Developing a machine learning-based predictive model for the analgesic effectiveness of transdermal fentanyl in cancer patients: an interpretable approach.

IF 3.2 4区 医学 Q2 PHARMACOLOGY & PHARMACY
Xiaogang Hu, Ya Chen, Yuelu Tang, Xiaoxiao Wang, Lixian Li, Chao Li, Wanyi Chen
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引用次数: 0

Abstract

Background: Cancer-related pain is a common and distressing symptom in patients with malignant tumors, significantly affecting quality of life. Transdermal fentanyl is a convenient opioid option for patients with intestinal obstruction or difficulty swallowing; however, some patients do not experience adequate pain relief. Predicting transdermal fentanyl analgesic effectiveness is crucial to optimize pain management.

Aim: This study aimed to develop a predictive model for transdermal fentanyl effectiveness in cancer patients.

Method: Clinical data from adult cancer pain patients at Chongqing University Cancer Hospital were analyzed (January 2020-December 2022). Logistic regression and feature selection were applied, followed by developing nine predictive models using Logistic Regression, Random Forest (RF), and Extreme Gradient Boosting. The receiver operating characteristic (ROC) curves, the Youden index, and the Brier score were used to evaluate the performance of the model. Cross-validation and SHapley Additive exPlanations (SHAP) analysis were used for validation and feature interpretation.

Results: Among 151 patients, 27.2% reported ineffectiveness of transdermal fentanyl. Logistic regression identified key factors of NRS, transdermal fentanyl dosage, BMI, and ALT. Among the nine models, RF Model 8 exhibited the best performance, achieving a ROC-AUC of 0.984 (95% CI: [0.968, 0.999]). This performance was further validated by the confusion matrix metrics and visualization results. The SHAP analysis highlighted BMI, lower doses, NRS, and ALT as predictors of transdermal fentanyl ineffectiveness.

Conclusion: The Random Forest model offers a valuable tool for predicting the effectiveness of transdermal fentanyl in cancer pain patients, supporting the refined assessment and management of pain.

开发一种基于机器学习的预测模型,用于癌症患者经皮芬太尼的镇痛效果:一种可解释的方法。
背景:肿瘤相关疼痛是恶性肿瘤患者常见的痛苦症状,严重影响患者的生活质量。经皮芬太尼是肠梗阻或吞咽困难患者的一种方便的阿片类药物选择;然而,一些患者并没有感受到足够的疼痛缓解。预测经皮芬太尼镇痛效果是优化疼痛管理的关键。目的:本研究旨在建立芬太尼对癌症患者经皮有效性的预测模型。方法:分析重庆大学肿瘤医院成人癌性疼痛患者的临床资料(2020年1月- 2022年12月)。应用逻辑回归和特征选择,然后利用逻辑回归、随机森林(RF)和极端梯度增强(Extreme Gradient Boosting)建立9个预测模型。采用受试者工作特征(ROC)曲线、约登指数(Youden index)和布里尔评分(Brier score)评价模型的性能。交叉验证和SHapley加性解释(SHAP)分析用于验证和特征解释。结果:151例患者中,27.2%报告经皮芬太尼无效。Logistic回归确定了NRS、芬太尼透皮剂量、BMI2和ALT为关键因素,其中RF模型8表现最佳,ROC-AUC为0.984 (95% CI:[0.968, 0.999])。混淆矩阵度量和可视化结果进一步验证了这一性能。SHAP分析强调,较低剂量、NRS和ALT是芬太尼透皮无效的预测因素。结论:随机森林模型为预测芬太尼透皮治疗癌症疼痛患者的有效性提供了有价值的工具,为疼痛的精细评估和管理提供了支持。
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来源期刊
CiteScore
4.10
自引率
8.30%
发文量
131
审稿时长
4-8 weeks
期刊介绍: The International Journal of Clinical Pharmacy (IJCP) offers a platform for articles on research in Clinical Pharmacy, Pharmaceutical Care and related practice-oriented subjects in the pharmaceutical sciences. IJCP is a bi-monthly, international, peer-reviewed journal that publishes original research data, new ideas and discussions on pharmacotherapy and outcome research, clinical pharmacy, pharmacoepidemiology, pharmacoeconomics, the clinical use of medicines, medical devices and laboratory tests, information on medicines and medical devices information, pharmacy services research, medication management, other clinical aspects of pharmacy. IJCP publishes original Research articles, Review articles , Short research reports, Commentaries, book reviews, and Letters to the Editor. International Journal of Clinical Pharmacy is affiliated with the European Society of Clinical Pharmacy (ESCP). ESCP promotes practice and research in Clinical Pharmacy, especially in Europe. The general aim of the society is to advance education, practice and research in Clinical Pharmacy . Until 2010 the journal was called Pharmacy World & Science.
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