The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD.
The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD.
Urol Oncol. 2022 Mar;40(3):104.e9-104.e15. doi: 10.1016/j.urolonc.2021.10.007. Epub 2021 Nov 29.
Judicious opioid stewardship would match each patient's prescription to their true medical necessity. However, most prescribing paradigms apply preset quantities and clinical judgment without objective data to predict individual use. We evaluated individual patient and in-hospital parameters as predictors of post-discharge opioid utilization after radical prostatectomy (RP) to provide evidence-based guidance for individualized prescribing.
A prospective cohort of patients who underwent open or robotic RP were followed in the Opioid Reduction Intervention for Open, Laparoscopic, and Endoscopic Surgery (ORIOLES) initiative. Baseline demographics, in-hospital parameters, and inpatient and post-discharge pain medication utilization were tabulated. Opioid medications were converted to oral morphine equivalents (OMEQ). Predictive factors for post-discharge opioid utilization were analyzed by univariable and multivariable linear regression, adjusting for opioid reduction interventions performed in ORIOLES.
Of 443 patients, 102 underwent open and 341 underwent robotic RP. The factors most strongly associated with post-discharge opioid utilization included inpatient opioid utilization in the final 12 hours before discharge (+39.6 post-discharge OMEQ if inpatient OMEQ was >15 vs. 0), maximum patient-reported pain score (range 0-10) in the 12 hours before discharge (+27.6 OMEQ for pain score ≥6 vs. ≤1), preoperative opioid use (+76.2 OMEQ), and body mass index (BMI; +1.4 OMEQ per 1 kg/m). A final predictive calculator to guide post-discharge opioid prescribing was constructed.
Following RP, inpatient opioid use, patient-reported pain scores, prior opioid use, and BMI are correlated with post-discharge opioid utilization. These data can help guide individualized opioid prescribing to reduce risks of both overprescribing and underprescribing.
明智的阿片类药物管理将使每位患者的处方与他们的真正医疗需求相匹配。然而，大多数处方模式应用预设数量和临床判断，而没有客观数据来预测个体使用。我们评估了患者个体和住院期间的参数作为根治性前列腺切除术 (RP) 后出院后阿片类药物使用的预测因素，为个体化处方提供循证指导。
前瞻性队列研究接受开放或机器人 RP 的患者在阿片类药物减少干预开放、腹腔镜和内镜手术 (ORIOLES) 计划中进行随访。记录基线人口统计学、住院期间参数以及住院期间和出院后疼痛药物使用情况。阿片类药物换算为口服吗啡当量 (OMEQ)。通过单变量和多变量线性回归分析调整 ORIOLES 中进行的阿片类药物减少干预措施，分析出院后阿片类药物使用的预测因素。
在 443 名患者中，102 名接受了开放性 RP，341 名接受了机器人 RP。与出院后阿片类药物使用最密切相关的因素包括出院前最后 12 小时的住院期间阿片类药物使用（如果住院期间 OMEQ >15，则出院后 OMEQ 增加 39.6），出院前最后 12 小时患者报告的最高疼痛评分（范围 0-10）（疼痛评分≥6 与≤1 相比，增加 27.6 OMEQ），术前阿片类药物使用（增加 76.2 OMEQ）和体重指数（BMI；每增加 1 kg/m，增加 1.4 OMEQ）。构建了最终预测计算器以指导出院后阿片类药物处方。
RP 后，住院期间阿片类药物使用、患者报告的疼痛评分、既往阿片类药物使用和 BMI 与出院后阿片类药物使用相关。这些数据可以帮助指导个体化阿片类药物处方，以降低过度处方和处方不足的风险。
