The Italian Questionnaire for Cancer Breakthrough Pain Diagnosis, a Multicenter Validation Study

Abstract

Introduction:The literature lacks formally validated and reliable tools for the diagnosis of breakthrough cancer pain (BTcP). The Italian Questionnaire for BTcP diagnosis (IQ-BTP) is an 11-item questionnaire aimed at detecting validity\measurement invariance), and criterion validity; Reliability (internal consistency, reliability, and measurement error); Interpretability, and Responsiveness. Results: Seven palliative and pain management facilities in Italy recruited 280 patients, yielding 753 evaluations. Using the IQ-BTP, the rate of potential-BTcP was 27.2%, of which its likelihood was high in 52.7% of patients, intermediate in 38.5, and low in 8.8%. The BPI item scores differed significantly between the two IQ-BTP classes (no-BTcP and potential-BTcP classes). The correlation of the latter class with BPI items was significant but low. The IQ-BTcP showed two principal components, accounting for 66.6% of the variance. Cronbach's a was 0.71. The agreement rate between the gold standard and IQ-BTP outcomes was 82%. Cohen's j was 0.535. The IQ-BTP showed sensitivity and specificity of 69 and 86%, respectively. Conclusions: The IQ-BTP extensive formal validation showed satisfactory psychometric and validity properties. Its content, face, construct, and criterion validities and its reliability, interpretability, and responsiveness were shown. Its use enabled potential-BTcP to be identified and differentiated into three likelihood classes with direct therapeutic and epidemiological implications. The latter may be confirmed in future studies.

Retrospective Observational Study on the Characteristics of Pain and Associated Factors of Breakthrough Pain in Advanced Cancer Patients

“…Age, diagnosis, PS score, and background pain intensity were demonstrated to be associated with the BTP episodes and intensity in cancer patients [ 13 ]. The IQ-BTP is an 11-item questionnaire with satisfactory psychometric and validity properties, which enable potential BTP to be identified and differentiated into three likelihood classes (no BTP or high, intermediate, or low likelihood for BTP) [ 16 ]. Also, risk-prediction models for BTP were developed, and the accuracy between machine learning and regression techniques was compared, though in labour epidural analgesia [ 17 ].…”