To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
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.
Significance of results
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.
Significance of results:
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.
Email your librarian or administrator to recommend adding this to your organisation's collection.