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132 Background: Advance care planning is necessary for cancer patients who are in their last year of life. We developed a clinical model to predict 1-year mortality for patients with advanced cancer. Methods: Patients with advanced cancer and no curatively aimed treatment options were included in a prospective multicenter observational study involving six hospitals in the Netherlands (June-November 2017). Using cox regression, a model was developed with candidate predictors as identified in literature: surprise question (SQ), clinical characteristics (age, sex, primary tumor, metastases, WHO performance status, food intake, weight loss, pain, comorbidity, dyspnea, and fatigue), and laboratory values (serum albumin, hemoglobin, and C-reactive protein). The primary outcome was all-cause 1-year mortality. Discriminative ability was measured using the c-statistic and assessed using internal-external validation by study hospital. Results: Of 867 patients (median age 66 years, male 47%), 362 (42%) died within 1-year follow-up. Three models were developed: model 1: SQ; model 2: SQ + clinical characteristics; model 3: SQ + clinical characteristics + laboratory values. Predictors included in the most expansive model 3 were: SQ ‘no’ (ref: ‘yes’; HR 3.43, 95% CI 2.57-4.58), age per 10 years (HR 1.07, 95% CI 0.97-1.18), primary tumor-site (ref: prostate, breast, or thyroid; HR 1.35, 95% CI 1.01-1.82), visceral metastases (HR 1.32, 95% CI 1.05-1.65), brain metastases (HR 1.54, 95% CI 1.07-2.22), WHO performance status 1 (ref: 0; HR 1.03, 95% CI 0.76-1.41), WHO performance status 2+ (ref: 0; HR 1.50, 95% CI 1.04-2.15), any weight loss (ref: none; HR 1.09, 95% CI 0.86-1.38), pain score (HR 1.03, 95% CI 0.98-1.09); dyspnea grade 1 (ref: 0; HR 1.20, 95% CI 0.94-1.54), dyspnea grade 2+ (ref: 0; HR 1.33, 95% CI 0.93-1.91), C-reactive protein (HR 1.15, 95% CI 1.04-1.27), and serum albumin (HR 0.97, 95% CI 0.95-1.00). The pooled c-statistic at internal-external validation was 0.69 for model 1, 0.76 for model 2, and 0.78 for model 3. Conclusions: A model that combines the SQ with easily available clinical characteristics (with or without laboratory values) is more accurate in predicting 1-year mortality in patients with advanced cancer than the SQ alone.

17 Background: Awareness is growing that palliative care is an important part in the hospital. It is aimed to improve the quality of life of patients and their relatives in the end-stage of a disease by a multidimensional approach. Prior to the development and implementation of a digital Care Pathway for Palliative Care, we wanted to map the palliative care process during the last hospitalization prior to death of patients with malignancies. Methods: We studied the medical files of patients who died in the period 1 July 2011 – 1 July 2012 within six months after having been admitted to the Department of Oncology and Lung diseases in a general hospital. A checklist was used to collect data about the patients’ diagnosis; the care that was provided during their last stay in the hospital; and about characteristics of the dying process. Results: In total 149 medical records were examined. Most of the patients (78%) were admitted because of complications or progression of the disease. During their last hospitalization, 90% of the patients underwent radiologic tests and 97% underwent blood tests. Furthermore, 38% of the patients received chemotherapy. Of all patients, 55% died during their last stay in the hospital. Medical files contained limited information about the care process: 18% contained information about provision of information to the general practitioner (GP) during admission, 78% contained reports of bad-news conversations. In 36%, the patient’s preference concerning their place of death was unknown. Further, there was no optimal communication with the GP after the patient had died in the hospital; 87% of the GP’s were informed. Conclusions: Palliative care in the hospital was found to involve suboptimal communication on several important topics. A structured medical file to guide palliative care may contribute to improvement of the care process. We are now evaluating the effects of a digital Care Pathway for Palliative Care, that includes ‘marking’ of the palliative phase of a disease and supports adequate communication, advanced care planning, multidisciplinary cooperation and continuity of care.