โก Quick Summary
This retrospective study evaluated the effectiveness of the WHO analgesic ladder in colorectal cancer patients and developed machine learning models to predict treatment response for precision pain management. The findings revealed that 73.8% of patients experienced clinically significant pain improvement, with the Random Forest model achieving an impressive AUC of 0.9167.
๐ Key Details
- ๐ Dataset: 107 oncological patients, with a focus on 42 colorectal cancer patients
- ๐งฉ Features used: Pain scores, demographics, disease staging, metastatic patterns, analgesic usage
- โ๏ธ Technology: Machine learning algorithms including Random Forest, CatBoost, XGBoost, and Neural Network
- ๐ Performance: Random Forest: AUC 0.9167
๐ Key Takeaways
- ๐ Pain management in cancer patients remains a critical clinical challenge.
- ๐ก The WHO analgesic ladder was validated as effective for colorectal cancer patients.
- ๐ฉโ๐ฌ Machine learning models can predict treatment response, enhancing precision medicine.
- ๐ 73.8% of colorectal cancer patients achieved clinically significant pain improvement.
- ๐ Variability in pain response was noted among patients with different metastatic sites.
- ๐ค Random Forest emerged as the most effective predictive model.
- ๐ Study conducted between July and September 2022.
- ๐ PMID: 41155728.
๐ Background
Cancer pain is a prevalent issue that significantly impacts the quality of life for patients. The World Health Organization (WHO) analgesic ladder provides a framework for pain management, yet its effectiveness can vary among different cancer types. This study aimed to explore the effectiveness of this approach specifically in patients with colorectal cancer, while also leveraging advanced machine learning techniques to enhance treatment personalization.
๐๏ธ Study
Conducted as a retrospective observational study, this research analyzed data from 107 oncological patients, focusing on a subgroup of 42 colorectal cancer patients hospitalized from July to September 2022. Pain assessments were conducted using numerical rating scales at baseline and during follow-up, while various clinical variables were collected to inform the analysis.
๐ Results
The study found that 73.8% of colorectal cancer patients experienced clinically significant pain improvement, with a mean reduction of 2.62 points and a median improvement of 3.00 points. Notably, the presence of visceral metastases correlated with a median improvement of 3.00 points, while patients with bone metastases exhibited a wide range of responses. The Random Forest model demonstrated optimal predictive performance with an AUC of 0.9167, identifying key features such as baseline pain score and analgesic usage.
๐ Impact and Implications
The findings from this study underscore the importance of personalized pain management strategies in oncology. By validating the effectiveness of the WHO analgesic ladder and demonstrating the utility of machine learning models, this research paves the way for improved pain management protocols in colorectal cancer patients. The integration of AI-guided strategies could significantly enhance treatment outcomes and patient quality of life.
๐ฎ Conclusion
This study highlights the potential of machine learning in predicting treatment responses for pain management in cancer patients. With a significant proportion of colorectal cancer patients achieving pain relief, the findings support the implementation of precision medicine approaches in oncology. Continued research in this area is essential to further refine pain management strategies and improve patient care.
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Modeling Pain Dynamics and Opioid Response in Oncology Inpatients: A Retrospective Study with Application to AI-Guided Analgesic Strategies in Colorectal Cancer.
Abstract
Background and Objectives: Cancer pain continues to be a major clinical problem nowadays. This study aims to evaluate the World Health Organization (WHO) analgesic ladder effectiveness in patients with colorectal cancer and develop machine learning models to predict treatment response for precision pain management. Materials and Methods: In a retrospective observational study, a total of 107 oncological patients were analyzed, with a detailed subgroup analysis of 42 patients with colorectal cancer, hospitalized between July and September in 2022. The pain assessment used numerical rating scales at baseline and 2-3 weeks follow-up. Clinical variables included demographics, disease staging, metastatic patterns, analgesic progression, and medication usage. Machine learning algorithms (e.g., Random Forest, CatBoost, XGBoost, and Neural Network) were used to predict pain reduction outcomes. The UMAP dimensionality reduction and clustering identified the patient phenotypes. Results: Statistical analyses included descriptive methods, Chi-square and Mann-Whitney tests, and the models’ performance was evaluated by AUC. Among patients with colorectal cancer, 73.8% achieved clinically pain improvement, with a mean reduction of 2.62 points and median improvement of 3.00 points. The metastatic site significantly affected outcomes: visceral metastases patients showed median improvement of 3.00 points with high variability, patients with bone metastases demonstrated heterogeneous responses (range: -2.00 to +8.00 points), while non-metastatic patients exhibited consistent improvement. Random Forest achieved optimal predictive performance (AUC: 0.9167), identifying the baseline pain score, bone metastases, Fentanyl usage, anticonvulsants, and antispasmodics as key predictive features. The clustering analysis revealed two distinct phenotypes, requiring different analgesic intensities. Conclusions: This study validates the WHO analgesic ladder effectiveness while demonstrating superior outcomes in patients with colorectal cancer. The machine learning models successfully predict the treatment response with excellent discriminative ability, supporting precision medicine implementation in cancer pain management.
Author: [‘Froicu Armeanu EM’, ‘Onicescu Oniciuc OM’, ‘Creangฤ-Murariu I’, ‘Dascฤlu C’, ‘Gafton B’, ‘Afrฤsรขnie VA’, ‘Alexa-Stratulat T’, ‘Marinca MV’, ‘Puศcaศu DM’, ‘Miron L’, ‘Bacoanu G’, ‘Afrฤsรขnie I’, ‘Poroch V’]
Journal: Medicina (Kaunas)
Citation: Froicu Armeanu EM, et al. Modeling Pain Dynamics and Opioid Response in Oncology Inpatients: A Retrospective Study with Application to AI-Guided Analgesic Strategies in Colorectal Cancer. Modeling Pain Dynamics and Opioid Response in Oncology Inpatients: A Retrospective Study with Application to AI-Guided Analgesic Strategies in Colorectal Cancer. 2025; 61:(unknown pages). doi: 10.3390/medicina61101741