Objective To summarize the clinical features of spontaneous remission in classic fever of unknown origin (FUO). Methods Medical records of 121 patients diagnosed with FUO at admission in Peking Union Medical College Hospital between January 2018 and June 2018 were reviewed retrospectively. Patients who were discharged without etiological diagnoses were followed for 2 years. The clinical features and outcomes of these patients were summarized. Multivariate regression was used to analyze related factors of spontaneous remission of FUO. Results After excluding 2 patients who lost to follow-up, the etiology of 119 FUO patients were as follows: infectious diseases in 30 (25.2%) cases, connective tissue diseases in 28 (23.5%) cases, tumor diseases in 8 (6.7%) cases, other diseases in 6 (5.0%) cases, and unknown diagnoses in 47 (39.5%) cases. Totally, 41 patients experienced spontaneous remission of fever (the median time from onset to remission was 9 weeks, ranging from 4 to 39 weeks). In patients with spontaneous remission in FUO, lymphadenopathy was less common clinical manifestation, the levels of inflammatory markers including leukocyte count, neutrophil count, neutrophil ratio, C-reactive protein, and ferritin were lower, and the proportion of CD8 positive T lymphocytes expressing CD38 was lower. Multivariate regression analysis of factors with a -value < 0.05 in univariate analysis shown that white blood cell count (: 0.545, 95%: 0.306-0.971, = 0.039), neutrophil count (: 2.074, 95%: 1.004-4.284, = 0.049), and proportion of neutrophils (: 0.928, 95%: 0.871-0.990, = 0.022) were independent significant factors associated with spontaneous remission in FUO. Conclusions This study suggested that most patients discharged with undiagnosed classic FUO would remit spontaneously. Thus, for patients with stable clinical conditions, follow-up and observation could be the best choice. Patients with lower level of some inflammatory factors may have a high likelihood of spontaneous remission in classic FUO.
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http://dx.doi.org/10.24920/003975 | DOI Listing |
BMC Ophthalmol
December 2024
Department of Ophthalmology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
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CEN Case Rep
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Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
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View Article and Find Full Text PDFJ Allergy Clin Immunol
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Department of Internal Medicine, Division of Rheumatology, Allergy and Immunology, University of Cincinnati, USA.
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Crit Rev Oncol Hematol
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Nuclear Medicine Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy; Department of Life Sciences and Public Health, Catholic University of the Sacred Heart, Rome, Italy.
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View Article and Find Full Text PDFLancet Digit Health
January 2025
Department of Pediatric Cardiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Engineering Research Center of Techniques and Instruments for Diagnosis and Treatment of Congenital Heart Disease, Ministry of Education, Shanghai, China. Electronic address:
Background: Perimembranous ventricular septal defect (PMVSD) is a prevalent congenital heart disease, presenting challenges in predicting spontaneous closure, which is crucial for therapeutic decisions. Existing models mainly rely on structured echocardiographic parameters or restricted data. This study introduces an artificial intelligence (AI)-based model, which uses natural language processing (NLP) and machine learning with the aim of improving spontaneous closure predictability in PMVSD.
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