Introduction

Neuropathic pain (NP) remains a major debilitating condition affecting more than 26% of breast cancer survivors worldwide. NP is diagnosed using a validated 10-items Douleur Neuropathique - 4 screening questionnaire which is administered 3 months after surgery and requires patient-doctor interaction. To develop an effective prognosis model admissible soon after surgery, without the need for patient-doctor interaction, we sought to [1] identify specific pain characteristics that can help determine which patients may be susceptible to NP after BC surgery, and 2) assess the utility of machine learning models developed in objective [1] as a knowledge discovery tool for downstream analysis.

Methods

The dataset is from a prospective cohort study of female patients scheduled to undergo breast cancer surgery for the first time at the Jewish General Hospital, Montreal, Canada between November 2014 and March 2019. NP was assessed at 3 months after surgery using Douleur Neuropathique – 4 interview scores (in short, DN4-interview; range: 0–7). For the primary analysis, we constructed six ML algorithms (least square, ridge, elastic net, random forest, gradient boosting, and neural net) to identify the most relevant predictors for DN4-interview score; and compared model performance based on root mean square error (RMSE). For the secondary analysis, we built a logistic classification model for neuropathic pain (DN4-interview score ≥ 3 versus DN4-interview score < 3) using the relevant-consensus-predictors from the primary analysis.

Results

Anxiety, type of surgery, preoperative baseline pain and acute pain on movement were identified as the most relevant predictors for DN4 - interview score. The least square regression model (RMSE = 1.43) is comparable in performance with random forest (RMSE = 1.39) and neural network model (RMSE = 1.50). The Gradient boosting model (RMSE = 1.16) outperformed the models compared including the penalized regression models (ridge regressions, RMSE = 1.28; and elastic net, RMSE = 1.31). In the secondary analysis, the preferred logistic regression classier for NP had an area under the curve (AUC) of 0.68 (95% CI = 0.57 to 0.79). Anxiety was significantly associated with the likelihood of NP (odds ratio = 2.18; 95% CI = 1.05–4.49). In comparison to their counterparts, the odds of NP were higher in participants with acute pain on movement or with present preoperative baseline pain or participants who performed total mastectomy surgery, but the differences were not statistically significant.

Conclusions

Modern machine learning models show improvements over traditional least square regression in predicting of DN4-interview score. Penalized regression methods and the Gradient boosting model out-perform other models. As a predictor discovery tool, machine learning algorithms identify relevant predictors for DN4-interview score that remain statistically significant indicators of neuropathic pain in the classification model. Anxiety, type of surgery and acute pain on movement remain the most useful predictors for neuropathic pain.

中文翻译：

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使用机器学习识别乳腺癌手术后神经性疼痛的预测因素。

介绍

神经性疼痛（NP）仍然是一种使人衰弱的主要疾病，影响了全世界26％以上的乳腺癌幸存者。NP使用经过验证的10项Douleur Neuropathique-4筛查问卷进行诊断，该问卷在手术后3个月内进行，需要患者与医生的互动。为了建立一种在手术后不久就可被接受且不需要医患互动的有效的预后模型，我们试图[1]确定特定的疼痛特征，以帮助确定哪些患者在BC手术后可能易患NP，以及2）评估目标[1]中开发的机器学习模型的实用程序，作为下游分析的知识发现工具。

方法

该数据集来自对2014年11月至2019年3月之间在加拿大蒙特利尔的犹太人综合医院首次进行乳腺癌手术的女性患者进行的前瞻性队列研究。在术后3个月使用Douleur Neuropathique对NP进行了评估– 4面试成绩（简而言之，DN4面试；范围：0-7）。对于初步分析，我们构建了6种ML算法（最小二乘，岭，弹性网，随机森林，梯度提升和神经网络），以识别与DN4访谈得分最相关的预测因子；并根据均方根误差（RMSE）比较了模型性能。对于次要分析，我们使用主要分析中的相关共识预测指标，建立了神经性疼痛的逻辑分类模型（DN4访谈得分≥3与DN4访谈得分<3）。

结果

焦虑，手术类型，术前基线疼痛和运动中的急性疼痛被确定为DN4的最相关预测因子-面试评分。最小二乘回归模型（RMSE = 1.43）在性能上与随机森林（RMSE = 1.39）和神经网络模型（RMSE = 1.50）相当。梯度提升模型（RMSE = 1.16）优于所比较的模型，包括惩罚回归模型（岭回归，RMSE = 1.28；弹性网，RMSE = 1.31）。在二级分析中，NP的首选逻辑回归分类器的曲线下面积（AUC）为0.68（95％CI = 0.57至0.79）。焦虑与NP的可能性显着相关（优势比= 2.18； 95％CI = 1.05-4.49）。与同行相比，

结论

现代机器学习模型在预测DN4-面试成绩方面显示出优于传统的最小二乘回归的改进。惩罚式回归方法和“梯度”提升模型的性能优于其他模型。作为预测器发现工具，机器学习算法可识别DN4访谈评分的相关预测器，这些预测器仍是分类模型中神经性疼痛的统计显着指标。焦虑，手术类型和运动中的急性疼痛仍然是神经性疼痛的最有用预测指标。