Su Wenhao, Li Xueling, Wang Yanru
Nursing Department, The Quzhou Affiliated of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China.
School of Nursing, Hainan Vocational University of Science and Technology, Haikou, Hainan, China.
PLoS One. 2025 Jul 17;20(7):e0322651. doi: 10.1371/journal.pone.0322651. eCollection 2025.
Chronic cancer pain is very common symptom in cancer patients, but this issue has not been satisfactorily resolved by the conventional three-step analgesic therapy. There are multiple non-pharmacological interventions for managing chronic cancer pain, but we haven't reached a consensus on which non pharmacological treatment is the best and these treatments are lack of high-quality evidence. In order to identify the most effective non-pharmaceutical therapy alternatives and investigate further possible medication interventions, this study will use network meta-analysis to assess the therapeutic effects of pharmacological and non-pharmacological treatments on chronic cancer pain patients and support clinical decision-making by prioritizing therapies according to the most valuable clinical outcomes for these patients.
We will carry out a systematic search of published randomized controlled trials (group, crossover, and parallel) in the PubMed, Web of Science, Cochrane Library, MEDLINE, Embase, and CINAHL databases, without language or date restrictions, in accordance with the PRISMA for Network Meta-Analyses (PRISMA-NMA) guidelines. Included studies must evaluate the effects of pharmacological and non-pharmacological treatments in patients with chronic cancer pain. Adult chronic cancer pain patients (≥ 18 years old) receiving pharmacological or non-pharmacological treatment will be our target participants. Our primary outcomes will be pain intensity, total effective rate of treatment, onset time, and quality of Life (QoL); Adverse reaction will be our secondary outcome. We'll utilize the mean difference (MD) for continuous variables, the odds ratio (OR) for binary variables, and the 95% confidence interval (CI) for interval estimates. The Cochrane Bias Risk Tool (RoB2.0) will be used to assess the bias risk of every RCT trial included in NMA. We will use Review Manager 5.3 software to conduct heterogeneity testing and meta-analysis. The network meta-analysis will be performed by ADDIS1.16.8 software. The Confidence in Network Meta-analysis (CINeMA) framework will be used to evaluate the level of confidence in the NMA results. Besides, we will use SUCRA for ranking the network meta-analysis results, and we will also apply normalized entropy to verify the accuracy of the SUCRA ranking outcomes.
This network meta-analysis will compare the efficacy of pharmacological versus non-pharmacological treatments for pain intensity in chronic cancer pain patients. The final analysis results may be significantly heterogeneous, because the population with cancerous pain suffers from different types of cancers. Owing to the databases primary reliance on our listed databases for inclusion, potentially valuable research will be overlooked.
This study has been registered in the PROSPERO database (CRD42024505214).
慢性癌痛是癌症患者非常常见的症状，但传统的三步镇痛疗法尚未令人满意地解决这一问题。有多种非药物干预措施用于管理慢性癌痛，但对于哪种非药物治疗是最佳的，我们尚未达成共识，而且这些治疗缺乏高质量证据。为了确定最有效的非药物治疗替代方案并研究进一步可能的药物干预措施，本研究将使用网络荟萃分析来评估药物和非药物治疗对慢性癌痛患者的治疗效果，并通过根据这些患者最有价值的临床结局对治疗进行排序来支持临床决策。
我们将按照网络荟萃分析的系统评价和Meta分析首选报告项目（PRISMA-NMA）指南，在PubMed、科学网、考克兰图书馆、MEDLINE、Embase和CINAHL数据库中对已发表的随机对照试验（组间、交叉和平行）进行系统检索，无语言或日期限制。纳入的研究必须评估药物和非药物治疗对慢性癌痛患者的效果。接受药物或非药物治疗的成年慢性癌痛患者（≥18岁）将是我们的目标参与者。我们的主要结局将是疼痛强度、治疗总有效率、起效时间和生活质量（QoL）；不良反应将是我们的次要结局。对于连续变量，我们将使用均数差（MD），对于二分类变量，我们将使用比值比（OR），并使用95%置信区间（CI）进行区间估计。将使用Cochrane偏倚风险工具（RoB2.0）评估纳入网络荟萃分析的每个随机对照试验的偏倚风险。我们将使用Review Manager 5.3软件进行异质性检验和荟萃分析。网络荟萃分析将由ADDIS1.16.8软件进行。将使用网络荟萃分析置信度（CINeMA）框架评估网络荟萃分析结果的置信水平。此外，我们将使用累积排序曲线下面积（SUCRA）对网络荟萃分析结果进行排序，我们还将应用标准化熵来验证SUCRA排序结果的准确性。
这项网络荟萃分析将比较药物和非药物治疗对慢性癌痛患者疼痛强度的疗效。最终分析结果可能存在显著异质性，因为癌痛患者群体患有不同类型的癌症。由于数据库主要依赖我们列出的数据库进行纳入，可能有价值的研究将被忽视。
本研究已在国际前瞻性系统评价注册库（PROSPERO）数据库中注册（CRD42024505214）。
