Emir Birol, Johnson Kjell, Kuhn Max, Parsons Bruce
Pfizer Inc, New York, New York.
Arbor Analytics, Ann Arbor, Michigan.
Clin Ther. 2017 Jan;39(1):98-106. doi: 10.1016/j.clinthera.2016.11.015. Epub 2016 Dec 20.
This post hoc analysis used 11 predictive models of data from a large observational study in Germany to evaluate potential predictors of achieving at least 50% pain reduction by week 6 after treatment initiation (50% pain response) with pregabalin (150-600 mg/d) in patients with neuropathic pain (NeP).
The potential predictors evaluated included baseline demographic and clinical characteristics, such as patient-reported pain severity (0 [no pain] to 10 [worst possible pain]) and pain-related sleep disturbance scores (0 [sleep not impaired] to 10 [severely impaired sleep]) that were collected during clinic visits (baseline and weeks 1, 3, and 6). Baseline characteristics were also evaluated combined with pain change at week 1 or weeks 1 and 3 as potential predictors of end-of-treatment 50% pain response. The 11 predictive models were linear, nonlinear, and tree based, and all predictors in the training dataset were ranked according to their variable importance and normalized to 100%.
The training dataset comprised 9187 patients, and the testing dataset had 6114 patients. To adjust for the high imbalance in the responder distribution (75% of patients were 50% responders), which can skew the parameter tuning process, the training set was balanced into sets of 1000 responders and 1000 nonresponders. The predictive modeling approaches that were used produced consistent results. Baseline characteristics alone had fair predictive value (accuracy range, 0.61-0.72; κ range, 0.17-0.30). Baseline predictors combined with pain change at week 1 had moderate predictive value (accuracy, 0.73-0.81; κ range, 0.37-0.49). Baseline predictors with pain change at weeks 1 and 3 had substantial predictive value (accuracy, 0.83-0.89; κ range, 0.54-0.71). When variable importance across the models was estimated, the best predictor of 50% responder status was pain change at week 3 (average importance 100.0%), followed by pain change at week 1 (48.1%), baseline pain score (14.1%), baseline depression (13.9%), and using pregabalin as a monotherapy (11.7%).
The finding that pain changes by week 1 or weeks 1 and 3 are the best predictors of pregabalin response at 6 weeks suggests that adhering to a pregabalin medication regimen is important for an optimal end-of-treatment outcome. Regarding baseline predictors alone, considerable published evidence supports the importance of high baseline pain score and presence of depression as factors that can affect treatment response. Future research would be required to elucidate why using pregabalin as a monotherapy also had more than a 10% variable importance as a potential predictor.
本事后分析使用了来自德国一项大型观察性研究的数据的11种预测模型，以评估在神经性疼痛（NeP）患者中，使用普瑞巴林（150 - 600 mg/天）治疗开始后第6周时实现至少50%疼痛减轻（50%疼痛缓解）的潜在预测因素。
评估的潜在预测因素包括基线人口统计学和临床特征，如患者报告的疼痛严重程度（0[无疼痛]至10[可能的最严重疼痛]）以及在门诊就诊期间（基线以及第1、3和6周）收集的疼痛相关睡眠障碍评分（0[睡眠未受影响]至10[严重睡眠障碍]）。还将基线特征与第1周或第1周和第3周的疼痛变化相结合进行评估，作为治疗结束时50%疼痛缓解的潜在预测因素。这11种预测模型基于线性、非线性和树模型，训练数据集中的所有预测因素均根据其变量重要性进行排名并归一化为100%。
训练数据集包括9187例患者，测试数据集有6114例患者。为了调整反应者分布中的高度不平衡（75%的患者为50%反应者），这可能会使参数调整过程产生偏差，训练集被平衡为1000例反应者和1000例无反应者的集合。所使用的预测建模方法产生了一致的结果。仅基线特征具有一定的预测价值（准确率范围为0.61 - 至0.72；κ范围为0.17 - 0.30）。基线预测因素与第1周的疼痛变化相结合具有中等预测价值（准确率为0.73 - 0.81；κ范围为0.37 - 0.49）。基线预测因素与第1周和第3周的疼痛变化相结合具有较高的预测价值（准确率为0.83 - 0.89；κ范围为0.54 - 0.71）。当估计各模型中的变量重要性时，50%反应者状态的最佳预测因素是第3周的疼痛变化（平均重要性100.0%），其次是第1周的疼痛变化（48.1%）、基线疼痛评分（14.1%）、基线抑郁（13.9%）以及使用普瑞巴林作为单一疗法（11.7%）。
第1周或第1周和第3周的疼痛变化是普瑞巴林6周反应的最佳预测因素这一发现表明，坚持普瑞巴林药物治疗方案对于获得最佳治疗结局很重要。关于仅基线预测因素，大量已发表的证据支持高基线疼痛评分和存在抑郁作为可影响治疗反应的因素的重要性。未来需要进行研究以阐明为何使用普瑞巴林作为单一疗法作为潜在预测因素时也具有超过10%的变量重要性。
