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Purpose: Accurate prediction of survival is important to facilitate clinical decision-making and improve quality of care at the end of life. While it is well documented that survival prediction poses a challenge for treating physicians, the need for clinically valuable predictive factors has not been met. This study aims to quantify the prevalence of patient transfer 72 h before death onto the acute palliative care unit in a tertiary care center in Switzerland, and to identify factors predictive of 72-h mortality. Methods: All patients hospitalized between January and December 2020 on the acute palliative care unit of the Competence Center Palliative Care of the Department of Radiation Oncology at the University Hospital Zurich were assessed. Variables were retrieved from the electronic medical records. Univariable and multivariable logistic regressions were used to identify predictors of mortality. Results: A total of 398 patients were screened, of which 188 were assessed. Every fifth patient spent less than 72 h on the acute palliative care unit before death. In multivariable logistic regression analysis, predictors for 72-h mortality after transfer were no prior palliative care consult (p = 0.011), no advance care directive (p = 0.044), lower performance status (p = 0.035), lower self-care index (p = 0.003), and lower blood albumin level (p = 0.026). Conclusion: Late transfer to the acute palliative care unit is not uncommon, which can cause additional distress to patients and caretakers. Though clinically practical short-term survival predictors remain largely unidentified, early integration of palliative care should be practiced more regularly in patients with life-limiting illness. [ABSTRACT FROM AUTHOR]

Purpose: The aims of the present study were to investigate the symptom clusters in terminally ill patients with cancer using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 15 Palliative Care (EORTC QLQ-C15-PAL), and to examine whether these symptom clusters influenced prognosis. Methods: We analyzed data from 130 cancer patients hospitalized in the palliative care unit from June 2018 to December 2019 in an observational study. Principal component analysis was used to detect symptom clusters using the scored date of 14 items in the QLQ-C15-PAL, except for overall QOL, at the time of hospitalization. The influence of the existence of these symptom clusters and Palliative Performance Scale (PPS) on survival was analyzed by Cox proportional hazards regression analysis, and survival curves were compared between the groups with or without existing corresponding symptom clusters using the log-rank test. Results: The following symptom clusters were identified: cluster 1 (pain, insomnia, emotional functioning), cluster 2 (dyspnea, appetite loss, fatigue, and nausea), and cluster 3 (physical functioning). Cronbach's alpha values for the symptom clusters ranged from 0.72 to 0.82. An increased risk of death was significantly associated with the existence of cluster 2 and poor PPS (log-rank test, p = 0.016 and p < 0.001, respectively). Conclusion: In terminally ill patients with cancer, three symptom clusters were detected based on QLQ-C15-PAL scores. Poor PPS and the presence of symptom cluster that includes dyspnea, appetite loss, fatigue, and nausea indicated poor prognosis. [ABSTRACT FROM AUTHOR]

Purpose: To assess the accuracy of hospice staff in predicting survival of subjects admitted to hospice, exploring the factors considered most helpful by the hospice staff to accurately predict survival. Methods: Five physicians and 11 nurses were asked to predict survival at admission of 827 patients. Actual and predicted survival times were divided into ≤ 1 week, 2–3 weeks, 4–8 weeks, and ≥ 2 months and the accuracy of the estimates was calculated. The staff members were each asked to score 17 clinical variables that guided them in predicting survival and we analyzed how these variables impacted the accuracy. Results: Physicians' and nurses' accuracy of survival of the patients was 46% and 40% respectively. Survival was underestimated in 20% and 12% and overestimated in 34% and 48% of subjects. Both physicians and nurses considered metastases, comorbidities, dyspnea, disability, tumor site, neurological symptoms, and confusion very important in predicting patients' survival with nurses assigning more importance to intestinal symptoms and pain too. All these factors, with the addition of cough and/or bronchial secretions, were associated with physicians' greater accuracy. In the multivariable models, intestinal symptoms and confusion continued to be associated with greater predictive accuracy. No factors appreciably raised nurses' accuracy. Conclusions: Some clinical symptoms rated as relevant by the hospice staff could be important for predicting survival. However, only intestinal symptoms and confusion significantly improved the accuracy of physicians' predictions, despite the high prevalence of overestimated survival. [ABSTRACT FROM AUTHOR]

Purpose: The expected survival of patients with metastatic cancer can significantly impact decisions regarding treatment, care setting, and future planning. We evaluated the prognostication ability of a multidisciplinary team (MDT) experienced in providing supportive care and palliative radiotherapy. Methods: After clinical assessment of consecutive patients, survival predictions were independently made by each MDT member. Patient demographics, factors influencing predictions, and dates of death were collected. Clinical predictions of survival (CPS) were considered correct if within 30 days of actual survival (AS). Summary statistics and Kaplan-Meier estimates of overall survival were obtained. Correlations between actual and CPS were calculated using Spearman's correlation coefficient. Multivariate logistic regression analysis identified factors associated with prognostication accuracy. Results: A total of 395 predictions (06/2010-07/2012) were made by eight disciplines. Average age was 68 years, 68.3 % of patients were male, and 48.4 % had lung cancer. Median AS was 87 days (95 % CI 66-102 days). Survival was over-estimated 72.4 % (286/395) of the time with r = 0.54 ( p < 0.0001) for all predictions across all disciplines. In addition, 30.3 % (36/119) of radiation therapist (RTT) predictions were correct compared to 30.1 % (22/73) of nurses', 28.7 % (43/150) of physicians', and 15.1 % (8/53) of allied health (AH) providers. There were no differences in accuracy by discipline except for the RTT versus AH groups ( p = 0.04). Factors most frequently cited as influencing correct predictions were Karnofsky performance status (KPS), extent of disease, and histology. KPS was the only significant variable on multivariate analysis ( p ≤ 0.04). Conclusion: MDT members providing collaborative care for advanced cancer patients utilize similar factors in predicting survival with comparable accuracy. [ABSTRACT FROM AUTHOR]