Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Northwestern University, Chicago, Illinois.
Arthritis Care Res (Hoboken). 2022 Aug;74(8):1342-1348. doi: 10.1002/acr.24559. Epub 2022 Apr 22.
To develop a claims-based model to predict persistent high-dose opioid use among patients undergoing total knee replacement (TKR).
Using Medicare claims (2010-2014), we identified patients ages ≥65 years who underwent TKR with no history of high-dose opioid use (mean >25 morphine milligram equivalents [MMEs]/day) in the year prior to TKR. We used group-based trajectory modeling to identify distinct opioid use patterns. The primary outcome was persistent high-dose opioid use in the year after TKR. We split the data into training (2010-2013) and test (2014) sets and used logistic regression with least absolute shrinkage and selection operator regularization, utilizing a total of 83 preoperative patient characteristics as candidate predictors. A reduced model with 10 prespecified variables, which included demographic characteristics, opioid use, and medication history was also considered.
The final study cohort included 142,089 patients who underwent TKR. The group-based trajectory model identified 4 distinct trajectories of opioid use (group 1: short-term, low-dose; group 2: moderate-duration, low-dose; group 3: moderate-duration, high-dose; and group 4: persistent high-dose). The model predicting persistent high-dose opioid use achieved high discrimination (receiver operating characteristic area under the curve [AUC] 0.85 [95% confidence interval (95% CI) 0.84-0.86]) in the test set. The reduced model with 10 predictors performed equally well (AUC 0.84 [95% CI 0.84-0.85]).
In this cohort of older patients, 10.6% became persistent high-dose (mean 22.4 MME/day) opioid users after TKR. Our model with 10 readily available clinical factors may help identify patients at high risk of future adverse outcomes from persistent opioid use after TKR.
开发一种基于索赔的模型,以预测接受全膝关节置换术(TKR)的患者中持续高剂量阿片类药物使用的情况。
使用医疗保险索赔数据(2010-2014 年),我们确定了年龄≥65 岁的患者,他们在 TKR 前一年没有高剂量阿片类药物使用史(平均>25 吗啡毫克当量[MME]/天)。我们使用基于群组的轨迹建模来识别不同的阿片类药物使用模式。主要结局是 TKR 后一年内持续高剂量阿片类药物使用。我们将数据分为训练集(2010-2013 年)和测试集(2014 年),并使用逻辑回归与最小绝对收缩和选择算子正则化,利用总共 83 个术前患者特征作为候选预测因子。还考虑了一个具有 10 个预设变量的简化模型,其中包括人口统计学特征、阿片类药物使用和药物史。
最终的研究队列包括 142089 名接受 TKR 的患者。基于群组的轨迹模型确定了 4 种不同的阿片类药物使用轨迹(第 1 组:短期、低剂量;第 2 组:中等持续时间、低剂量;第 3 组:中等持续时间、高剂量;第 4 组:持续高剂量)。在测试集中,预测持续高剂量阿片类药物使用的模型具有较高的区分度(受试者工作特征曲线下面积[AUC]为 0.85[95%置信区间(95%CI)为 0.84-0.86])。具有 10 个预测因子的简化模型表现同样出色(AUC 为 0.84[95%CI 为 0.84-0.85])。
在这个老年患者队列中,10.6%的患者在 TKR 后成为持续高剂量(平均 22.4 MME/天)阿片类药物使用者。我们的模型具有 10 个易于获得的临床因素,可能有助于识别 TKR 后持续使用阿片类药物未来不良后果风险较高的患者。