Dolendo Isabella M, Wallace Anne M, Armani Ava, Waterman Ruth S, Said Engy T, Gabriel Rodney A
Anesthesiology, University of California San Diego, La Jolla, USA.
Surgery, University of California San Diego, La Jolla, USA.
Cureus. 2022 Mar 11;14(3):e23079. doi: 10.7759/cureus.23079. eCollection 2022 Mar.
The use of opioids in mastectomy patients is a particular challenge, having to balance the management of acute pain while minimizing risks of continuous opioid use postoperatively. Despite attempts to decrease postmastectomy opioid use, including regional anesthetics, gabapentinoids, topical anesthetics, and nonopioid anesthesia, prolonged opioid use remains clinically significant among these patients. The goal of this study is to identify risk factors and develop machine-learning-based models to predict patients who are at higher risk for postoperative opioid use after mastectomy.
In this retrospective cohort study, we collected data from patients that underwent mastectomy procedures. The primary outcome of interest was defined as oxycodone milligram equivalents (OME) greater than or equal to the 75% of OME use on a postoperative day 1. Model performance (area under the receiver-operating characteristics curve (AUC)) of various machine learning approaches was calculated via 10-fold cross-validation. Odds ratio (OR) and 95% confidence intervals (CI) were reported.
There were a total of 148 patients that underwent mastectomy and were included. The medium (quartiles) postoperative day 1 opioid use was 5 mg OME (0.25 mg OME). Using multivariable logistic regression, the most protective factors against higher opioid use was being postmenopausal (OR: 0.13, 95% CI: 0.03-0.61, p = 0.009) and cancer diagnosis (OR: 0.19, 95% CI: 0.05-0.73, p = 0.01). The AUC was 0.725 (95% CI: 0.572-0.876). There was no difference in the performance of other machine-learning-based approaches.
The ability to predict patients' postoperative pain could have a significant impact on preoperative counseling and patient satisfaction.
在乳房切除患者中使用阿片类药物是一项特殊挑战，必须在管理急性疼痛的同时尽量降低术后持续使用阿片类药物的风险。尽管人们尝试减少乳房切除术后阿片类药物的使用，包括使用区域麻醉剂、加巴喷丁类药物、局部麻醉剂和非阿片类麻醉，但在这些患者中，长期使用阿片类药物在临床上仍然很显著。本研究的目的是识别风险因素，并开发基于机器学习的模型，以预测乳房切除术后阿片类药物使用风险较高的患者。
在这项回顾性队列研究中，我们收集了接受乳房切除手术患者的数据。感兴趣的主要结局定义为术后第1天羟考酮毫克当量（OME）大于或等于OME使用量的75%。通过10倍交叉验证计算各种机器学习方法的模型性能（受试者操作特征曲线下面积（AUC））。报告比值比（OR）和95%置信区间（CI）。
共有148例接受乳房切除术的患者被纳入研究。术后第1天阿片类药物使用的中位数（四分位数）为5毫克OME（0.25毫克OME）。使用多变量逻辑回归分析，预防阿片类药物高使用量的最具保护作用的因素是绝经后（OR：0.13，95%CI：0.03-0.61，p=0.009）和癌症诊断（OR：0.19，95%CI：0.05-0.73，p=0.01）。AUC为0.725（95%CI：0.572-0.876）。其他基于机器学习的方法的性能没有差异。
预测患者术后疼痛的能力可能对术前咨询和患者满意度产生重大影响。
