Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department.

Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Aim of the study: Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting. Material and methods: We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed. Results: The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model. Conclusions: Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.

Admission and Discharge Following Ambulance Transport to the Emergency Department.

Emergency ambulance use is deemed necessary for the transport of acutely ill patients to hospital emergency departments (ED). However, some patients are discharged as they present low acuity or chronic problems and should receive primary healthcare services, while the most severely ill are admitted. In the present study, we examined the descriptive epidemiology of ambulance transports for emergencies in the ED by utilizing the data of the information systems of a public tertiary general hospital in Greece. More than half of the patients transferred to the ED by an ambulance were finally admitted to the hospital (52.25%), whereas only one-third (33.74%) of those transferred by other means. A statistically significant association was detected between ambulance use and hospital admission. Age was also statistically significantly higher in the ambulance group. Higher mean values of creatinine, CRP, LDH, urea, white-blood-cell count, and neutrophils were detected in the ambulance group, in contrast to hemoglobin and lymphocyte count which were higher in the non-ambulance group.

Profile of endocrinological derangements affecting PSA values in patients with COPD.

BACKGROUND/AIM: Chronic obstractive pulmonary disease (COPD) in men has been associated with testosterone deficiency, known as the late-onset hypogonadism. Prostate cancer becomes more prevalent when testosterone values decline in males. We sought to determine endocrinological derangements that may affect PSA values in male patients with COPD. MATERIALS AND METHODS: A total of 69 male patients with COPD and 82 healthy volunteers were divided into subgroups according to: their age: (i) ≤60 years and (ii) >60 years; or disease severity: (i) FEV1<50% and (ii) FEV1≥50% predicted. RESULTS: There was a significant reduction in total and free testosterone in patients with COPD. Patients with COPD aged >60 years had significantly lower free PSA compared to the control group. CONCLUSION: Alterations of the male hormonal status in COPD are related with older age (>60 years) and poorer lung function (FEV1<50% predicted). This may have implications for the use of the PSA-based screening tests in the elderly male population with COPD.