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

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

Independent determinants of prolonged emergency department length of stay in a tertiary care centre: a prospective cohort study

Abstract Background Emergency department (ED) overcrowding is a potential threat for patient safety. We searched for independent determinants of prolonged ED length of stay (LOS) with the aim to identify factors which can be targeted to reduce ED LOS, which may help in preventing overcrowding. Methods This prospective cohort study included consecutive ED patients in a Dutch tertiary care centre. Multivariable logistic regression analysis was used to identify independent determinants of ED LOS > 4 h, including patient characteristics (demographics, referral type, acuity, (number of) presenting complaints and comorbidity), treating specialty, diagnostic testing, consultations, number of patients in the ED and disposition. Furthermore, we quantified the absolute time delays (measured in real-time) associated with the most important independent determinants of prolonged ED LOS. Results In 1434 included patients independent determinants of prolonged ED LOS were number and type of presenting complaints, specialty, laboratory/radiology testing and consultations, and ICU admission. Modifiable determinants with the largest impact were blood testing; Adjusted odds ratio (AOR (95%-CI)); 3.45 (1.95–6.11), urine testing; 1.79 (1.21–2.63), radiology imaging; 3.02 (2.13–4.30), and consultation; 5.90 (4.08–8.54). Combined with the laboratory/radiology testing and/or consultations (requested in 1123 (78%) patients) the decision-making and discharge process consumed between 74 (42%) and 117 (66%) minutes of the total ED LOS of 177 (IQR: 129–225) minutes. Conclusions In tertiary care EDs, ED LOS can be reduced if the process of laboratory/radiology testing and consulting is optimized and the decision-making and discharge procedures are accelerated

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

textabstractAcutely 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