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

PURPOSE Accurate risk assessment is essential for the success of population screening programs in breast cancer. Models with high sensitivity and specificity would enable programs to target more elaborate screening efforts to high-risk populations, while minimizing overtreatment for the rest. Artificial intelligence (AI)-based risk models have demonstrated a significant advance over risk models used today in clinical practice. However, the responsible deployment of novel AI requires careful validation across diverse populations. To this end, we validate our AI-based model, Mirai, across globally diverse screening populations. METHODS We collected screening mammograms and pathology-confirmed breast cancer outcomes from Massachusetts General Hospital, USA; Novant, USA; Emory, USA; Maccabi-Assuta, Israel; Karolinska, Sweden; Chang Gung Memorial Hospital, Taiwan; and Barretos, Brazil. We evaluated Uno's concordance-index for Mirai in predicting risk of breast cancer at one to five years from the mammogram. RESULTS A total of 128,793 mammograms from 62,185 patients were collected across the seven sites, of which 3,815 were followed by a cancer diagnosis within 5 years. Mirai obtained concordance indices of 0.75 (95% CI, 0.72 to 0.78), 0.75 (95% CI, 0.70 to 0.80), 0.77 (95% CI, 0.75 to 0.79), 0.77 (95% CI, 0.73 to 0.81), 0.81 (95% CI, 0.79 to 0.82), 0.79 (95% CI, 0.76 to 0.83), and 0.84 (95% CI, 0.81 to 0.88) at Massachusetts General Hospital, Novant, Emory, Maccabi-Assuta, Karolinska, Chang Gung Memorial Hospital, and Barretos, respectively. CONCLUSION Mirai, a mammography-based risk model, maintained its accuracy across globally diverse test sets from seven hospitals across five countries. This is the broadest validation to date of an AI-based breast cancer model and suggests that the technology can offer broad and equitable improvements in care.

Long-term survival, health-related quality of life, and quality-adjusted life year expectancy of cancer patients admitted to the ICU are limited. Nevertheless, these clinical outcomes exhibit wide variability among patients and are associated with simple characteristics present at the time of ICU admission, which may help healthcare professionals estimate patients' prognoses.

BackgroundThe incidence of melanoma, one of the most aggressive of the skin cancers, has been increasing worldwide in the last few decades. Data from Latin America and Brazil remain scarce. We aimed to describe the demographic, clinical, and histopathological data; therapy characteristics; and survival rates of the Brazilian melanoma patient population.ResultsWe collected and analysed retrospective data from 15 years at a tertiary cancer centre. We describe patient characteristics and treatment. We calculated survival, and identified the main prognostic factors through univariate and multivariate analysis. We analysed a total of 1073 patients, with a mean age of 56.7 years. Men and women experienced similar prevalence, and 91.2% of patients had white skin. The most prevalent subtype was superficial spreading, and the most prevalent anatomic location was the trunk (32.2%), followed by the lower extremities (28%). Of all cases, 567 (52.9%) were assigned to clinical stages I and II, while 382 (32.6%) were stages III and IV. Surgery was the main treatment. Sentinel node biopsy was performed in 373 patients, with 23.8% positivity. Overall actuarial 5-year survival was 67.6%. Multivariate analysis showed that gender, serum lactate dehydrogenase (LDH) levels at diagnosis; anatomic location, TNM stage, and local recurrence were significant prognostic factors.ConclusionsOverall survival was lower than worldwide rates. The main factors influencing survival were similar to those in other populations. Local recurrence was independently associated with lower survival rates. The high prevalence of advanced cases reinforces the importance of strategies to diagnose melanomas in the early stages. There is a need for future multi-institutional prospective studies to attain a better understanding of possible socioeconomic and other influences on survival among melanoma populations in Brazil and Latin America.

Context Survival prognostication is important during end-of-life. The accuracy of clinician prediction of survival (CPS) over time has not been well characterized. Objectives 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 6718 temporal and 6621 probabilistic estimations were provided by physicians and nurses for 311 patients, respectively. Median (interquartile range) survival was 8 (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.