Department of Physiological Nursing, School of Nursing, University of California San Francisco (UCSF), San Francisco, California, USA.
Department of Nursing, Columbia University Medical Center, New York, New York, USA.
BMJ Support Palliat Care. 2024 Jan 8;13(e3):e1351-e1362. doi: 10.1136/spcare-2023-004319.
In a sample of patients with cancer (n=1145) who were assessed during the height of the COVID-19 pandemic, latent profile analysis was used to identify subgroups of patients with distinct stress profiles and to evaluate for differences in demographic and clinical characteristics and symptom severity scores among these subgroups.
Patients completed measures of cancer-specific and COVID-19 stress, global stress, social isolation, loneliness, depression, state and trait anxiety, morning and evening fatigue, morning and evening energy, sleep disturbance, cognitive function, and pain. Latent profile analysis was used to identify subgroups of patients with distinct stress profiles. Differences among the subgroups in study measures were evaluated using parametric and non-parametric tests.
Using clinically meaningful cut-off scores for the stress measures, four distinct stress profiles were identified (ie, none class (51.3%); low stress and moderate loneliness class (24.4%), high stress and moderate loneliness class (14.0%), and very high stress and moderately high loneliness class (high, 10.3%)). Risk factors associated with membership in the high class included: younger age, lower annual household income, lower functional status and higher comorbidity burden. The two worst stress profiles reported clinically meaningful levels of all of the common symptoms associated with cancer and its treatments.
Findings from this study, obtained prior to the availability of COVID-19 vaccines and anti-viral medications, provide important 'benchmark data' to evaluate for changes in stress and symptom burden in patients with cancer in the postvaccine era and in patients with long COVID-19.
在一项针对癌症患者（n=1145）的研究中，这些患者是在 COVID-19 大流行期间接受评估的，使用潜在剖面分析来识别具有不同压力特征的患者亚组，并评估这些亚组在人口统计学和临床特征以及症状严重程度评分方面的差异。
患者完成了癌症特异性和 COVID-19 压力、总体压力、社会隔离、孤独感、抑郁、状态和特质焦虑、早晚疲劳、早晚能量、睡眠障碍、认知功能和疼痛的测量。使用潜在剖面分析来识别具有不同压力特征的患者亚组。使用参数和非参数检验评估亚组之间研究测量的差异。
使用压力测量的临床有意义的截断值，确定了四个不同的压力特征亚组（即无压力和中度孤独亚组（51.3%）、低压力和中度孤独亚组（24.4%）、高压力和中度孤独亚组（14.0%）和非常高压力和中度高孤独亚组（高，10.3%））。与高亚组成员身份相关的风险因素包括：年龄较小、年收入较低、功能状态较低和合并症负担较高。两个最差的压力特征报告了与癌症及其治疗相关的所有常见症状的临床有意义的水平。
这项研究在 COVID-19 疫苗和抗病毒药物可用之前进行，提供了重要的“基准数据”，可用于评估癌症患者在后疫苗时代和长 COVID-19 患者的压力和症状负担变化。
