Physicians' views on the usefulness and feasibility of identifying and disclosing patients' last phase of life: A focus group study

Objectives: Accurate assessment that a patient is in the last phase of life is a prerequisite for timely initiation of palliative care in patients with a life-limiting disease, such as advanced cancer or advanced organ failure. Several palliative care quality standards recommend the surprise question (SQ) to identify those patients. Little is known about physicians' views on identifying and disclosing the last phase of life of patients with different illness trajectories. Methods: Data from two focus groups were analysed using thematic analysis with a phenomenological approach. Results: Fifteen medical specialists and general practitioners participated. Participants thought prediction of patients' last phase of life, i.e. expected death within 1 year, is important. They seemed to find that prediction is more difficult in patients with advanced organ failure compared with cancer. The SQ was considered a useful prognostic tool; its use is facilitated by its simplicity but hampered by its subjective character. The medical specialist was considered mainly responsible for prognosticating and gradually disclosing the last phase. Participa

The Value of the Surprise Question to Predict One-Year Mortality in Idiopathic Pulmonary Fibrosis

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive fatal disease with a heterogeneous disease course. Timely initiation of palliative care is often lacking. The surprise question "Would you be surprised if this patient died within the next year?"is increasingly used as a clinical prognostic tool in chronic diseases but has never been evaluated in IPF. Objective: We aimed to evaluate the predictive value of the surprise question for 1-year morta

Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer

Importance: To optimize palliative care in patients with cancer who are in their last year of life, timely and accurate prognostication is needed. However, available instruments for prognostication, such as the surprise question ("Would I be surprised if this patient died in the next year?") and various prediction models using clinical variables, are not well validated or lack discriminative ability. Objective: To develop and validate a prediction model to calculate the 1-year risk of death among patients with advanced cancer. Design, Setting, and Participants: This multicenter prospective prognostic study was performed in the general oncology inpatient and outpatient clinics of 6 hospitals in the Netherlands. A total of 867 patients were enrolled between June 2 and November 22, 2017, and followed up for 1 year. The primary analyses were performed from October 9 to 25, 2019, with the most recent analyses performed from June 19 to 22, 2022. Cox proportional hazards regression analysis was used to develop a prediction model including 3 categories of candidate predictors: clinician responses to the surprise question, patient clinical characteristics, and patient laboratory values. Data on race and ethnicity were not collected because most patients were expected to be of White race and Dutch ethnicity, and race and ethnicity were not considered as prognostic factors. The models' discriminative ability was assessed using internal-external validation by study hospital and measured using the C statistic. Patients 18 years and older with locally advanced or metastatic cancer were eligible. Patients with hematologic cancer were excluded. Main Outcomes and Measures: The risk of death by 1 year. Results: Among 867 patients, the median age was 66 years (IQR, 56-72 years), and 411 individuals (47.4%) were male. The 1-year mortality rate was 41.6% (361 patients). Three prediction models with increasing complexity were developed: (1) a simple model including the surprise question, (2) a clinical model including the surprise question and clinical characteristics (age, cancer type prognosis, visceral metastases, brain metastases, Eastern Cooperative Oncology Group performance status, weight loss, pain, and dyspnea), and (3) an extended model including the surprise question, clinical characteristics, and laboratory values (hemoglobin, C-reactive protein, and serum albumin). The pooled C statistic was 0.69 (95% CI, 0.67-0.71) for the simple model, 0.76 (95% CI, 0.73-0.78) for the clinical model, and 0.78 (95% CI, 0.76-0.80) for the extended model. A nomogram and web-based calculator were developed to support clinicians in adequately caring for patients with advanced cancer. Conclusions and Relevance: In this study, a prediction model including the surprise question, clinical characteristics, and laboratory values had better discriminative ability in predicting death among patients with advanced cancer than models including the surprise question, clinical characteristics, or laboratory values alone. The nomogram and web-based calculator developed for this study can be used by clinicians to identify patients who may benefit from palliative care and advance care planning. Further exploration of the feasibility and external validity of the model is needed