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ObjectivesVariable 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.”MethodsOut 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).ResultsMedians 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%.ConclusionThe 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.

The I-MPSS showed satisfactory psychometric and validation properties. With adequate feasibility, it enabled the screening of mixed non-cancer-pain outpatients in three chronicity/prognostic stages. Results are sufficient to warrant its use for a subsequent impact study as a prognostic model and screening tool for referring pain patients.

Biased pain evaluation due to automated heuristics driven by symptom uncertainty may undermine pain treatment; medical evidence moderators are thought to play a role in such circumstances. We explored, in this cross-sectional survey, the effect of such moderators (e.g., nurse awareness of patients' pain experience and treatment) on the agreement between n = 862 inpatients' self-reported pain and n = 115 nurses' pain ratings using a numerical rating scale. We assessed the mean of absolute difference, agreement (κ-statistics), and correlation (Spearman rank) of inpatients and nurses' pain ratings and analyzed congruence categories' (CCs: underestimation, congruence, and overestimation) proportions and dependence upon pain categories for each medical evidence moderator (χ 2 analysis). Pain ratings agreement and correlation were limited; the CCs proportions were further modulated by the studied moderators. Medical evidence promoted in nurses overestimation of low and underestimation of high inpatients' self-reported pain. Knowledge of the negative influence of automated heuristics driven by symptoms uncertainty and medical-evidence moderators on pain evaluation may render pain assessment more accurate.

While no oxymorphone or noroxymorphone metabolites were detected, limited dialyzability of oxycodone and noroxycodone was documented along with insignificant postdialysis pain increment. This evidence will contribute toward considerations as to the safety of the use of oxycodone in dialysis patients in the future.