Data-driven personalised recommendations for eczema treatment using a Bayesian model of severity dynamics

Atopic dermatitis (AD) is a chronic inflammatory skin disease. AD has heterogeneous phenotypes, making it challenging to predict treatment effects for each patient and to generate personalised treatment recommendations. Here we aim to develop a computational model that predicts the evolution of AD severity and generates treatment recommendations for individual patients. We modelled the temporal evolution of eczema severity by applying a previously developed computational framework (EczemaPred) to the daily record of Patient-Oriented SCORing Atopic Dermatitis (PO-SCORAD) collected from 16 AD patients over 12 weeks in an observational study. We also leveraged historical data from 337 AD patients to kickstart the model training and reach more robust conclusions. We estimated the effects of topical corticosteroids and emollients on the next day’s PO-SCORAD, and generated personalised treatment recommendations using Bayesian decision analysis on whether treatment should be applied to improve PO-SCORAD on the next day for each of the 16 patients. We calibrated daily PO-SCORAD recorded by patients with monthly SCORAD assessed by clinical staff to improve the data quality. This study demonstrated a proof-of-concept for generating personalised treatment recommendations for AD using a Bayesian model that integrates multiple sources of information, including PO-SCORAD, SCORAD, and treatment usage. ### Competing Interest Statement MSA is an employee of Pierre Fabre Laboratories. GH, JFS and RJT have no conflicts to disclose. ### Funding Statement This study was funded by the British Skin Foundation (005R18). Funders had no involvement in study design. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by IEC (CPP Ile de France V, Saint Antoine Hospital, number 582211) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The analysis code is available at