Evidence map›Paper›PMID 36862306›Full record

ArticleDermatology and therapy2023

Factors Predicting Quality of Life Impairment in Adult Patients with Atopic Dermatitis: Results from a Patient Survey and Machine Learning Analysis.

Carle Paul, Christopher E M Griffiths, Antonio Costanzo, Pedro Herranz, Susanne Grond, Can Mert, Nicole Tietz, Elisabeth Riedl, Matthias Augustin

Abstract read
In one paragraph

Article in Dermatology and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. Efficacy of up to 4 years of tralokinumab in adults with moderate-to-severe atopic dermatitis.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026
    Trial
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Carle PaulUniversité Toulouse Paul Sabatier, Toulouse, France.
Christopher E M GriffithsDermatology Centre, Salford Royal Hospital, University of Manchester, Manchester Biomedical Research Centre, Manchester, UK.
Antonio CostanzoHumanitas University, Rozzano, Italy.
Pedro HerranzLa Paz University Hospital, Madrid, Spain.
Susanne GrondEli Lilly and Company, Indianapolis, IN, USA.
Can MertHaaPACS GmbH, Schriesheim, Germany.
Nicole TietzEli Lilly and Company, Indianapolis, IN, USA.
Elisabeth RiedlEli Lilly and Company, Indianapolis, IN, USA.
Matthias AugustinUniversity Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany. m.augustin@uke.de.ORCID http://orcid.org/0000-0002-4026-8728

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAtopic 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.

methodsUsing 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.

resultsIn 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.

conclusionsActivity 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.

Indexed as

Atopic dermatitisMachine learning methodsQuality of life

Identifiers

PMID36862306
PMCPMC10060474

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