Lees Charlie W, Deuring J Jasper, Chiorean Michael, Daperno Marco, Bonfanti Gianluca, Germino Rebecca, Brown Pritha Bhadra, Modesto Irene, Edwards Roger A
Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Crewe Road, Edinburgh EH4 2XU, UK.
Pfizer Netherlands GmbH, Rotterdam, The Netherlands.
Therap Adv Gastroenterol. 2021 Nov 29;14:17562848211054710. doi: 10.1177/17562848211054710. eCollection 2021.
Tofacitinib is an oral, small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). Outcome prediction based on early treatment response, along with clinical and laboratory variables, would be very useful for clinical practice. The aim of this study was to determine early variables predictive of responder status in patients with UC treated with tofacitinib.
Data were collected from patients treated with tofacitinib 10 mg twice daily in the OCTAVE Induction 1 and 2 studies (NCT01465763 and NCT01458951). Logistic regression and random forest analyses were performed to determine the power of clinical and/or laboratory variables to predict 2- and 3-point partial Mayo score responder status of patients at Weeks 4 or 8 after baseline.
From a complete list of variables measured in OCTAVE Induction 1 and 2, analyses identified partial Mayo score, partial Mayo subscore (stool frequency, rectal bleeding, and Physician Global Assessment), cholesterol level, and C-reactive protein level as sufficient variables to predict responder status. Using these variables at baseline and Week 2 predicted responder status at Week 4 with 84-87% accuracy and Week 8 with 74-79% accuracy. Variables at baseline, Weeks 2 and 4 could predict responder status at Week 8 with 85-87% accuracy.
Using a limited set of time-dependent variables, statistical and machine learning models enabled early and clinically meaningful predictions of tofacitinib treatment outcomes in patients with moderately to severely active UC.
托法替布是一种口服小分子Janus激酶抑制剂，用于治疗溃疡性结肠炎（UC）。基于早期治疗反应以及临床和实验室变量进行结果预测，对临床实践将非常有用。本研究的目的是确定在接受托法替布治疗的UC患者中预测缓解者状态的早期变量。
从OCTAVE诱导1和2研究（NCT01465763和NCT01458951）中接受每日两次10 mg托法替布治疗的患者收集数据。进行逻辑回归和随机森林分析，以确定临床和/或实验室变量预测基线后第4周或第8周患者2分和3分部分梅奥评分缓解者状态的能力。
从OCTAVE诱导1和2中测量的完整变量列表中，分析确定部分梅奥评分、部分梅奥亚评分（大便频率、直肠出血和医生整体评估）、胆固醇水平和C反应蛋白水平为预测缓解者状态的充分变量。使用基线和第2周的这些变量预测第4周的缓解者状态，准确率为84 - 87%，预测第8周的准确率为74 - 79%。基线、第2周和第4周的变量可预测第8周的缓解者状态，准确率为85 - 87%。
使用一组有限的时间依赖性变量，统计和机器学习模型能够对中度至重度活动性UC患者的托法替布治疗结果进行早期且具有临床意义的预测。
