Papp Kim A, Soliman Ahmed M, Done Nicolae, Carley Christopher, Lemus Wirtz Esteban, Puig Luis
K Papp Clinical Research and Probity Medical Research, Waterloo, ON, Canada.
AbbVie, Inc., North Chicago, IL, USA.
Dermatol Ther (Heidelb). 2021 Aug;11(4):1291-1304. doi: 10.1007/s13555-021-00550-8. Epub 2021 May 21.
Risankizumab has demonstrated efficacy in treating moderate-to-severe psoriasis. The phase-3 IMMhance trial (NCT02672852) examined the effect of continuing versus withdrawing from risankizumab treatment on psoriasis severity, including the Psoriasis Area and Severity Index (PASI) and static Physician Global Assessment (sPGA). However, the effect of withdrawal on health-related quality of life (HRQL) was not assessed. Therefore, this study was conducted to evaluate the impact of risankizumab withdrawal on HRQL measured by the Dermatology Life Quality Index (DLQI). Because DLQI was not measured beyond week 16 in IMMhance, a machine learning predictive model for DLQI was developed.
A machine learning model for DLQI was fitted using repeated measures data from three phase-3 trials (NCT02684370, NCT02684357, NCT02694523) (pooled N = 1602). An elastic-net algorithm performed automated variable selection among candidate predictors including concurrent PASI and sPGA, demographics, and interaction terms. The machine learning model was used to predict DLQI at weeks 28-104 of IMMhance among patients re-randomized to continue (N = 111) or withdraw from (N = 225) risankizumab after achieving response (sPGA = 0/1) at week 28.
The machine learning predictive model demonstrated good statistical fit during tenfold cross-validation and external validation against observed DLQI at weeks 0-16 of IMMhance (N = 507). Predicted improvements in DLQI from baseline were lower in the withdrawal versus the continuation cohort (mean DLQI change at week 104, -5.9 versus -11.5, difference [95% CI] = 5.6 [4.1, 7.3]). Predicted DLQI deteriorated more extensively than PASI (49.7% versus 36.4%) after treatment withdrawal.
The predicted DLQI score deteriorated more rapidly after risankizumab withdrawal than the PASI score, an objective measure of disease. These findings suggest that the deterioration in HRQL reflects more substantial impacts after risankizumab discontinuation than those measured by PASI only.
司库奇尤单抗已证明在治疗中度至重度银屑病方面具有疗效。3期IMMhance试验（NCT02672852）研究了继续或停止司库奇尤单抗治疗对银屑病严重程度的影响，包括银屑病面积和严重程度指数（PASI）以及静态医师整体评估（sPGA）。然而，未评估停药对健康相关生活质量（HRQL）的影响。因此，本研究旨在评估司库奇尤单抗停药对通过皮肤病生活质量指数（DLQI）衡量的HRQL的影响。由于IMMhance试验中DLQI在第16周后未进行测量，因此开发了一种DLQI的机器学习预测模型。
使用来自三项3期试验（NCT02684370、NCT02684357、NCT02694523）的重复测量数据（汇总N = 1602）拟合DLQI的机器学习模型。弹性网络算法在包括同期PASI和sPGA、人口统计学以及交互项在内的候选预测变量中进行自动变量选择。该机器学习模型用于预测在第28周达到缓解（sPGA = 0/1）后重新随机分组继续（N = 111）或停用（N = 225）司库奇尤单抗的患者在IMMhance试验第28 - 104周的DLQI。
在十折交叉验证以及针对IMMhance试验第0 - 16周观察到的DLQI进行外部验证期间，机器学习预测模型显示出良好的统计拟合度（N = 507）。停药组与继续用药组相比，从基线预测的DLQI改善程度更低（第104周DLQI平均变化，-5.9对-11.
