartwatch or/ and smartphone device
Laboratory methods
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sNfL: we plan to use a single molecule array with a SiMoA HD-1 (Quanterix, USA) using the Nf-Light Advantage Kits (Quanterix) according to manufacturer’s instructions for NfL measurements to measure sNfL in sera obtained at each visit of patients.
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Proteomics: Serum samples are planned to be depleted using the ProteoMiner kit (Bio-Rad Laboratories Inc., Hercules, CA, USA) according to the manufacturer’s instructions. After further standard processing steps such as reduction and alkylation, peptides will be collected, dried and resolved. Peptide solutions are set to be analyzed by reversed-phase chromatography coupled to ion mobility mass spectrometry with Synapt G2 Si/ M-Class nano-ultra performance liquid chromatography (UPLC) (Waters Corporation, Milford, MA, US). Analysis is planned to be carried out using Progenesis for Proteomics (Walters) and Perseus.
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Metabolomics: The exact protocol is currently under development, we aim to integrate serum sample preprocessing as an alternative to above-mentioned processing. The current goal is to base the metabolomics analysis on mass spectrometry as well.
Analysis strategy
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Advanced machine learning algorithms, employing both linear and non-linear boosted regression, along with gradient boosted random forest trees and potentially other machine learning algorithms, will be implemented for integrative prediction and categorization of clinical parameters, using biomedical (inflammation markers, proteome, metabolome) data as the base.
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Data of the different digitally tracked features will be analyzed using time series analysis methods and will be screened for trends, autocorrelation and seasonality components as well as feature analysis and time series decomposition. Features and traits of this time series data will be correlated with clinical developments as well as with biological features such as promisingmarkers like sNfL or proteomic marker candidates.
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Aggregated digital measures of different timepoints in the treatment cycle will be compared repetitively with each other (e.g. first week after treatment vs. week before treatment) and screened for differences on an individual and a group level, which might point to end-of-dose phenomena. Further stratification might include subgroups based on clinical or biomedical features.
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Repetitive trends in digitally-acquired time series data will be analyzed and potentially modeled using time-split data, deploying autoregressive moving average models as well as other algorithms.