Narbutt Joanna, Krzyścin Janusz, Sobolewski Piotr, Skibińska Małgorzata, Noweta Marcin, Owczarek Witold, Rajewska-Więch Bonawentura, Lesiak Aleksandra
Department of Dermatology, Pediatric Dermatology and Oncology, Medical University of Łódź, Łódź, Poland.
Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland.
Clin Cosmet Investig Dermatol. 2021 Mar 18;14:253-259. doi: 10.2147/CCID.S296604. eCollection 2021.
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.
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.
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.
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.
如今，中重度银屑病患者接受传统免疫抑制剂或新型生物制剂治疗。光疗是局部治疗不足患者的一线治疗方法。尽管已经开展了大量研究，但仍难以预测个体患者光疗的效果。
在标准窄谱（NB）紫外线B（UVB）光疗前，患者填写一份关于个人生活和健康状况的问卷。在20次NB-UVB治疗疗程前后进行多项标准血液检查，包括选定的细胞因子水平检测。使用人工智能方法——随机森林（RF）分类工具对问卷答案、血液检查结果和治疗结果进行分析。
共纳入82例中重度银屑病患者。在开始光疗前，RF分类器能够识别出光疗预期效果良好、缓解期较短以及可能放弃NB-UVB第二疗程治疗的患者，其灵敏度超过84%，准确率分别为75%、85%和79%。纳入细胞因子数据并未提高RF分类器的性能。
这种方法有助于做出临床决策，识别出光疗能显著改善皮肤状况的银屑病患者，或那些应预先推荐其他治疗方法的患者。
