Genetic and Clinical Factors Associated with Opioid Response in Chinese Han Patients with Cancer Pain: An Exploratory Cross-Sectional Study

Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms

Objectives

Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machine learning (ML) model and evaluate its feasibility to predict opioid nonadherence in patients with cancer pain.

Methods

This was a secondary analysis from a cross-sectional study that included 1195 patients from March 1, 2018, to October 31, 2019. Five ML algorithms, such as logistic regression (LR), random forest, eXtreme Gradient Boosting, multilayer perceptron, and support vector machine, were used to predict opioid nonadherence in patients with cancer pain using 43 demographic and clinical factors as predictors. The predictive effects of the models were compared by the area under the receiver operating characteristic curve (AUC_ROC), accuracy, precision, sensitivity, specificity, and F1 scores. The value of the best model for clinical application was assessed using decision curve analysis (DCA).

Results

The best model obtained in this study, the LR model, had an AUC_ROC of 0.82, accuracy of 0.82, and specificity of 0.71. The DCA showed that clinical interventions for patients at high risk of opioid nonadherence based on the LR model can benefit patients. The strongest predictors for adherence were, in order of importance, beliefs about medicines questionnaire (BMQ)-harm, time since the start of opioid, and BMQ-necessity. Discussion. ML algorithms can be used as an effective means of predicting adherence to opioids in patients with cancer pain, which allows for proactive clinical intervention to optimize cancer pain management. This trial is registered with ChiCTR2000033576.

Effect of opioids for breathlessness in heart failure: a systematic review and meta-analysis

BACKGROUND

For the treatment of breathlessness in heart failure (HF), most textbooks advocate the use of opioids. Yet, meta-analyses are lacking.

METHODS

A systematic review was performed for randomised controlled trials (RCTs) assessing effects of opioids on breathlessness (primary outcome) in patients with HF. Key secondary outcomes were quality of life (QoL), mortality and adverse effects. Cochrane Central Register of Controlled Trials, MEDLINE and Embase were searched in July 2021. Risk of bias (RoB) and certainty of evidence were assessed by the Cochrane RoB 2 Tool and Grading of Recommendations Assessment, Development and Evaluation criteria, respectively. The random-effects model was used as primary analysis in all meta-analyses.

RESULTS

After removal of duplicates, 1180 records were screened. We identified eight RCTs with 271 randomised patients. Seven RCTs could be included in the meta-analysis for the primary endpoint breathlessness with a standardised mean difference of 0.03 (95% CI -0.21 to 0.28). No study found statistically significant differences between the intervention and placebo. Several key secondary outcomes favoured placebo: risk ratio of 3.13 (95% CI 0.70 to 14.07) for nausea, 4.29 (95% CI 1.15 to 16.01) for vomiting, 4.77 (95% CI 1.98 to 11.53) for constipation and 4.42 (95% CI 0.79 to 24.87) for study withdrawal. All meta-analyses revealed low heterogeneity (I2 in all these meta-analyses was <8%).

CONCLUSION

Opioids for treating breathlessness in HF are questionable and may only be the very last option if other options have failed or in case of an emergency.

PROSPERO REGISTRATION NUMBER

CRD42021252201.

Pharmacogenomics of Cancer Pain Treatment Outcomes in Asian Populations: A Review

In advanced cancer, pain is a poor prognostic factor, significantly impacting patients' quality of life. It has been shown that up to 30% of cancer patients in Southeast Asian countries may receive inadequate analgesia from opioid therapy. This significant under-management of cancer pain is largely due to the inter-individual variability in opioid dosage and relative efficacy of available opioids, leading to unpredictable clinical responses to opioid treatment. Single nucleotide polymorphisms (SNPs) cause the variability in opioid treatment outcomes, yet their association in Asian populations remains unclear. Therefore, this review aimed to evaluate the association of SNPs with variability in opioid treatment responses in Asian populations. A literature search was conducted in Medline and Embase databases and included primary studies investigating the association of SNPs in opioid treatment outcomes, namely pharmacokinetics, opioid dose requirements, and pain control among Asian cancer patients. The results show that CYP2D6*10 has the most clinical relevance in tramadol treatment. Other SNPs such as rs7439366 (UGT2B7), rs1641025 (ABAT) and rs1718125 (P2RX7) though significant have limited pharmacogenetic implications due to insufficient evidence. OPRM1 rs1799971, COMT rs4680 and ABCB1 (rs1045642, rs1128503, and rs2032582) need to be further explored in future for relevance in Asian populations.

Clinical Observation of the Effects of Oral Opioid on Inflammatory Cytokines and Gut Microbiota in Patients with Moderate to Severe Cancer Pain: A Retrospective Cohort Study

Introduction

Methods

Results

Conclusion