La Paz University Hospital, Madrid Autonomous University, Madrid, Spain.
J Clin Oncol. 2011 Mar 20;29(9):1159-67. doi: 10.1200/JCO.2010.31.6752. Epub 2011 Feb 22.
The purpose of this study was to identify factors associated with at-home death among patients with advanced cancer and create a decision-making model for discharging patients from an acute-care hospital.
We conducted an observational cohort study to identify the association between place of death and the clinical and demographic characteristics of patients with advanced cancer who received care from a palliative home care team (PHCT) and of their primary caregivers. We used logistic regression analysis to identify the predictors of at-home death.
We identified 380 patients who met the study inclusion criteria; of these, 245 patients (64%) died at home, 72 (19%) died in an acute-care hospital, 60 (16%) died in a palliative care unit, and three (1%) died in a nursing home. Median follow-up was 48 days. We included the 16 variables that were significant in univariate analysis in our decision-making model. Five variables predictive of at-home death were retained in the multivariate analysis: caregiver's preferred place of death, patients' preferred place of death, caregiver's perceived social support, number of hospital admission days, and number of PHCT visits. A subsequent reduced model including only those variables that were known at the time of discharge (caregivers' preferred place of death, patients' preferred place of death, and caregivers' perceived social support) had a sensitivity of 96% and a specificity of 81% in predicting place of death.
Asking a few simple patient- and family-centered questions may help to inform the decision regarding the best place for end-of-life care and death.
本研究旨在确定与晚期癌症患者居家死亡相关的因素,并为从急性护理医院出院的患者创建一个决策模型。
我们进行了一项观察性队列研究,以确定死亡地点与接受姑息家庭护理团队(PHCT)治疗的晚期癌症患者及其主要照顾者的临床和人口统计学特征之间的关联。我们使用逻辑回归分析来确定居家死亡的预测因素。
我们确定了 380 名符合研究纳入标准的患者;其中,245 名(64%)患者在家中死亡,72 名(19%)患者在急性护理医院死亡,60 名(16%)患者在姑息治疗病房死亡,3 名(1%)患者在疗养院死亡。中位随访时间为 48 天。我们将单因素分析中有意义的 16 个变量纳入决策模型。在多变量分析中保留了 5 个可预测居家死亡的变量:照顾者首选的死亡地点、患者首选的死亡地点、照顾者感知的社会支持、住院天数和 PHCT 访问次数。随后的简化模型仅包括出院时已知的变量(照顾者首选的死亡地点、患者首选的死亡地点和照顾者感知的社会支持),在预测死亡地点方面具有 96%的敏感性和 81%的特异性。
询问一些简单的以患者和家庭为中心的问题,可能有助于确定临终关怀和死亡的最佳地点。