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 rapidonset 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.
Pantano, F., Manca, P., Armento, G., Zeppola, T., Onorato, A., Iuliani, M., et al. (2020). Breakthrough Cancer Pain Clinical Features and Differential Opioids Response: A Machine Learning Approach in Patients With Cancer From the IOPS-MS Study. JCO PRECISION ONCOLOGY(4), 1339-1349 [10.1200/PO.20.00158].
|Tipologia:||Articolo su rivista|
|Citazione:||Pantano, F., Manca, P., Armento, G., Zeppola, T., Onorato, A., Iuliani, M., et al. (2020). Breakthrough Cancer Pain Clinical Features and Differential Opioids Response: A Machine Learning Approach in Patients With Cancer From the IOPS-MS Study. JCO PRECISION ONCOLOGY(4), 1339-1349 [10.1200/PO.20.00158].|
|Settore Scientifico Disciplinare:||Settore MED/41|
|Revisione (peer review):||Esperti anonimi|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1200/PO.20.00158|
|Stato di pubblicazione:||Pubblicato|
|Data di pubblicazione:||nov-2020|
|Titolo:||Breakthrough Cancer Pain Clinical Features and Differential Opioids Response: A Machine Learning Approach in Patients With Cancer From the IOPS-MS Study|
|Autori:||Pantano, F; Manca, P; Armento, G; Zeppola, T; Onorato, A; Iuliani, M; Simonetti, S; Vincenzi, B; Santini, D; Mercadante, S; Marchetti, P; Cuomo, A; Caraceni, A; Mediati, RD; Vellucci, R; Mammucari, M; Natoli, S; Lazzari, M; Dauri, M; Adile, C; Airoldi, M; Azzarello, G; Blasi, L; Chiurazzi, B; Degiovanni, D; Fusco, F; Guardamagna, V; Liguori, S; Palermo, L; Mameli, S; Masedu, F; Mazzei, T; Melotti, RM; Menardo, V; Miotti, D; Moroso, S; Pascoletti, G; De Santis, S; Orsetti, R; Papa, A; Ricci, S; Scelzi, E; Sofia, M; Aielli, F; Valle, A; Tonini, G|
|Appare nelle tipologie:||01 - Articolo su rivista|