Prediction of 90-day mortality in older patients after discharge from an emergency department: a retrospective follow-up study

Background: Older people frequently attend the emergency department (ED) and have a high risk of poor outcome as compared to their younger counterparts. Our aim was to study routinely collected clinical parameters as predictors of 90-day mortality in older patients attending our ED. Methods: We conducted a retrospective follow-up study at the Leiden University Medical Center (The Netherlands) among patients aged 70 years or older attending the ED in 2012. Predictors were age, gender, time and way of arrival, presenting complaint, consulting medical specialty, vital signs, pain score and laboratory testing. Cox regression analyses were performed to analyse the association between these predictors and 90-day mortality. Results: Three thousand two hundred one unique patients were eligible for inclusion. Ninety-day mortality was 10.5 % for the total group. Independent predictors of mortality were age (hazard ratio [HR] 1.06, 95 % confidence interval [95 % CI] 1.04-1.08), referral from another hospital (HR 2.74, 95 % CI 1.22-6.11), allocation to a non-surgical specialty (HR: 1.55, 95 % CI 1.13-2.14), increased respiration rate (HR up to 2.21, 95 % CI 1.25-3.92), low oxygen saturation (HR up to 1.96, 95 % CI 1.19-3.23), hypothermia (HR 2.27, 95 % CI 1.28-4.01), fever (HR 0.43, 95 % CI 0.24-0.75), high pain score (HR 1.55, 95 % CI 1.03-2.32) and the indication to perform laboratory testing (HR 3.44, 95 % CI 2.13-5.56). Conclusions: Routinely collected parameters at the ED can predict 90-day mortality in older patients presenting to the ED. This study forms the first step towards creating a new and simple screening tool to predict and improve health outcome in acutely presenting older patients

Predicting mortality in acutely hospitalized older patients: a retrospective cohort study

Acutely hospitalized older patients have an increased risk of mortality, but at the moment of presen- tation this risk is difficult to assess. Early identification of patients at high risk might increase the awareness of the physician, and enable tailored decision-making. Existing screening instruments mainly use either geriatric factors or severity of disease for prognostication. Predictive perfor- mance of these instruments is moderate, which hampers successive interventions. We conducted a retrospective cohort study among all patients aged 70 years and over who were acutely hospitalized in the Acute Medical Unit of the Leiden University Medical Center, the Netherlands in 2012. We developed a prediction model for 90-day mor- tality that combines vital signs and laboratory test results reflecting severity of disease with geriatric factors, repre- sented by comorbidities and number of medications. Among 517 patients, 94 patients (18.2 %) died within 90 days after admission. Six predictors of mortality were included in a model for mortality: oxygen saturation, Charlson comorbidity index, thrombocytes, urea, C-reac- tive protein and non-fasting glucose. The prediction model performs satisfactorily with an 0.738 (0.667–0.798). Using this model, 53 % of the patients in the highest risk decile ( N = 51) were deceased within 90 days. In conclusion, we are able to predict 90-day mortality in acutely hospitalized older patients using a model with directly available clinical data describing disease severity and geriatric factors. After further validation, such a model might be used in clinical decision making in older patients

Palliative care needs of advanced cancer patients in the emergency department at the end of life: an observational cohort study

Purpose Patients with advanced cancer commonly visit the emergency department (ED) during the last 3 months of life. Identification of these patients and their palliative care needs help initiating appropriate care according to patients' wishes. Our objective was to provide insight into ED visits of advanced cancer patients at the end of life. Methods Adult palliative patients with solid tumours who died = 1 per day. ED visits were initiated by patients and family in 34.0% and 51.9% occurred during out-of-office hours. Dyspnoea (21.0%) or pain (18.6%) were most reported symptoms. Before the ED visit, limitations on life-sustaining treatments were discussed in 33.8%, during or after the ED visit in 70.7%. Median stay at the ED was 3:29 h (range 00:12-18:01 h), and 319 (76.0%) were hospitalized. Median survival was 18 days (IQ range 7-41). One hundred four (24.8%) died within 7 days after the ED visit, of which 71.2% in-hospital. Factors associated with approaching death were lung cancer, neurologic deterioration, dyspnoea, hypercalcemia, and jaundice. Conclusion ED visits of advanced cancer patients often lead to hospitalization and in-hospital deaths. Timely recognition of patients with limited life expectancies and urgent palliative care needs, and awareness among ED staff of the potential of ED-initiated palliative care may improve the end-of-life trajectory of these patients.Development and application of statistical models for medical scientific researc

