Background: Erysipelas and cellulitis are relatively common cutaneous infections that can sometimes be the cause of a prolonged hospital admission. The objective of this study was to determine the most relevant epidemiologic factors and their influence on the length of hospital stay, comparing our results with those of previous studies.
Material And Methods: We performed a retrospective, observational, cross-sectional study of 122 patients admitted over a 5-year period to the dermatology department of our hospital with a diagnosis of erysipelas or cellulitis.
Results: Patients with a diagnosis of erysipelas or cellulitis represented 8.6% of all admissions during the study period. The mean age was 58.93 years and the female to male ratio was 1.06:1. The most common site of involvement was on the legs (76.22%). Overweight or obesity was present in 42.6% of patients and tinea pedis was detected in 33.6% of cases. A skin abscess developed in 7.4% of cases. The mean length of admission was 10.20 days; length of stay increased with age and with the erythrocyte sedimentation rate (ESR) on admission (P < .01 for both differences).
Conclusions: We confirm general epidemiologic factors such as sex and age distributions, predominant site, past history, and length of hospital stay. In view of their predictive value for the length of hospital stay, we propose that age and the ESR on admission should be considered to be indirect indicators of disease severity.
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J Clin Oncol
January 2025
German Breast Group, Neu-Isenburg, Germany.
Purpose: To assess trial-level surrogacy value for overall survival (OS) of the pathologic complete response (pCR) and invasive disease-free survival (iDFS) in randomized clinical trials (RCTs) for early breast cancer (BC).
Methods: Individual patient data of neoadjuvant RCTs with available data on pCR, iDFS, and OS were included in the analysis. We used the coefficient of determination from weighted linear regression models to quantify the association between treatment effects on OS and on the surrogate end points.
J Int Med Res
January 2025
Department of Gastroenterology and Hepatology, Henry Ford Hospital, Detroit, MI, United States.
Objectives: Central nervous system complications of acute pancreatitis (AP) can result in cerebral edema (CE). We assessed the risk of serious outcomes and health care features associated with CE in patients hospitalized with AP.
Methods: We conducted a retrospective cohort study using the National Inpatient Sample database.
PLoS One
January 2025
Department of Anesthesiology, Henan Provincial Chest Hospital & Chest Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Background: Postoperative nausea and vomiting (PONV) is a common complication of general anesthesia. This affects 30-80% of patients, and leads to discomfort and extended hospital stays. The effectiveness of penehyclidine for preventing PONV remains a subject of debate in the literature.
View Article and Find Full Text PDFPLoS One
January 2025
Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Munich University Hospital (LMU), Munich, Germany.
Introduction: Despite its importance in voice training, comprehensive research into sustained vowel phonation with constant pitch and increasing and decreasing loudness, the so-called Messa di Voce, is lacking. The study examines the laryngeal behavior during Messa di Voce, regarding the impact of the speed of execution on voice stability parameters.
Materials And Methods: Nine untrained, healthy subjects (5 female, 4 male) were asked to perform Messa di Voce exercises on the vowel [i:], involving a gradual increase and decrease of volume.
PLoS One
January 2025
School of Industrial and Management Engineering, Korea University, Seongbuk-gu, Seoul, Republic of Korea.
A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researchers have explored clinical predictive models using real medical text data. Medical text data include large amounts of information regarding patients, which increases the sequence length.
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