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Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis
INTRODUCTION: Atopic dermatitis (AD) is a chronic, inflammatory skin disorder that impairs patients’ quality of life (QoL). Physician assessment of AD disease severity is determined by clinical scales and assessment of affected body surface area (BSA), which might not mirror patients’ perceived dise...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Healthcare
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060474/ https://www.ncbi.nlm.nih.gov/pubmed/36862306 http://dx.doi.org/10.1007/s13555-023-00897-0 |
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author | Paul, Carle Griffiths, Christopher E. M. Costanzo, Antonio Herranz, Pedro Grond, Susanne Mert, Can Tietz, Nicole Riedl, Elisabeth Augustin, Matthias |
author_facet | Paul, Carle Griffiths, Christopher E. M. Costanzo, Antonio Herranz, Pedro Grond, Susanne Mert, Can Tietz, Nicole Riedl, Elisabeth Augustin, Matthias |
author_sort | Paul, Carle |
collection | PubMed |
description | INTRODUCTION: Atopic dermatitis (AD) is a chronic, inflammatory skin disorder that impairs patients’ quality of life (QoL). Physician assessment of AD disease severity is determined by clinical scales and assessment of affected body surface area (BSA), which might not mirror patients’ perceived disease burden. METHODS: Using data from an international cross-sectional web-based survey of patients with AD and a machine learning approach, we sought to identify disease attributes with the highest impact on QoL for patients with AD. Adults with dermatologist-confirmed AD participated in the survey between July–September 2019. Eight machine learning models were applied to the data with dichotomised Dermatology Life Quality Index (DLQI) as the response variable to identify factors most predictive of AD-related QoL burden. Variables tested were demographics, affected BSA and affected body areas, flare characteristics, activity impairment, hospitalisation and AD therapies. Three machine learning models, logistic regression model, random forest and neural network, were selected on the basis of predictive performance. Each variable’s contribution was computed via importance values from 0 to 100. For relevant predictive factors, further descriptive analyses were conducted to characterise those findings. RESULTS: In total, 2314 patients completed the survey with mean age 39.2 years (standard deviation 12.6) and average disease duration of 19 years. Measured by affected BSA, 13.3% of patients had moderate-to-severe disease. However, 44% of patients reported a DLQI > 10, indicative of a very large to extremely large impact on QoL. Activity impairment was the most important factor predicting high QoL burden (DLQI > 10) across models. Hospitalisation during the past year and flare type were also highly ranked. Current BSA involvement was not a strong predictor of AD-related QoL impairment. CONCLUSIONS: Activity impairment was the single most important factor for AD-related QoL impairment while current extent of AD did not predict higher disease burden. These results support the importance of considering patients’ perspectives when determining the severity of AD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13555-023-00897-0. |
format | Online Article Text |
id | pubmed-10060474 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Healthcare |
record_format | MEDLINE/PubMed |
spelling | pubmed-100604742023-03-31 Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis Paul, Carle Griffiths, Christopher E. M. Costanzo, Antonio Herranz, Pedro Grond, Susanne Mert, Can Tietz, Nicole Riedl, Elisabeth Augustin, Matthias Dermatol Ther (Heidelb) Original Research INTRODUCTION: Atopic dermatitis (AD) is a chronic, inflammatory skin disorder that impairs patients’ quality of life (QoL). Physician assessment of AD disease severity is determined by clinical scales and assessment of affected body surface area (BSA), which might not mirror patients’ perceived disease burden. METHODS: Using data from an international cross-sectional web-based survey of patients with AD and a machine learning approach, we sought to identify disease attributes with the highest impact on QoL for patients with AD. Adults with dermatologist-confirmed AD participated in the survey between July–September 2019. Eight machine learning models were applied to the data with dichotomised Dermatology Life Quality Index (DLQI) as the response variable to identify factors most predictive of AD-related QoL burden. Variables tested were demographics, affected BSA and affected body areas, flare characteristics, activity impairment, hospitalisation and AD therapies. Three machine learning models, logistic regression model, random forest and neural network, were selected on the basis of predictive performance. Each variable’s contribution was computed via importance values from 0 to 100. For relevant predictive factors, further descriptive analyses were conducted to characterise those findings. RESULTS: In total, 2314 patients completed the survey with mean age 39.2 years (standard deviation 12.6) and average disease duration of 19 years. Measured by affected BSA, 13.3% of patients had moderate-to-severe disease. However, 44% of patients reported a DLQI > 10, indicative of a very large to extremely large impact on QoL. Activity impairment was the most important factor predicting high QoL burden (DLQI > 10) across models. Hospitalisation during the past year and flare type were also highly ranked. Current BSA involvement was not a strong predictor of AD-related QoL impairment. CONCLUSIONS: Activity impairment was the single most important factor for AD-related QoL impairment while current extent of AD did not predict higher disease burden. These results support the importance of considering patients’ perspectives when determining the severity of AD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13555-023-00897-0. Springer Healthcare 2023-03-02 /pmc/articles/PMC10060474/ /pubmed/36862306 http://dx.doi.org/10.1007/s13555-023-00897-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Original Research Paul, Carle Griffiths, Christopher E. M. Costanzo, Antonio Herranz, Pedro Grond, Susanne Mert, Can Tietz, Nicole Riedl, Elisabeth Augustin, Matthias Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis |
title | Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis |
title_full | Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis |
title_fullStr | Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis |
title_full_unstemmed | Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis |
title_short | Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis |
title_sort | factors predicting quality of life impairment in adult patients with atopic dermatitis: results from a patient survey and machine learning analysis |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060474/ https://www.ncbi.nlm.nih.gov/pubmed/36862306 http://dx.doi.org/10.1007/s13555-023-00897-0 |
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