背景:晚期癌症患者在疾病死亡阶段口服液摄入量减少。评估证据不充分,和管理,死亡中的水合作用。生物电阻抗分析(BIA)是一种身体成分评估工具。BIA有可能为晚期癌症的临床管理提供信息,通过检查水合状态和临床变量之间的关系。
目的:BIA用于确定水合状态之间的关联,症状,临床体征,晚期癌症的生活质量和生存率,包括那些正在死亡的人(即生命的最后一周)。
方法:我们在三个中心对晚期癌症患者进行了一项前瞻性观察性研究。使用预先同意方法进行临终水合评估。使用BIA阻抗指数(高度-H(m)2/电阻-R(欧姆))估算体内水分。反向回归用于识别因素(体征,症状,生活质量)预测H2/R。参与者在生命的最后7天用BIA进一步评估,以评估水合变化,及其与临床结局的关系。
结果:有125人参加(男性n=74(59.2%),女性,n=51(40.8%))。我们使用后向回归分析来描述统计模型来预测晚期癌症的水合状态。该模型表明,“较少的水合作用”(较低的H2/R)与女性相关(β=-0.39,p<0.001),食欲增加(β=-0.12,p=0.09),增加脱水评估量表评分(口干,干腋下,凹陷的眼睛-Beta=-0.19,p=0.006),和增加的呼吸困难(β=-0.15,p=0.03)。“更多的水合作用”(更高的H2/R)与水肿相关(β=0.49,p<0.001)。在垂死的参与者中(n=18,14.4%),水合状态(H2/R)与基线测量值相比没有显着差异(n=18,M=49.6,SD=16.0与M=51.0,SD=12.1;t(17)=0.64,p=0.53),并且与躁动没有显着相关(rs=-0.85,p=0.74),疼痛(rs=0.31,p=0.23)或呼吸道分泌物(rs=-0.34,p=0.19)。
结论:这是第一个使用生物阻抗报告模型(使用临床因素)来预测晚期癌症中的水合状态的研究。我们的数据证明了使用预先同意方法对垂死的人进行研究的可行性。这种方法可以潜在地改善证据基础(因此,护理质量)。未来的BIA研究可能涉及癌症的水合评估(根据类型和阶段)和相关变量(例如,疾病阶段,种族和性别)。进一步的工作可以使用BIA来确定水化研究的临床相关结果,并建立核心结果集,以评估水化如何影响癌症的症状和生活质量。
BACKGROUND: Oral fluid intake decreases in advanced cancer in the dying phase of illness. There is inadequate evidence to support the assessment, and management, of hydration in the dying. Bioelectrical impedance analysis (BIA) is a body composition assessment tool. BIA has the potential to inform clinal management in advanced cancer, by examining the relationships between hydration status and clinical variables.
OBJECTIVE: BIA was used to determine the association between hydration status, symptoms, clinical signs, quality-of-life and survival in advanced cancer, including those who are dying (i.e. in the last week of life).
METHODS: We conducted a prospective observational study of people with advanced cancer in three centres. Advance consent methodology was used to conduct hydration assessments in the dying. Total body water was estimated using the BIA Impedance index (Height - H (m)2 /Resistance - R (Ohms)). Backward regression was used to identify factors (physical signs, symptoms, quality of life) that predicted H2/R. Participants in the last 7 days of life were further assessed with BIA to assess hydration changes, and its relationship with clinical outcomes.
RESULTS: One hundred and twenty-five people participated (males n = 74 (59.2%), females, n = 51 (40.8%)). We used backward regression analysis to describe a statistical model to predict hydration status in advanced cancer. The model demonstrated that \'less hydration\' (lower H2/R) was associated with female sex (Beta = -0.39, p < 0.001), increased appetite (Beta = -0.12, p = 0.09), increased dehydration assessment scale score (dry mouth, dry axilla, sunken eyes - Beta = -0.19, p = 0.006), and increased breathlessness (Beta = -0.15, p = 0.03). \'More hydration\' (higher H2/R) was associated with oedema (Beta = 0.49, p < 0.001). In dying participants (n = 18, 14.4%), hydration status (H2/R) was not significantly different compared to their baseline measurements (n = 18, M = 49.6, SD = 16.0 vs. M = 51.0, SD = 12.1; t(17) = 0.64, p = 0.53) and was not significantly associated with agitation (rs = -0.85, p = 0.74), pain (rs = 0.31, p = 0.23) or respiratory tract secretions (rs = -0.34, p = 0.19).
CONCLUSIONS: This is the first study to use bioimpedance to report a model (using clinical factors) to predict hydration status in advanced cancer. Our data demonstrates the feasibility of using an advance consent method to conduct research in dying people. This method can potentially improve the evidence base (and hence, quality of care) for the dying. Future BIA research can involve hydration assessment of cancers (according to type and stage) and associated variables (e.g., stage of illness, ethnicity and gender). Further work can use BIA to identify clinically relevant outcomes for hydration studies and establish a core outcome set to evaluate how hydration affects symptoms and quality-of-life in cancer.