基于COSMIN指南对癌症患者支持性照护需求量表的系统评价

背景国内外用于评估癌症患者支持性照护需求的量表较多，但有关此类量表质量的标准化评价研究及不同量表间的横向比较研究较为缺乏，也少有研究者对此类量表的测量特性进行系统的整合与评价。目的评价中文版癌症患者支持性照护需求量表的测量学性能及研究的方法学质量。方法2021年4月检索中国知网、万方数据知识服务平台、维普中文科技期刊全文数据库、中国生物医学文献数据库、PubMed、EmBase、Web of Science、CINAHL Complete数据库，获取有关中文版癌症患者支持性照护需求量表测量学性能评价的研究，检索时限均为建库至2021年3月30日。由两位研究者独立筛选文献、提取资料后，采用健康测量工具遴选标准（COSMIN）系统综述指南，在对量表的测量特性及研究的方法学质量进行评价的基础上，综合评定中文版癌症患者支持性照护需求评估量表各测量特性的证据等级，并形成对于量表的最终推荐意见。采用描述分析法对评价结果进行汇总、分析。结果共纳入15项研究，涉及8个中文版癌症患者支持性照护需求评估量表〔癌症患者支持性照护需求简明问卷中文版（SCNS-SF34）、中文版支持性照护需求筛查工具（SCNS-ST9-C）、癌症患者综合需求评估量表（CNAT）、癌症需求简明问卷（CNQ-SF）、中文版癌症患者未满足需求量表（CaSUN-C）、癌症患者未满足需求简明量表（SF-SUNS）、晚期癌症患者需求评估问卷（ACNQ-41）、晚期癌症患者需求评估表简表（ACNQ-29）〕。就量表的测量特性质量而言，除ACNQ-29的内容效度为"未提及"外，其余7个量表的内容效度均为"不确定"；除CaSUN-C、SF-SUNS的结构效度为"充分"外，其余6个量表的结构效度均为"不确定"；SCNS-SF34、CNQ-SF、CaSUN-C、SF-SUNS的内部一致性为"充分"，ACNQ-41的内部一致性为"不充分"，其余3个量表的内部一致性为"不确定"；CNAT、CNQ-SF、ACNQ-29的假设检验为"未提及"，CaSUN-C、SF-SUNS、ACNQ-41的假设检验为"不确定"，SCNS-SF34、SCNS-ST9-C的假设检验为"充分"；除ACNQ-41的稳定性为"不充分"，SCNS-ST9-C、ACNQ-29的稳定性为"未提及"外，其余5个量表的稳定性均为"充分"；仅SCNS-SF34的跨文化效度为"充分"，其余7个量表的跨文化效度均为"未提及"。8个量表的推荐等级均为B级。结论SCNS-SF34的测量特性得到了最为全面的评价，其具有较好的信效度，且临床应用可行性高，可暂时被推荐使用，但上述结论仍有待更多高质量证据加以支撑。     

Impact of the COVID-19 pandemic on the hospital attendance of patients with primary cervical cancer in Heilongjiang,China

Information regarding the impact of the coronavirus disease 2019 (COVID-19) pandemic on cervical cancer in mainland China is lacking. We explored its impact on the hospital attendance of patients with primary cervical cancer. We included 1918 patients with primary cervical cancer who initially attended Harbin Medical University Cancer Hospital between January 23, 2019, and January 23, 2021. Attendance decreased by 31%, from 1135 in 2019 to 783 in 2020, mainly from January to June (𝜒² = 73.362, P < .001). The percentage of patients detected by screening decreased from 12.1% in January-June 2019 to 5.8% in January-June 2020 (𝜒² = 7.187, P = .007). Patients with stage I accounted for 28.4% in 2020 significantly lower than 36.6% in 2019 (𝜒² = 14.085, P < .001), and patients with stage III accounted for 27.1% in 2020 significantly higher than 20.5% in 2019 (𝜒² = 11.145, P < .001). Waiting time for treatment was extended from 8 days (median) in January-June and July-December 2019 to 16 days in January-June (𝜒² = 74.674, P < .001) and 12 days in July-December 2020 (𝜒² = 37.916, P < .001). Of the 179 patients who delayed treatment, 164 (91.6%) were for the reasons of the healthcare providers. Compared to 2019, the number of patients in Harbin or non-Harbin in Heilongjiang Province and outside the province decreased, and cross-regional medical treatment has been hindered. The COVID-19 pandemic has negatively impacted cervical cancer patient attendance at the initial phase. These results are solid evidence that a strategy and mechanism for the effective attendance of cervical cancer patients in response to public health emergencies is urgently needed.     

慢性疼痛中枢调控网络的研究现状与思考＊

慢性疼痛是一种复杂的身心疾病，包括躯体痛觉异常、认知障碍、负性情绪等多个方面的改变，同时伴随着神经系统的功能以及结构的改变。本文将对慢性疼痛与下行疼痛调节通路、疼痛情感-认知调控网络以及中脑边缘奖赏网络的相关性，以及针刺镇痛的中枢机制相关研究文献进行综述，旨在探讨下行疼痛调节通路、疼痛情感-认知调控网络以及中脑边缘奖赏网络在慢痛发生机制中的作用，为临床治疗慢性疼痛类疾病提供更优势的治疗方案。     

Risk as a pattern over time: Delineation of time-dependent risk factors in biological,psychological, and social variables in cancer patients

Background

Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time.

Methods

Patients who attended the authors’ outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model.

Results

The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0–4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short-term, medium-term, and long-term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time-independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long-term.

Conclusions

The findings of this study allow the development of comprehensive patient-specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care.