Internal and emergency medicine
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Observational Study
Bioactive adrenomedullin a prognostic biomarker in patients with mild to moderate dyspnea at the emergency department: an observational study.
Acute dyspnea with underlying congestion is a leading cause of emergency department (ED) visits with high rates of hospitalization. Adrenomedullin is a vasoactive neuropeptide hormone secreted by the endothelium that mediates vasodilation and maintains vascular integrity. Plasma levels of biologically active adrenomedullin (bio-ADM) predict septic shock and vasopressor need in critically ill patients and are associated with congestion in patients with acute heart failure (HF) but the prognostic value in unselected dyspneic patients at the ED is unknown. ⋯ Bio-ADM (per interquartile range from median) predicts both 90-day mortality [odds ratio (OR): 1.5, 95% confidence interval (CI) 1.2-2.0, p < 0.002] and hospitalization (OR: 1.5, 95% CI 1.2-1.8, p < 0.001) independently of sex, age, NT-proBNP, creatinine, and CRP. Bio-ADM statistically significantly improves the reference model in predicting mortality (added χ2 9.8, p = 0.002) and hospitalization (added χ2 14.1, p = 0.0002), and is associated with IV diuretic treatment and HF diagnosis at discharge. Plasma levels of bio-ADM sampled at ED presentation in acutely dyspneic patients are independently associated with 90-day mortality, hospitalization and indicate the need for decongestive therapy.
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Observational Study
Characteristics and retention of emergency department patients who left without being seen (LWBS).
A retrospective observational study was conducted for patients 18 years or older presenting to a Midwestern emergency department (ED) in the United States during February 2019-January 2020 to characterize associated subsequent care utilization in patients who left the ED without being seen. Patients were classified as left without being seen (LWBS) based on documented ED disposition. The healthcare system's records were reviewed for any associated utilizations within 3 weeks following the initial ED encounter. ⋯ Patients without a subsequent health system associated encounter tended to be younger, female, non-White, and present with possible lower-acuity chief complaints. At least one-half of LWBS patients sought care related to the concerns by a health system provider within 3 weeks of the initial encounter within the same system. The high prevalence of ED returns within a narrow turnaround window highlights a missed opportunity to provide services to these patients during their initial encounter.
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Early prediction of the mortality, neurological outcome is clinically essential after successful cardiopulmonary resuscitation. To find a prognostic marker among unselected cardiac arrest survivors, we aimed to evaluate the alterations of the L-arginine pathway molecules in the early post-resuscitation care. We prospectively enrolled adult patients after successfully resuscitated in- or out-of-hospital cardiac arrest. ⋯ Based on receiver operator characteristic analysis (AUC = 0.723; p = 0.005) of initial ADMA for poor neurological outcome, the best cutoff was determined as > 0.65 µmol/L (sensitivity = 66.7%; specificity = 81.5%), while for 72 h mortality (AUC = 0.789; p = 0.001) as > 0.81 µmol/L (sensitivity = 71.0%; specificity = 87.5%). Based on multivariate analysis, initial ADMA (OR = 1.8 per 0.1 µmol/L increment; p = 0.002) was an independent predictor for 72 h mortality. Increased initial ADMA predicts 72 h mortality and poor neurological outcome among unselected cardiac arrest victims.
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Various scoring systems have been developed to predict the need for endoscopic treatment in patients with non-variceal upper gastrointestinal bleeding (NVUGIB). However, they have rarely been applied in clinical practice because the processes are complicated. The aim of this study was to establish a simple scoring system that predicts the need for endoscopic intervention in patients with NVUGIB. ⋯ GBS 0.615 [0.523-0.708], Hirosaki 0.719 [0.636-0.803]). The N score revealed a sensitivity of 84.5% and a specificity of 61.8%. Our N score, which is consisted of only four factors, would select patients who require endoscopic intervention with high probability.
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Machine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets. ⋯ The performance in the prediction of discharge within the next 7 days was more limited (area under the receiver operator curve 0.68 and 0.67). This study has shown that in prospective and external validation datasets the previously derived deep learning algorithms have demonstrated moderate performance in the prediction of which patients will be discharged within the next 2 days. Future studies may seek to further refine or evaluate the effect of the implementation of such algorithms.