Health Services Consulting Corporation, 169 Summer Road, Boxborough, MA, 01719, USA.
Fair Dynamics Consulting, srl, Via Carlo Farini 5, 20154, Milan, Italy.
Adv Ther. 2018 Oct;35(10):1585-1597. doi: 10.1007/s12325-018-0780-3. Epub 2018 Sep 11.
Prediction of final clinical outcomes based on early weeks of treatment can enable more effective patient care for chronic pain. Our goal was to predict, with at least 90% accuracy, 12- to 13-week outcomes for pregabalin-treated painful diabetic peripheral neuropathy (pDPN) patients based on 4 weeks of pain and pain-related sleep interference data.
We utilized active treatment data from six placebo-controlled randomized controlled trials (n = 939) designed to evaluate efficacy of pregabalin for reducing pain in patients with pDPN. We implemented a three-step, trajectory-focused analytics approach based upon patient responses collected during the first 4 weeks using monotonicity, path length, frequency domain (FD), and k-nearest neighbor (kNN) methods. The first two steps were based on combinations of baseline pain, pain at 4 weeks, weekly monotonicity and path length during the first 4 weeks, and assignment of patients to one of four responder groups (based on presence/absence of 50% or 30% reduction from baseline pain at 4 and at 12/13 weeks). The third step included agreement between prediction of logistic regression of daily FD amplitudes and assignment made from kNN analyses.
Step 1 correctly assigned 520/939 patients from the six studies to a responder group using a 3-metric combination approach based on unique assignment to a 50% responder group. Step 2 (applied to the remaining 419 patients) predicted an additional 121 patients, using a blend of 50% and 30% responder thresholds. Step 3 (using a combination of FD and kNN analyses) predicted 204 of the remaining 298 patients using the 50% responder threshold. Our approach correctly predicted 90.0% of all patients.
By correctly predicting 12- to 13-week responder outcomes with 90% accuracy based on responses from the first month of treatment, we demonstrated the value of trajectory measures in predicting pDPN patient response to pregabalin.
www.clinicaltrials.gov identifiers, NCT00156078/NCT00159679/NCT00143156/NCT00553475.
Pfizer. Plain language summary available for this article.
基于治疗早期阶段的预测,可以为慢性疼痛患者提供更有效的护理。我们的目标是利用普瑞巴林治疗糖尿病性周围神经痛(pDPN)患者的 4 周疼痛和与疼痛相关的睡眠干扰数据,至少以 90%的准确率预测 12-13 周的治疗结果。
我们利用了六项安慰剂对照随机对照试验(n=939)的积极治疗数据,这些试验旨在评估普瑞巴林在减轻 pDPN 患者疼痛方面的疗效。我们采用了一种三步骤、以轨迹为重点的分析方法,该方法基于患者在前 4 周内收集的反应,使用单调性、路径长度、频域(FD)和 K-最近邻(kNN)方法。前两个步骤基于基线疼痛、第 4 周时的疼痛、前 4 周的每周单调性和路径长度的组合,以及将患者分配到四个反应者组之一(基于第 4 周和第 12/13 周时基线疼痛减少 50%或 30%的存在/不存在情况)。第三步包括逻辑回归的每日 FD 幅度预测与 kNN 分析得出的分配之间的一致性。
第 1 步使用基于独特分配到 50%反应者组的 3 个指标组合方法,正确地将来自六项研究的 939 名患者中的 520 名分配到一个反应者组。第 2 步(适用于其余 419 名患者)预测了另外 121 名患者,使用 50%和 30%反应者阈值的混合。第 3 步(使用 FD 和 kNN 分析的组合)使用 50%反应者阈值预测了其余 298 名患者中的 204 名。我们的方法正确地预测了所有患者的 90.0%。
通过根据治疗第一个月的反应以 90%的准确率正确预测 12-13 周的反应者结果,我们证明了轨迹测量在预测普瑞巴林治疗 pDPN 患者反应中的价值。
www.clinicaltrials.gov 标识符,NCT00156078/NCT00159679/NCT00143156/NCT00553475。
辉瑞。本文提供了通俗易懂的摘要。