Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.
Department of Dermatology, University of Zürich, Zürich, Switzerland.
JAMA Dermatol. 2022 Oct 1;158(10):1149-1156. doi: 10.1001/jamadermatol.2022.3171.
Identifying the optimal long-term biologic therapy for patients with psoriasis is often done through trial and error.
To identify the optimal biologic therapy for individual patients with psoriasis using predictive statistical and machine learning models.
DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study used data from Danish nationwide registries, primarily DERMBIO, and included adult patients treated for moderate-to-severe psoriasis with biologics. Data were processed and analyzed between spring 2021 and spring 2022.
Patient clusters of clinical relevance were identified and their success rates estimated for each drug. Furthermore, predictive prognostic models to identify optimal biologic treatment at the individual level based on data from nationwide registries were evaluated.
Assuming a success criterion of 3 years of sustained treatment, this study included 2034 patients with a total of 3452 treatment series. Most treatment series involved male patients (2147 [62.2%]) originating from Denmark (3190 [92.4%]), and 2414 (69.9%) had finished an education longer than primary school. The average ages were 24.9 years at psoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decision trees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17 inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%, and top 2 accuracies of 95.9% and 93.9%. When predicting specific drugs resulting in success, gradient boost and logistic regression had accuracies of 48.5% and 44.4%, top 2 accuracies of 77.6% and 75.9%, and top 3 accuracies of 89.9% and 89.0%.
Of the treatment prediction models used in this cohort study of patients with psoriasis, gradient-boosted decision trees performed significantly better than logistic regression when predicting specific biologic therapy (by drug as well as target) leading to a treatment duration of at least 3 years without discontinuation. Predicting the optimal biologic could benefit patients and clinicians by minimizing the number of failed treatment attempts.
对于患有银屑病的患者，确定最佳的长期生物治疗方案通常需要经过反复试验。
使用预测统计和机器学习模型为每位银屑病患者确定最佳的生物治疗方案。
设计、设置和参与者：这项基于人群的队列研究使用了丹麦全国性登记处（主要是 DERMBIO）的数据，包括接受生物制剂治疗中重度银屑病的成年患者。数据的处理和分析在 2021 年春季至 2022 年春季之间进行。
确定了具有临床相关性的患者聚类，并估计了每种药物的成功率。此外，还评估了基于全国登记处数据在个体水平上识别最佳生物治疗的预测预后模型。
假设成功标准为 3 年持续治疗，本研究共纳入了 2034 名患者，总计 3452 个治疗系列。大多数治疗系列涉及男性患者（2147[62.2%]）来自丹麦（3190[92.4%]），2414（69.9%）接受过高于小学的教育。银屑病诊断时的平均年龄为 24.9 岁，生物治疗开始时的平均年龄为 45.5 岁。梯度提升决策树和逻辑回归能够以 63.6%和 59.2%的准确度预测与成功治疗相关的特定细胞因子靶点（例如白细胞介素-17 抑制），前 2 名的准确度为 95.9%和 93.9%。当预测导致成功的特定药物时，梯度提升和逻辑回归的准确度分别为 48.5%和 44.4%，前 2 名的准确度分别为 77.6%和 75.9%，前 3 名的准确度分别为 89.9%和 89.0%。
在这项针对银屑病患者的队列研究中使用的治疗预测模型中，梯度提升决策树在预测特定生物治疗（按药物和靶点）方面的表现明显优于逻辑回归，这些生物治疗可使治疗持续时间至少 3 年而无需停药。预测最佳的生物制剂可以通过最大限度地减少治疗失败的尝试次数，使患者和临床医生受益。