Predicting mortality in acutely hospitalized older patients: a retrospective cohort study

Acutely hospitalized older patients have an increased risk of mortality, but at the moment of presentation this risk is difficult to assess. Early identification of patients at high risk might increase the awareness of the physician, and enable tailored decision-making. Existing screening instruments mainly use either geriatric factors or severity of disease for prognostication. Predictive performance of these instruments is moderate, which hampers successive interventions. We conducted a retrospective cohort study among all patients aged 70 years and over who were acutely hospitalized in the Acute Medical Unit of the Leiden University Medical Center, the Netherlands in 2012. We developed a prediction model for 90-day mortality that combines vital signs and laboratory test results reflecting severity of disease with geriatric factors, represented by comorbidities and number of medications. Among 517 patients, 94 patients (18.2 %) died within 90 days after admission. Six predictors of mortality were included in a model for mortality: oxygen saturation, Charlson comorbidity index, thrombocytes, urea, C-reactive protein and non-fasting glucose. The prediction model performs satisfactorily with an 0.738 (0.667–0.798). Using this model, 53 % of the patients in the highest risk decile (N = 51) were deceased within 90 days. In conclusion, we are able to predict 90-day mortality in acutely hospitalized older patients using a model with directly available clinical data describing disease severity and geriatric factors. After further validation, such a model might be used in clinical decision making in older patients

Predictors and Outcomes of Revisits in Older Adults Discharged from the Emergency Department

Pathophysiology, epidemiology and therapy of agein

Current practice patterns of outpatient management of acute pulmonary embolism: A post-hoc analysis of the YEARS study

Background: Studies have shown the safety of home treatment of patients with pulmonary embolism (PE) at low risk of adverse events. Management studi

Early prediction of hospital admission for emergency department patients: a comparison between patients younger or older than 70 years

Pathophysiology, epidemiology and therapy of agein

Plasma levels of leptin and mammographic density among postmenopausal women: a cross-sectional study

INTRODUCTION: Obesity has been linked to increased risk of breast cancer in postmenopausal women. Increased peripheral production of estrogens has been regarded as the main cause for this association, but other features of increased body fat mass may also play a part. Leptin is a protein produced mainly by adipose tissue and may represent a growth factor in cancer. We examined the association between leptin plasma levels and mammographic density, a biomarker for breast cancer risk. METHODS: We included data from postmenopausal women aged 55 and older, who participated in a cross-sectional mammography study in Tromsø, Norway. Mammograms, plasma leptin measurements as well as information on anthropometric and hormonal/reproductive factors were available from 967 women. We assessed mammographic density using a previously validated computer-assisted method. Multiple linear regression analysis was applied to investigate the association between mammographic density and quartiles of plasma leptin concentration. Because we hypothesized that the effect of leptin on mammographic density could vary depending on the amount of nondense or fat tissue in the breast, we also performed analyses on plasma leptin levels and mammographic density within tertiles of mammographic nondense area. RESULTS: After adjusting for age, postmenopausal hormone use, number of full-term pregnancies and age of first birth, there was an inverse association between leptin and absolute mammographic density (P(trend )= 0.001). When we additionally adjusted for body mass index and mammographic nondense area, no statistically significant association between plasma leptin and mammographic density was found (P(trend )= 0.16). Stratified analyses suggested that the association between plasma leptin and mammographic density could differ with the amount of nondense area of the mammogram, with the strongest association between leptin and mammographic absolute density in the stratum with the medium breast fat content (P(trend )= 0.003, P for interaction = 0.05). CONCLUSION: We found no overall consistent association between the plasma concentration of leptin and absolute mammographic density. Although weak, there was some suggestion that the association between leptin and mammographic density could differ with the amount of fat tissue in the breast