Development of the ADO-SQ model to predict 1-year mortality in patients with COPD

Goals of end-of-life care must be adapted to the needs of patients with chronic pulmonary disease (COPD) who are in the last phase of life. However, identification of those patients is limited by 1) moderate performances of existing prognostic models, and 2) limited validation of the often-recommended surprise question (SQ). We prospectively included COPD patients from five hospitals in the Netherlands to develop a clinical prediction model to predict 1-year mortality in patients with COPD. The ADO-SQ model, which consists of the ADO model (Age, Dyspnea, airflow Obstruction) and SQ, offers improved discriminative performance for predicting 1-year mortality compared to the SQ and ADO. A nomogram and web application were developed

The development of a clinical prediction model for 1-year mortality in patients with advanced cancer

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 and various prediction models using clinical variables, are not well validated or lack discriminative ability. We prospectively included cancer patients from six hospitals in the Netherlands to develop a clinical prediction model to predict 1-year mortality in patients with advanced cancer. In a relatively large cohort of 867 patients, we showed that an extended prediction model that combines the surprise question, clinical characteristics (age, cancer type, visceral metastases, brain metastases, performance status, weight loss, pain, and dyspnea), and laboratory values (hemoglobin, C-reactive protein, and serum albumin) has better discrimination (c-statistic 0.78) in predicting 1-year mortality than the surprise question (c-statistic 0·69), clinical characteristics (c-statistic 0·70), or laboratory values (c-statistic 0·71) alone. Additionally, our prediction model also showed better discrimination than other models in literature. We developed a nomogram and web-based calculator to calculate the 1-year mortality risk for individual patients with advanced cancer

Characteristics and Outcome of Pediatric Non-Hodgkin Lymphoma Patients With Ovarian Infiltration at Presentation

BackgroundOvarian infiltration in pediatric non-Hodgkin lymphoma (NHL) at presentation is rare and information on outcome is scarce and mainly based on case reports and small series. ProcedureEvaluation of clinical characteristics and outcome of ovarian infiltrated pediatric NHL cases of a single center, and an extensive review of the all cases reported so far in literature. ResultsAt presentation, 6/60 female NHL cases of our center had ovarian infiltration, and combining these cases with earlier case reports, a total of 42 cases were identified. Median age at presentation was 10.9 years (range 0-18), and all but one had a B-cell immunophenotype, with 32/42 cases being classified as Burkitt. Bilateral involvement was reported in 26/41 cases, of which 22 were bilaterally ovariectomized as first treatment. All cases were treated with chemotherapy. Relapses were repor ConclusionsWe conclude that in case of ovarian tumors with negative markers, NHL should be considered in order to avoid unnecessary surgery. Pediatr Blood Cancer 2013;60:2054-2059. (c) 2013 Wiley Periodicals, Inc

The development of a clinical prediction model for 1-year mortality in patients with advanced cancer

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 and various prediction models using clinical variables, are not well validated or lack discriminative ability. We prospectively included cancer patients from six hospitals in the Netherlands to develop a clinical prediction model to predict 1-year mortality in patients with advanced cancer. In a relatively large cohort of 867 patients, we showed that an extended prediction model that combines the surprise question, clinical characteristics (age, cancer type, visceral metastases, brain metastases, performance status, weight loss, pain, and dyspnea), and laboratory values (hemoglobin, C-reactive protein, and serum albumin) has better discrimination (c-statistic 0.78) in predicting 1-year mortality than the surprise question (c-statistic 0·69), clinical characteristics (c-statistic 0·70), or laboratory values (c-statistic 0·71) alone. Additionally, our prediction model also showed better discrimination than other models in literature. We developed a nomogram and web-based calculator to calculate the 1-year mortality risk for individual patients with advanced cancer

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

The prognostic value of the 12-, 6-, 3-and 1-month 'Surprise Question' in cancer patients: A prospective cohort study in three hospitals

Objective This prospective study aimed to evaluate the performance of the 'Surprise Question' (SQ) 'Would I be surprised if this patient died in the next 12 months?' in predicting survival of 12, 6, 3 and 1 month(s), respectively, in hospitalised patients with cancer. Methods In three hospitals, physicians were asked to answer SQs for 12/6/3/1 month(s) for inpatients with cancer. Sensitivity, specificity, positive and negative predictive values were calculated. Results A total of 783 patients were included, of whom 51% died in the 12-month period after inclusion. Sensitivity of the SQ predicting death within 12 months was 0.79, specificity was 0.66, the positive predictive value was 0.71 and the negative predictive value was 0.75. When the SQ concerned a shorter survival period, sensitivities and positive predictive values decreased, whereas specificities and negative predictive values increased. In multivariable logistic regression analysis, the SQ was significantly associated with mortality (OR 3.93, 95% CI 2.70-5.71, p < 0.01). Conclusions The 12-month SQ predicts death in patients with cancer admitted to the hospital reasonably well. Shortening the timeframe decreases sensitivities and increases specificities. The four surprise questions may help to identify patients for whom palliative care is indicated.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

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