Coping styles can be improved by dyadic palliative care interventions and may alleviate patients’ and family caregivers’ distress. Moreover, family caregivers’ preloss resilience protects against depression after bereavement. This study aimed to determine the types of coping styles can be encouraged to increase resilience.

A self-reported questionnaire survey was administered to family caregivers at the 4 palliative care units, and their resilience was assessed using the Connor–Davidson Resilience Scale (CD-RISC) and their coping styles were assessed using the Brief Coping Orientation to Problem Experienced, as well as their background characteristics.

Among 291 caregivers with a mean CD-RISC score of 56.2 (standard deviation: 16.13), internal locus of control, educational level, and history of psychotropic drug use were associated with resilience. After adjusting for the aforementioned factors, more frequent use of positive coping styles such as active coping (Spearman’s ρ = 0.29), acceptance (ρ = 0.29), positive reframing (ρ = 0.29), planning (ρ = 0.24), and humor (ρ = 0.18), was found to be associated with higher resilience. On the contrary, more frequent use of negative coping styles such as behavioral disengagement (ρ = −0.38), self-blame (ρ = −0.27), and denial (ρ = −0.14) was found to be associated with less resilience.

By assessing internal locus of control, educational level, and history of psychotropic medication use of family caregivers, as factors associated with their respective resilience, may help identify less resilient family caregivers who are at risk for developing major depression after bereavement. In addition, coping skill-based educational interventions targeting patients and their family caregivers that focus on specific coping styles associated with resilience may increase family caregivers’ resilience, resulting in less emotional distress and a lower risk of major depression after bereavement.

There is no widely used prognostic model for delirium in patients with advanced cancer. The present study aimed to develop a decision tree prediction model for a short-term outcome.

This is a secondary analysis of a multicenter and prospective observational study conducted at 9 psycho-oncology consultation services and 14 inpatient palliative care units in Japan. We used records of patients with advanced cancer receiving pharmacological interventions with a baseline Delirium Rating Scale Revised-98 (DRS-R98) severity score of ≥10. A DRS-R98 severity score of <10 on day 3 was defined as the study outcome. The dataset was randomly split into the training and test dataset. A decision tree model was developed using the training dataset and potential predictors. The area under the curve (AUC) of the receiver operating characteristic curve was measured both in 5-fold cross-validation and in the independent test dataset. Finally, the model was visualized using the whole dataset.

Altogether, 668 records were included, of which 141 had a DRS-R98 severity score of <10 on day 3. The model achieved an average AUC of 0.698 in 5-fold cross-validation and 0.718 (95% confidence interval, 0.627–0.810) in the test dataset. The baseline DRS-R98 severity score (cutoff of 15), hypoxia, and dehydration were the important predictors, in this order.

We developed an easy-to-use prediction model for the short-term outcome of delirium in patients with advanced cancer receiving pharmacological interventions. The baseline severity of delirium and precipitating factors of delirium were important for prediction.

The present study aims were (1) to identify the proportion of terminally ill cancer patients with desire for hastened death (DHD) receiving specialized palliative care, (2) to identify the reasons for DHD, and (3) to classify patients with DHD into some interpretable subgroups.

Advanced cancer patients admitted to 23 inpatients hospices/palliative care units in 2017 were enrolled. Data were prospectively obtained by the primarily responsible physicians. The presence/absence of DHD and reasons for DHD were recorded. A cluster analysis was performed to identify patterns of subgroups in patients with DHD.

Data from 971 patients, whose Richmond Agitation–Sedation Scale score at admission was zero and who died in palliative care units, were analyzed. The average age was 72 years, common primary cancer sites were the gastrointestinal tract (31%) and the liver/biliary ducts/pancreas (19%). A total of 174 patients (18%: 95% confidence interval, 16–20) expressed DHD. Common reasons for DHD were dependency (45%), burden to others (28%), meaninglessness (24%), and inability to engage in pleasant activities (24%). We identified five clusters of patients with DHD: cluster 1 (35%, 61/173): “physical distress,” cluster 2 (21%, 37/173): “dependent and burdensome,” cluster 3 (19%, 33/173): “hopelessness,” cluster 4 (17%, 30/173): “profound fatigue,” and cluster 5 (7%, 12/173): “extensive existential suffering.”

A considerable number of patients expressed DHD and could be categorized into five subgroups. These findings may contribute to the development of therapeutic strategies.

Little is known about the associations between family satisfaction with end-of-life care and caregiver burden. We conducted a researcher-assisted questionnaire survey to clarify the impact of caregiver burden on family satisfaction and to determine the types of burden that decrease family satisfaction.

Bereaved family caregivers of patients with advanced cancer who received our outreach palliative care service were retrospectively identified. Family satisfaction with the end-of-life care provided by the palliative care service and caregiver burden were quantified using the Japanese versions of the FAMCARE Scale and the Zarit Burden Interview (ZBI), respectively.

Our study subjects included 23 family caregivers. The mean scores on the FAMCARE Scale and the ZBI for the total population were 72.8 ± 11.2 and 22.8 ± 17.3, respectively, indicating moderate-to-high satisfaction and low-to-moderate burden. Caregiver burden had a strong negative correlation to family satisfaction with end-of-life care (Spearman's rho [ρ] = −0.560, p = 0.005), which remained after adjustment for potential confounders (standardized beta [β] = −0.563, p = 0.01). Several burden items—including loss of control, personal time, social engagement with others, feeling angry with the patient, feeling that the patient wants more help than he/she needs, and a wish to leave the care to someone else—were associated with decreased satisfaction. The major cause of dissatisfaction for family members included the information provided regarding prognosis, family conferences with medical professionals, and the method of involvement of family members in care decisions.

Caregiver burden can be a barrier to family satisfaction with end-of-life care at home. A home care model focused on caregiver burden could improve end-of-life experiences for patients and family caregivers.