Samolsky Dekel Boaz Gedaliahu, Palma Marco, Sorella Maria Cristina, Gori Alberto, Vasarri Alessio, Melotti Rita Maria
Department of Medicine and Surgery Sciences, University of Bologna.
Department of Emergency-Urgency, Bologna's University Teaching Hospital, Policlinic S. Orsola-Malpighi.
J Pain Res. 2017 May 31;10:1327-1335. doi: 10.2147/JPR.S126132. eCollection 2017.
Variable prevalence and treatment of breakthrough pain (BTP) in different clinical contexts are partially due to the lack of reliable/validated diagnostic tools with prognostic capability. We report the statistical basis and performance analysis of a novel BTP scoring system based on the naïve Bayes classifier (NBC) approach and an 11-item IQ-BTP validated questionnaire. This system aims at classifying potential BTP presence in three likelihood classes: "High," "Intermediate," and "Low."
Out of a training set of n=120 mixed chronic pain patients, predictors associated with the BTP likelihood variables (Pearson's and/or Fisher's exact test) were employed for the NBC planning. Adjusting the binary classification to a three-likelihood classes case enabled the building of a scoring algorithm and to retrieve the score of each predictor's answer options and the Patient's Global Score (PGS). The latter medians were used to establish the NBC thresholds, needed to evaluate the scoring system performance (leave-one-out cross-validation).
Medians of PGS in the "High," "Intermediate," and "Low" likelihood classes were 3.44, 1.53, and -2.84, respectively. Leading predictors for the model (based on score differences) were flair frequency (ΔS=1.31), duration (ΔS=5.25), and predictability (ΔS=1.17). Percentages of correct classification were 63.6% for the "High" and of 100.0% for either the "Intermediate" and "Low" likelihood classes; overall accuracy of the scoring system was 90.9%.
The NBC-based BTP scoring system showed satisfactory performance in classifying potential BTP in three likelihood classes. The reliability, flexibility, and simplicity of this statistical approach may have significant relevance for BTP epidemiology and management. These results need further impact studies to generalize our findings.
在不同临床背景下，突破性疼痛（BTP）的患病率和治疗情况各异，部分原因是缺乏具有预后能力的可靠/经过验证的诊断工具。我们报告了一种基于朴素贝叶斯分类器（NBC）方法和一份经过验证的11项IQ-BTP问卷的新型BTP评分系统的统计基础和性能分析。该系统旨在将潜在BTP的存在分为三个可能性类别：“高”、“中”和“低”。
在n = 120名混合慢性疼痛患者的训练集中，使用与BTP可能性变量相关的预测因素（Pearson检验和/或Fisher精确检验）进行NBC规划。将二元分类调整为三可能性类别情况，从而能够构建评分算法，并获取每个预测因素答案选项的分数以及患者整体评分（PGS）。后者的中位数用于确定评估评分系统性能所需的NBC阈值（留一法交叉验证）。
“高”、“中”和“低”可能性类别的PGS中位数分别为3.44、1.53和 -2.84。该模型的主要预测因素（基于分数差异）为耀斑频率（ΔS = 1.31）、持续时间（ΔS = 5.25）和可预测性（ΔS = 1.17）。“高”可能性类别的正确分类百分比为63.6%，“中”和“低”可能性类别的正确分类百分比均为100.0%；评分系统的总体准确率为90.9%。
基于NBC的BTP评分系统在将潜在BTP分为三个可能性类别方面表现出令人满意的性能。这种统计方法的可靠性、灵活性和简单性可能对BTP的流行病学和管理具有重要意义。这些结果需要进一步的影响研究来推广我们的发现。
