Breakthrough Cancer Pain Clinical Features and Differential Opioids Response: A Machine Learning Approach in Patients With Cancer From the IOPS-MS Study

Abstract

PURPOSE A large proportion of patients with cancer suffer from breakthrough cancer pain (BTcP). Several unmet clinical needs concerning BTcP treatment, such as optimal opioid dosages, are being investigated. In this analysis the hypothesis, we explore with an unsupervised learning algorithm whether distinct subtypes of BTcP exist and whether they can provide new insights into clinical practice. METHODS Partitioning around a k-medoids algorithm on a large data set of patients with BTcP, previously collected by the Italian Oncologic Pain Survey group, was used to identify possible subgroups of BTcP. Resulting clusters were analyzed in terms of BTcP therapy satisfaction, clinical features, and use of basal pain and rapid-onset opioids. Opioid dosages were converted to a unique scale and the BTcP opioids-to-basal pain opioids ratio was calculated for each patient. We used polynomial logistic regression to catch nonlinear relationships between therapy satisfaction and opioid use. RESULTS Our algorithm identified 12 distinct BTcP clusters. Optimal BTcP opioids-to-basal pain opioids ratios differed across the clusters, ranging from 15% to 50%. The majority of clusters were linked to a peculiar association of certain drugs with therapy satisfaction or dissatisfaction. A free online tool was created for new patients’ cluster computation to validate these clusters in future studies and provide handy indications for personalized BTcP therapy. CONCLUSION This work proposes a classification for BTcP and identifies subgroups of patients with unique efficacy of different pain medications. This work supports the theory that the optimal dose of BTcP opioids depends on the dose of basal opioids and identifies novel values that are possibly useful for future trials. These results will allow us to target BTcP therapy on the basis of patient characteristics and to define a precision medicine strategy also for supportive care.

Patients’ Satisfaction with Breakthrough Cancer Pain Therapy: A Secondary Analysis of IOPS-MS Study

“…Patient satisfaction with treatment is an increasingly outcome measure that modulates the pain experience, while assessing the quality and effectiveness of symptom management, especially for BTcP. 11 Although several ancillary analyses of the IOPS-MS study reported descriptive data about patients and pain treatment relationship, 12 , 13 there are some important data worth noting. The findings of this study provided key information regarding the factors influencing the level of satisfaction with BTcP medication.…”

Multidimensional Statistical Technique for Interpreting the Spontaneous Breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study

“…The main challenge for BTcP management is the implementation of new procedures that can be useful for both diagnostic and therapeutic approaches. Recently, Pantano et al [19] performed a machine learning approach to identify possible subgroups of both types of BTcP. Their algorithm was based on BTcP therapy satisfaction, clinical features, basal pain, and rapid-onset opioids.…”

Multidimensional Statistical Technique for Interpreting the Spontaneous breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study

“…The main challenge for BTcP management is the implementation of new procedures that can be useful for both diagnostic and therapeutic approaches. Recently, Pantano et al [9] performed a machine learning approach to identify possible subgroups of both types of BTcP. Their algorithm was based on BTcP therapy satisfaction, clinical features, basal pain, and rapid-onset opioids.…”