Background: This study was performed to evaluate the outcome after hemostatic radiotherapy (RT) of significant bleeding in incurable cancer patients. Methods: Patients treated by hemostatic RT between November 2006 and February 2010 were retrospectively analyzed. Bleeding was assessed according to the World Health Organization (WHO) scale (grade 0 = no bleeding, 1 = petechial bleeding, 2 = clinically significant bleeding, 3 = bleeding requiring transfusion, 4 = bleeding associated with fatality). The primary endpoint was bleeding at the end of RT. Key secondary endpoints included overall survival (OS) and acute toxicity. The bleeding score before and after RT were compared using the Wilcoxon signed rank test. Time to event endpoints were estimated using the Kaplan Meier method. Results: Overall 62 patients were analyzed including 1 patient whose benign cause of bleeding was pseudomyxoma peritonei. Median age was 66 (range, 37–93) years. Before RT, bleeding was graded as 2 and 3 in 24 (39%) and 38 (61%) patients, respectively. A median dose of 20 (range, 5–45) Gy of hemostatic RT was applied to the bleeding site. At the end of RT, there was a statistically significant difference in bleeding (p < 0.001); it was graded as 0 (n = 39), 1 (n = 12), 2 (n = 6), 3 (n = 4) and 4 (n = 1). With a median follow-up of 19.3 (range, 0.3-19.3) months, the 6-month OS rate was 43%. Forty patients died (65%); 5 due to bleeding. No grade 3 or above acute toxicity was observed. Conclusions: Hemostatic RT seems to be a safe and effective treatment for clinically and statistically significantly reducing bleeding in incurable cancer patients.

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.

Background: International oncology societies recommend early palliative care. Specific models to integrate early palliative care efficiently into clinical practice are debated. The authors designed a study to look at the quantitative and qualitative outcomes of an early palliative care intervention in oncological care to decrease stress and improve quality of life. Aims: To compare a single structured early palliative care intervention added to a usual oncology care in terms of distress and health-related quality of life at baseline compared to 6 months after enrollment. Design: This multicenter randomized controlled trial (NCT01983956) enrolled adult patients with advanced cancer. Participants were either randomly assigned to usual oncology care alone or usual care plus a structured early palliative care intervention. Setting/participants: One hundred fifty adult patients with a variety of advanced cancer diagnoses were randomized. Seventy-four participants were in the intervention and 76 participants in the control group. The primary outcome was the change in patient distress assessed by the National Comprehensive Cancer Network distress thermometer at 6 months. Health-related quality of life, the secondary outcome, was assessed by the Functional Assessment of Cancer Therapy–General Questionnaire. Results: The results showed no significant effect of the early palliative care intervention neither on patient distress nor on health-related quality of life. Conclusion: The addition of an early intervention to usual care for patients with advanced cancer did not improve distress or quality of life. Thus, patients may need more intensive early palliative care with continuous professional support to identify and address their palliative needs early.

In 2007, a first survey on undergraduate palliative care teaching in Switzerland has revealed major heterogeneity of palliative care content, allocation of hours and distribution throughout the 6 year curriculum in Swiss medical faculties. This second survey in 2012/13 has been initiated as part of the current Swiss national strategy in palliative care (2010 – 2015) to serve as a longitudinal monitoring instrument and as a basis for redefinition of palliative care learning objectives and curriculum planning in our country. As in 2007, a questionnaire was sent to the deans of all five medical faculties in Switzerland in 2012. It consisted of eight sections: basic background information, current content and hours in dedicated palliative care blocks, current palliative care content in other courses, topics related to palliative care presented in other courses, recent attempts at improving palliative care content, palliative care content in examinations, challenges, and overall summary. Content analysis was performed and the results matched with recommendations from the EAPC for undergraduate training in palliative medicine as well as with recommendations from overseas countries. There is a considerable increase in palliative care content, academic teaching staff and hours in all medical faculties compared to 2007. No Swiss medical faculty reaches the range of 40 h dedicated specifically to palliative care as recommended by the EAPC. Topics, teaching methods, distribution throughout different years and compulsory attendance still differ widely. Based on these results, the official Swiss Catalogue of Learning Objectives (SCLO) was complemented with 12 new learning objectives for palliative and end of life care (2013), and a national basic script for palliative care was published (2015). Performing periodic surveys of palliative care teaching at national medical faculties has proven to be a useful tool to adapt the national teaching framework and to improve the recognition of palliative medicine as an integral part of medical training.

Medications may become inappropriate for patients in the last phase of life and may even compromise their quality of life. To find consensus on recommendations regarding deprescribing of medications for adult patients with a life expectancy of six months or less. Experts working in palliative care or other relevant disciplines were asked to participate in this international Delphi study. Existing tools for deprescribing of medication in the last phase of life were integrated in a list of 42 recommendations regarding potential deprescription of various medication types. In two Delphi rounds, experts were asked to rate their agreement with each recommendation on a 5-point Likert-scale (strongly agree-strongly disagree). Recommendations were accepted, if at least 70% of the experts (strongly) agreed, the interquartile range (IQR) was one or less, and less than 10% strongly disagreed. About 47 experts from 10 countries participated (response rate 53%). In most cases (76%), consensus was reached on deprescribing recommendations for patients with a life expectancy of six months or less. The highest level of consensus was reached for recommendations on the deprescription of diuretics in case of decreasing fluid intake or increasing fluid loss, lipid modifying agents if prescribed for primary prevention, and vitamin K antagonists and direct oral anticoagulants in case of high bleeding risk. A high level of consensus was reached on recommendations on potential deprescription of several medications for patients with a life expectancy of six months or less.

Anticipatory planning for end of life requires a common language for discussion among patients, families, and professionals. Studies show that early Palliative Care (PC) interventions based on a problem-oriented approach can improve quality of life, support decision-making, and optimize the timing of medical treatment and transition to hospice services. The aim of this quality-improvement project was to develop a pragmatic structure meeting all clinical settings and populations needs. Based on the Medical Research Council (MRC) framework, a literature review identifying approaches commonly used in PC was performed. In addition, more than 500 hospital-based interprofessional consultations were analyzed. Identified themes were structured and compared to published approaches. We evaluated the clinical usefulness of this structure with an online survey among professionals. The emerged ‘SENS’-structure stands for: Symptoms patients suffer from; End-of-life decisions; Network around the patient delivering care; and Support for the carer. Evaluation among professionals has confirmed that the ‘SENS’-structure covers all relevant areas for anticipatory planning in PC. ‘SENS’ is useful in guiding patient-centered PC conversations and pragmatic anticipatory planning, alongside the regular diagnosis-triggered approach in various settings. Following this approach, ‘SENS’ may facilitate systematic integration of PC in clinical practice. Depending on clearly defined outcomes, this needs to be confirmed by future randomized controlled studies.