Objective To determine if serum levels of endothelial adhesion molecules were associated with the development of multiple organ failure (MOF) and in-hospital mortality in adult patients with severe sepsis. Design This study was a secondary data analysis of a prospective cohort study. Setting Patients were admitted to two tertiary intensive care units in San Antonio, TX, between 2007 and 2012. Patients Patients with severe sepsis at the time of intensive care unit (ICU) admission were enrolled. Inclusion criteria were consistent with previously published criteria for severe sepsis or septic shock in adults. Exclusion criteria included immunosuppressive medications or conditions. Interventions None. Measurements Baseline serum levels of the following endothelial cell adhesion molecules were measured within the first 72 hours of ICU admission: Intracellular Adhesion Molecule 1 (ICAM-1), Vascular Cell Adhesion Molecule-1 (VCAM-1), and Vascular Endothelial Growth Factor (VEGF). The primary and secondary outcomes were development of MOF (≥2 organ dysfunction) and in-hospital mortality, respectively. Main results Forty-eight patients were enrolled in this study, of which 29 (60%) developed MOF. Patients that developed MOF had higher levels of VCAM-1 (p=0.01) and ICAM-1 (p=0.01), but not VEGF (p=0.70) compared with patients without MOF (single organ failure only). The area under the curve (AUC) to predict MOF according to VCAM-1, ICAM-1 and VEGF was 0.71, 0.73, and 0.54, respectively. Only increased VCAM-1 levels were associated with in-hospital mortality (p=0.03). These associations were maintained even after adjusting for APACHE and SOFA scores using logistic regression. Conclusions High levels of serum ICAM-1 was associated with the development of MOF. High levels of VCAM-1 was associated with both MOF and in-hospital mortality.
Frequent COPD exacerbations have a large impact on morbidity, mortality and health-care expenditures. By 2020, the World Health Organization expects COPD and COPD exacerbations to be the third leading cause of death world-wide. Furthermore, In 2005 it was estimated that COPD exacerbations cost the U.S. health-care system 38 billion dollars. Studies attempting to determine factors related to COPD readmissions are still very limited. Moreover, few have used a organized machine-learning, sensitivity analysis approach, such as a Random Forest (RF) statistical model, to analyze this problem. This study utilized the RF machine learning algorithm to determine factors that predict risk for multiple COPD exacerbations in a single year.This was a retrospective study with a data set of 106 patients. These patients were divided randomly into training (80%) and validating (20%) data-sets, 100 times, using approximately sixty variables intially, which in prior studies had been found to be associated with patient readmission for COPD exacerbation. In an interactive manner, an RF model was created using the training set and validated on the testing dataset. Mean area-under-curve (AUC) statistics, sensitivity, specificity, and negative/positive predictive values (NPV, PPV) were calculated for the 100 runs.The following variables were found to be important predictors of patients having at least two COPD exacerbations within one year: employment, body mass index, number of previous surgeries, administration of azithromycin/ceftriaxone/moxifloxacin, and admission albumin level. The mean AUC was 0.72, sensitivity of 0.75, specificity of 0.56, PPV of 0.7 and NPV of 0.63. Histograms were used to confirm consistent accuracy.The RF design has consistently demonstrated encouraging results. We expect to validate our results on new patient groups and improve accuracy by increasing our training dataset. We hope that identifying patients at risk for frequent readmissions will improve patient outcome and save valuable hospital resources.
INTRODUCTION Community acquired pneumonia (CAP) is associated with high rates of morbidity and mortality, especially among the elderly. Antibiotic treatment for CAP in the elderly is particularly challenging for many reasons, including compliance issues, immunosuppression, polypharmacy and antimicrobial resistance. There are few available antibiotics that are able to address these concerns. AREAS COVERED After a systematic review of the current literature, we describe seven novel antibiotics that are currently in advanced stages of development (phase 3 and beyond) and show promise for the treatment of CAP in those over the age of 65. These antibiotics are: Solithromycin, Pristinamycin, Nemonaxacin, Lefamulin, Omadacycline, Ceftobiprole and Delafloxacin. Using a novel conceptual framework designed by the present authors, known as the ‘San Antonio NIPS model’, we evaluate their strengths and weaknesses based on their ability to address the unique challenges that face the elderly. EXPERT OPINION All seven antibiotics have potential value for effective utilization in the elderly, but to varying degrees based on their NIPS model score. The goal of this model is to reorganize a clinician’s focus on antibiotic choices in the elderly and bring attention to a seldom discussed topic that may potentially become a health-care crisis in the next decade.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.