Sun Yuming, Kang Jian, Brummett Chad, Li Yi
Department of Biostatistics, University of Michigan, Ann Arbor.
Department of Anesthesiology, University of Michigan, Ann Arbor.
Ann Appl Stat. 2023 Mar;17(1):434-453. doi: 10.1214/22-aoas1634. Epub 2023 Jan 24.
Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures. Understanding the risk of preoperative opioid use helps establish patient-centered pain management. In the field of machine learning, deep neural network (DNN) has emerged as a powerful means for risk assessment because of its superb prediction power; however, the blackbox algorithms may make the results less interpretable than statistical models. Bridging the gap between the statistical and machine learning fields, we propose a novel Interpretable Neural Network Regression (INNER), which combines the strengths of statistical and DNN models. We use the proposed INNER to conduct individualized risk assessment of preoperative opioid use. Intensive simulations and an analysis of 34,186 patients expecting surgery in the Analgesic Outcomes Study (AOS) show that the proposed INNER not only can accurately predict the preoperative opioid use using preoperative characteristics as DNN, but also can estimate the patient-specific odds of opioid use without pain and the odds ratio of opioid use for a unit increase in the reported overall body pain, leading to more straight-forward interpretations of the tendency to use opioids than DNN. Our results identify the patient characteristics that are strongly associated with opioid use and is largely consistent with the previous findings, providing evidence that INNER is a useful tool for individualized risk assessment of preoperative opioid use.
据报道，术前使用阿片类药物与更高的术前阿片类药物需求、更差的术后结果以及术后医疗保健利用率和支出增加有关。了解术前使用阿片类药物的风险有助于建立以患者为中心的疼痛管理。在机器学习领域，深度神经网络（DNN）因其出色的预测能力已成为一种强大的风险评估手段；然而，黑箱算法可能使结果的可解释性不如统计模型。为弥合统计和机器学习领域之间的差距，我们提出了一种新颖的可解释神经网络回归（INNER），它结合了统计模型和DNN模型的优势。我们使用所提出的INNER对术前使用阿片类药物进行个性化风险评估。在镇痛结果研究（AOS）中对34186名预期手术患者进行的密集模拟和分析表明，所提出的INNER不仅可以像DNN一样使用术前特征准确预测术前阿片类药物的使用情况，还可以估计无疼痛时患者使用阿片类药物的特定几率以及报告的全身疼痛每增加一个单位时使用阿片类药物的几率比，从而比DNN更直接地解释使用阿片类药物的倾向。我们的结果确定了与阿片类药物使用密切相关的患者特征，并且在很大程度上与先前的研究结果一致，这提供了证据表明INNER是术前使用阿片类药物个性化风险评估的有用工具。
