Development and performance of a diagnostic/prognostic scoring system for breakthrough pain
Authors Samolsky Dekel BG, Palma M, Sorella MC, Gori A, Vasarri A, Melotti RM
Received 30 October 2016
Accepted for publication 2 May 2017
Published 31 May 2017 Volume 2017:10 Pages 1327—1335
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Amy Norman
Peer reviewer comments 3
Editor who approved publication: Dr Michael Schatman
Boaz Gedaliahu Samolsky Dekel,1–3 Marco Palma,4 Maria Cristina Sorella,1–3 Alberto Gori,3 Alessio Vasarri,3 Rita Maria Melotti1–3
1Department of Medicine and Surgery Sciences, University of Bologna, 2Department of Emergency-Urgency, Bologna’s University Teaching Hospital, Policlinic S. Orsola-Malpighi, 3University of Bologna, Post Graduate School of Anaesthesia and Intensive Care, 4Collegio Superiore, Istituto di Studi Superiori – ISS, University of Bologna, Bologna, Italy
Objectives: 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.”
Methods: Out of a training set of n=120 mixed chronic pain patients, predictors associated with the BTP likelihood variables (Pearson’s χ2 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).
Results: 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 (âS=1.31), duration (âS=5.25), and predictability (âS=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%.
Conclusion: 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.
Keywords: naïve Bayes classifier, breakthrough pain, multiclass scoring-system
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