Dataset Information

EczemaPred: A computational framework for personalised prediction of eczema severity dynamics.

ABSTRACT:

Background

Atopic dermatitis (AD) is a chronic inflammatory skin disease leading to substantial quality of life impairment with heterogeneous treatment responses. People with AD would benefit from personalised treatment strategies, whose design requires predicting how AD severity evolves for each individual.

Objective

This study aims to develop a computational framework for personalised prediction of AD severity dynamics.

Methods

We introduced EczemaPred, a computational framework to predict patient-dependent dynamic evolution of AD severity using Bayesian state-space models that describe latent dynamics of AD severity items and how they are measured. We used EczemaPred to predict the dynamic evolution of validated patient-oriented scoring atopic dermatitis (PO-SCORAD) by combining predictions from the models for the nine severity items of PO-SCORAD (six intensity signs, extent of eczema, and two subjective symptoms). We validated this approach using longitudinal data from two independent studies: a published clinical study in which PO-SCORAD was measured twice weekly for 347 AD patients over 17 weeks, and another one in which PO-SCORAD was recorded daily by 16 AD patients for 12 weeks.

Results

EczemaPred achieved good performance for personalised predictions of PO-SCORAD and its severity items daily to weekly. EczemaPred outperformed standard time-series forecasting models such as a mixed effect autoregressive model. The uncertainty in predicting PO-SCORAD was mainly attributed to that in predicting intensity signs (75% of the overall uncertainty).

Conclusions

EczemaPred serves as a computational framework to make a personalised prediction of AD severity dynamics relevant to clinical practice. EczemaPred is available as an R package.

SUBMITTER: Hurault G

PROVIDER: S-EPMC8967258 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

EczemaPred: A computational framework for personalised prediction of eczema severity dynamics.

Hurault Guillem G Stalder Jean François JF Mery Sophie S Delarue Alain A Saint Aroman Markéta M Josse Gwendal G Tanaka Reiko J RJ

Clinical and translational allergy 20220301 3

<h4>Background</h4>Atopic dermatitis (AD) is a chronic inflammatory skin disease leading to substantial quality of life impairment with heterogeneous treatment responses. People with AD would benefit from personalised treatment strategies, whose design requires predicting how AD severity evolves for each individual.<h4>Objective</h4>This study aims to develop a computational framework for personalised prediction of AD severity dynamics.<h4>Methods</h4>We introduced EczemaPred, a computational fr ...[more]

PMID: 35344305

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Project description:BackgroundThe Hand Eczema Severity Index (HECSI) is used to measure the severity of hand eczema. It is unclear what HECSI scores mean and what is the minimally important change (MIC). Furthermore, its responsiveness has not been studied.ObjectivesTo study the responsiveness and interpretability of the HECSI.MethodsThis was a prospective study covering two time points: baseline and after 4-12 weeks. Responsiveness was assessed using a criterion approach, with at least one-step improvement on the 'Photographic guide for severity of hand eczema' as the anchor for important improvement. Interpretability of single scores was determined by defining severity bands based on agreement with the anchor. For change scores, the smallest detectable change (SDC) was calculated in patients indicating no change and the MIC was obtained in patients indicating that they had changed using three methods: mean cutoff, receiver operating characteristic (ROC) curve and 95% limit.ResultsIn total, 294 patients participated (160 male, mean age 44·9 years). HECSI scores improved or deteriorated in parallel with the anchor. The area under the ROC curve was 0·86 (95% confidence interval 0·81-0·91). The final severity band for single scores had a κ-coefficient of agreement of 0·694: clear, 0; almost clear, 1-16; moderate, 17-37; severe, 38-116; very severe, ≥ 117. The SDC in 93 unchanged patients was 40·3 points. The obtained MIC values were all smaller than the SDC.ConclusionsThe HECSI has good responsiveness. This study gives meaning to HECSI scores, which can be applied to clinical decision making and the design of clinical trials. We recommend that an improvement of 41 points on the HECSI is regarded as the minimally measurable true change. What's already known about this topic? The Hand Eczema Severity Index (HECSI) is one of the most widely used measurement instruments to measure the severity of hand eczema. The HECSI has good reliability, but its responsiveness and interpretability have not been studied. What does this study add? This study shows good responsiveness of the HECSI. A severity grading for single scores is proposed. The smallest detectable change and minimally important change for improvement are determined. What are the clinical implications of this work? The obtained values help to interpret HECSI scores in daily practice and clinical studies, and may facilitate researchers in the calculation of sample sizes for clinical trials.

Dataset Information

EczemaPred: A computational framework for personalised prediction of eczema severity dynamics.

Background

Objective

Methods

Results

Conclusions

Publications

EczemaPred: A computational framework for personalised prediction of eczema severity dynamics.

Similar Datasets

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