Clinical signs of impending death in cancer patients.

BACKGROUND: The physical signs of impending death have not been well characterized in cancer patients. A better understanding of these signs may improve the ability of clinicians to diagnose impending death. We examined the frequency and onset of 10 bedside physical signs and their diagnostic performance for impending death. METHODS: We systematically documented 10 physical signs every 12 hours from admission to death or discharge in 357 consecutive patients with advanced cancer admitted to two acute palliative care units. We examined the frequency and median onset of each sign from death backward and calculated their likelihood ratios (LRs) associated with death within 3 days. RESULTS: In total, 203 of 357 patients (52 of 151 in the U.S., 151 of 206 in Brazil) died. Decreased level of consciousness, Palliative Performance Scale ≤20%, and dysphagia of liquids appeared at high frequency and >3 days before death and had low specificity (<90%) and positive LR (<5) for impending death. In contrast, apnea periods, Cheyne-Stokes breathing, death rattle, peripheral cyanosis, pulselessness of radial artery, respiration with mandibular movement, and decreased urine output occurred mostly in the last 3 days of life and at lower frequency. Five of these signs had high specificity (>95%) and positive LRs for death within 3 days, including pulselessness of radial artery (positive LR: 15.6; 95% confidence interval [CI]: 13.7-17.4), respiration with mandibular movement (positive LR: 10; 95% CI: 9.1-10.9), decreased urine output (positive LR: 15.2; 95% CI: 13.4-17.1), Cheyne-Stokes breathing (positive LR: 12.4; 95% CI: 10.8-13.9), and death rattle (positive LR: 9; 95% CI: 8.1-9.8). CONCLUSION: We identified highly specific physical signs associated with death within 3 days among cancer patients.

The accuracy of probabilistic versus temporal clinician prediction of survival for patients with advanced cancer: a preliminary report.

Clinicians have limited accuracy in the prediction of patient survival. We assessed the accuracy of probabilistic clinician prediction of survival (CPS) and temporal CPS for advanced cancer patients admitted to our acute palliative care unit, and identified factors associated with CPS accuracy. Eight physicians and 20 nurses provided their estimation of survival on admission by (a) the temporal approach, "What is the approximate survival for this patient (in days)?" and (b) the probabilistic approach, "What is the approximate probability that this patient will be alive (0%-100%)?" for ≥24 hours, 48 hours, 1 week, 2 weeks, 1 month, 3 months, and 6 months. We also collected patient and clinician demographics. Among 151 patients, the median age was 58 years, 95 (63%) were female, and 138 (81%) had solid tumors. The median overall survival time was 12 days. The median temporal CPS was 14 days for physicians and 20 days for nurses. Physicians were more accurate than nurses. A higher accuracy of temporal physician CPS was associated with older patient age. Probabilistic CPS was significantly more accurate than temporal CPS for both physicians and nurses, although this analysis was limited by the different criteria for determining accuracy. With the probabilistic approach, nurses were significantly more accurate at predicting survival at 24 hours and 48 hours, whereas physicians were significantly more accurate at predicting survival at 6 months. The probabilistic approach was associated with high accuracy and has practical implications.

Longitudinal temporal and probabilistic prediction of survival in a cohort of patients with advanced cancer.

CONTEXT: Survival prognostication is important during the end of life. The accuracy of clinician prediction of survival (CPS) over time has not been well characterized. OBJECTIVES: The aims of the study were to examine changes in prognostication accuracy during the last 14 days of life in a cohort of patients with advanced cancer admitted to two acute palliative care units and to compare the accuracy between the temporal and probabilistic approaches. METHODS: Physicians and nurses prognosticated survival daily for cancer patients in two hospitals until death/discharge using two prognostic approaches: temporal and probabilistic. We assessed accuracy for each method daily during the last 14 days of life comparing accuracy at Day -14 (baseline) with accuracy at each time point using a test of proportions. RESULTS: A total of 6718 temporal and 6621 probabilistic estimations were provided by physicians and nurses for 311 patients, respectively. Median (interquartile range) survival was 8 days (4-20 days). Temporal CPS had low accuracy (10%-40%) and did not change over time. In contrast, probabilistic CPS was significantly more accurate (P < .05 at each time point) but decreased close to death. CONCLUSION: Probabilistic CPS was consistently more accurate than temporal CPS over the last 14 days of life; however, its accuracy decreased as patients approached death. Our findings suggest that better tools to predict impending death are necessary.

Development and cross-validation of the in-hospital mortality prediction in advanced cancer patients score: a preliminary study.

PURPOSE: Acute palliative care units (APCUs) provide intensive symptom support and transition of care for advanced cancer patients. Better understanding of the predictors of in-hospital mortality is needed to facilitate program planning and patient care. In this prospective study, we identified predictors of APCU mortality, and developed a four-item In-hospital Mortality Prediction in Advanced Cancer Patients (IMPACT) predictive model. METHODS: Between April and July 2010, we documented baseline demographics, the Edmonton Symptom Assessment Scale (ESAS), 80 clinical signs including known prognostic factors, and 26 acute complications on admission in consecutive APCU patients. Multivariate logistic regression analysis was used to identify factors for inclusion in a nomogram, which was cross-validated with bootstrap analysis. RESULTS: Among 151 consecutive patients, the median age was 58, 13 (9%) had hematologic malignancies, and 52 (34%) died in the hospital. In multivariate analysis, factors associated with in-hospital mortality were advanced education (odds ration [OR]=11.8, p=0.002), hematologic malignancies (OR=8.6, p=0.02), delirium (OR=4.3, p=0.02), and high ESAS global distress score (OR=20.8, p=0.01). In a nomogram based on these four factors, total scores of 6, 10, 14, 17, and 21 corresponded to a risk of death of 10%, 25%, 50%, 75%, and 90%, respectively. The model has 92% sensitivity and 88% specificity for predicting patients at low/high risk of dying in the hospital, and a receiver-operator characteristic curve concordance index of 83%. CONCLUSIONS: Higher education was associated with increased utilization of the interdisciplinary palliative care unit until at the end of life. Patients with higher symptom burden, delirium, and hematologic malignancies were also more likely to require APCU care until death.