التفاصيل البيبلوغرافية
| العنوان: |
Further Clarification of Pain Management Complexity in Radiotherapy: Insights from Modern Statistical Approaches. |
| المؤلفون: |
Donati, Costanza Maria, Galietta, Erika, Cellini, Francesco, Di Rito, Alessia, Portaluri, Maurizio, De Tommaso, Cristina, Santacaterina, Anna, Tamburella, Consuelo, Mammini, Filippo, Di Franco, Rossella, Parisi, Salvatore, Cossa, Sabrina, Bianculli, Antonella, Ziccarelli, Pierpaolo, Ziccarelli, Luigi, Genovesi, Domenico, Caravatta, Luciana, Deodato, Francesco, Macchia, Gabriella, Fiorica, Francesco |
| المصدر: |
Cancers; Apr2024, Vol. 16 Issue 7, p1407, 12p |
| مصطلحات موضوعية: |
STATISTICAL models, RESEARCH funding, BREAST tumors, SCIENTIFIC observation, CANCER patient medical care, DESCRIPTIVE statistics, POPULATION geography, AGE distribution, ANALGESICS, LONGITUDINAL method, PAIN management, PAIN, RESEARCH, ALGORITHMS |
| مصطلحات جغرافية: |
ITALY |
| مستخلص: |
Simple Summary: This analysis of the ARISE study, a multicenter observational cohort trial, is based on a modern statistical approach, integrating the Least Absolute Shrinkage and Selection Operator algorithm and the Classification and Regression Tree analysis. The results of this study show significant shortcomings in pain management for breast cancer patients undergoing radiotherapy, particularly highlighting that younger patients and those with non-neoplastic pain, especially in southern and central Italy, experience even poorer pain management. This research underscores the urgent need for tailored pain management strategies in breast cancer patients, taking into account patient age, pain type, and geographic disparities to enhance care quality and outcomes for subjects across different regions. Background: The primary objective of this study was to assess the adequacy of analgesic care in radiotherapy (RT) patients, with a secondary objective to identify predictive variables associated with pain management adequacy using a modern statistical approach, integrating the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and the Classification and Regression Tree (CART) analysis. Methods: This observational, multicenter cohort study involved 1387 patients reporting pain or taking analgesic drugs from 13 RT departments in Italy. The Pain Management Index (PMI) served as the measure for pain control adequacy, with a PMI score < 0 indicating suboptimal management. Patient demographics, clinical status, and treatment-related factors were examined to discern the predictors of pain management adequacy. Results: Among the analyzed cohort, 46.1% reported inadequately managed pain. Non-cancer pain origin, breast cancer diagnosis, higher ECOG Performance Status scores, younger patient age, early assessment phase, and curative treatment intent emerged as significant determinants of negative PMI from the LASSO analysis. Notably, pain management was observed to improve as RT progressed, with a greater discrepancy between cancer (33.2% with PMI < 0) and non-cancer pain (73.1% with PMI < 0). Breast cancer patients under 70 years of age with non-cancer pain had the highest rate of negative PMI at 86.5%, highlighting a potential deficiency in managing benign pain in younger patients. Conclusions: The study underscores the dynamic nature of pain management during RT, suggesting improvements over the treatment course yet revealing specific challenges in non-cancer pain management, particularly among younger breast cancer patients. The use of advanced statistical techniques for analysis stresses the importance of a multifaceted approach to pain management, one that incorporates both cancer and non-cancer pain considerations to ensure a holistic and improved quality of oncological care. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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