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A Priori Estimation of the Narrow-Band UVB Phototherapy Outcome for Moderate-to-Severe Psoriasis Based on the Patients’ Questionnaire and Blood Tests Using Random Forest Classifier

Authors Narbutt J, Krzyścin J, Sobolewski P, Skibińska M, Noweta M, Owczarek W, Rajewska-Więch B, Lesiak A

Received 9 December 2020

Accepted for publication 18 February 2021

Published 18 March 2021 Volume 2021:14 Pages 253—259

DOI https://doi.org/10.2147/CCID.S296604

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Jeffrey Weinberg


Joanna Narbutt,1 Janusz Krzyścin,2 Piotr Sobolewski,2 Małgorzata Skibińska,1 Marcin Noweta,1 Witold Owczarek,3 Bonawentura Rajewska-Więch,2 Aleksandra Lesiak1

1Department of Dermatology, Pediatric Dermatology and Oncology, Medical University of Łódź, Łódź, Poland; 2Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland; 3Department of Dermatology, Military Institute of Medicine, Warsaw, Poland

Correspondence: Janusz Krzyścin
Institute of Geophysics, Polish Academy of Sciences, Warsaw, 01-452, Poland
Tel +48-2269-15950
Email [email protected]

Background: Nowadays, patients with moderate-to-severe psoriasis are treated with conventional immunosuppressants or with new biological agents. Phototherapy is the first-line treatment for patients in whom topical therapy is insufficient. Although numerous studies have been carried out, it is still difficult to predict the outcome of phototherapy in individual patients.
Methods: Prior to standard narrow band (NB) ultraviolet B (UVB) phototherapy, the patients filled out a questionnaire about personal life and health status. Several standard blood tests, including selected cytokine levels, were performed before and after a course of 20 NB-UVB treatments. The questionnaire answers, results of the blood tests, and treatment outcomes were analyzed using an artificial intelligence approach—the random forest (RF) classification tool.
Results: A total of 82 participants with moderate-to-severe psoriasis were enrolled. Prior to starting phototherapy, the patients with expected good outcome from the phototherapy, shorter remission, and quitting a possible second course of the NB-UVB treatment could be identified by the RF classifier with sensitivity over 84%, and accuracy of 75%, 85%, and 79%, respectively. The inclusion of cytokine data did not improve the performance of the RF classifier.
Conclusion: This approach offers help in making clinical decisions by identifying psoriatic patients in whom phototherapy will significantly improve their skin, or those in whom other therapies should be recommended beforehand.

Keywords: psoriasis, phototherapy, outcome prediction, artificial intelligence

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