Dataset Information

Translation and Validation of the Self-Assessment Psoriasis Area Severity Index.

ABSTRACT:

Background

The self-assessment psoriasis area severity index (SAPASI) is a patient-administered psoriasis assessment tool for which we present a validated translation from English to Swedish.

Methods

Validity was evaluated in this single-centre study using the psoriasis area severity index (PASI) as the standard. Test-retest reliability was assessed using repeated SAPASI measurements.

Results

Significant correlations (p < 0.0001) using Spearman's correlation coefficient (r) were found between PASI and SAPASI scores (r = 0.60) for 51 participants (median baseline PASI 4.4, interquartile range [IQR]: 1.8-5.6) and repeated SAPASI measurements (r = 0.70) among 38 participants (median baseline SAPASI 4.0, IQR: 2.5-6.1). Bland-Altman plots showed generally higher SAPASI scores than PASI scores.

Conclusion

The translated version of SAPASI is valid and reliable, although patients generally tend to overrate their disease severity compared to PASI. Keeping this limitation in mind, SAPASI has the potential of being implemented as a time- and cost-efficient assessment tool in a Scandinavian context.

SUBMITTER: Laskowski M

PROVIDER: S-EPMC10614249 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Translation and Validation of the Self-Assessment Psoriasis Area Severity Index.

Laskowski Marta M Schiöler Linus L Wennberg Ann-Marie AM Torén Kjell K Gustafsson Helena H

Dermatology (Basel, Switzerland) 20230310 5

<h4>Background</h4>The self-assessment psoriasis area severity index (SAPASI) is a patient-administered psoriasis assessment tool for which we present a validated translation from English to Swedish.<h4>Methods</h4>Validity was evaluated in this single-centre study using the psoriasis area severity index (PASI) as the standard. Test-retest reliability was assessed using repeated SAPASI measurements.<h4>Results</h4>Significant correlations (p < 0.0001) using Spearman's correlation coefficient ...[more]

PMID: 36907178

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Project description:BackgroundThe Psoriasis Area and Severity Index (PASI) score is commonly used in clinical practice and research to monitor disease severity and determine treatment efficacy. Automating the PASI score with deep learning algorithms, like Convolutional Neural Networks (CNNs), could enable objective and efficient PASI scoring.ObjectivesTo assess the performance of image-based automated PASI scoring in anatomical regions by CNNs and compare the performance of CNNs to image-based scoring by physicians.MethodsImaging series were matched to PASI subscores determined in real life by the treating physician. CNNs were trained using standardized imaging series of 576 trunk, 614 arm and 541 leg regions. CNNs were separately trained for each PASI subscore (erythema, desquamation, induration and area) in each anatomical region (trunk, arms and legs). The head region was excluded for anonymity. Additionally, PASI-trained physicians retrospectively determined image-based subscores on the test set images of the trunk. Agreement with the real-life scores was determined with the intraclass correlation coefficient (ICC) and compared between the CNNs and physicians.ResultsIntraclass correlation coefficients between the CNN and real-life scores of the trunk region were 0.616, 0.580, 0.580 and 0.793 for erythema, desquamation, induration and area, respectively, with similar results for the arms and legs region. PASI-trained physicians (N = 5) were in moderate-good agreement (ICCs 0.706-0.793) with each other for image-based PASI scoring of the trunk region. ICCs between the CNN and real-life scores were slightly higher for erythema (0.616 vs. 0.558), induration (0.580 vs. 0.573) and area scoring (0.793 vs. 0.694) than image-based scoring by physicians. Physicians slightly outperformed the CNN on desquamation scoring (0.580 vs. 0.589).ConclusionsConvolutional Neural Networks have the potential to automatically and objectively perform image-based PASI scoring at an anatomical region level. For erythema, desquamation and induration scoring, CNNs performed similar to physicians, while for area scoring CNNs outperformed physicians on image-based PASI scoring.

Project description:BackgroundIn practice, the goal of treatment for patients with psoriasis is to achieve almost clear or clear skin and maintain disease control, regardless of baseline disease severity. However, identifying absolute Psoriasis Area and Severity Index (PASI) values for new treatment goals is challenging, as most clinical trials report relative PASI 50, 75, 90 or 100 improvements but rarely absolute PASI values achieved.ObjectiveOur objective was to illustrate a statistical conversion method that was developed to derive absolute PASI values from available clinical trial data on relative PASI improvements. The results of network meta-analyses (NMAs) based on these derived data were then compared with those of NMAs based on the corresponding relative PASI improvement data for selected biologics for moderate-to-severe psoriasis.MethodsThe PASI statistical conversion method was applied to relative PASI improvement data for 11 biologic treatment regimens and placebo at 12 weeks using data from 50 published studies. The respective proportions of patients reaching absolute PASI values ≤1, 2, 3 or 5 were then calculated. Frequentist NMAs (Rücker method) were subsequently used to compare efficacy results across relative and absolute PASI data.ResultsThe ranking of included treatment regimens for patients achieving absolute PASI 0 to 8 was aligned with results for relative PASI scores (from 100 to 60) at end of induction therapy. Across the range of PASI scores considered, the most effective treatment regimens based on both absolute and relative PASI NMAs were brodalumab 210 mg every 2 weeks and ixekizumab 80 mg every 2 weeks, followed by guselkumab 100 mg every 8 weeks and risankizumab 150 mg every 12 weeks.ConclusionData generated using this mathematical model will be useful to inform ongoing scientific discussions on treatment goals in the absence of primary absolute PASI data for all available treatments for moderate-to-severe plaque psoriasis.

Dataset Information

Translation and Validation of the Self-Assessment Psoriasis Area Severity Index.

Background

Methods

Results

Conclusion

Publications

Translation and Validation of the Self-Assessment Psoriasis Area Severity Index.

Similar Datasets

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