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: artificial intelligence; outcome prediction; phototherapy; psoriasis.
© 2021 Narbutt et al.