Differential diagnosis of ascites: etiologies, ascitic fluid analysis, diagnostic algorithm.
ascites
ascitic fluid analysis
diagnosis algorithm
etiology
Journal
Clinical chemistry and laboratory medicine
ISSN: 1437-4331
Titre abrégé: Clin Chem Lab Med
Pays: Germany
ID NLM: 9806306
Informations de publication
Date de publication:
20 Dec 2023
20 Dec 2023
Historique:
received:
06
10
2023
accepted:
07
12
2023
medline:
19
12
2023
pubmed:
19
12
2023
entrez:
19
12
2023
Statut:
aheadofprint
Résumé
Ascites is the pathological accumulation of fluid within the peritoneal cavity. It often occurs as results of liver cirrhosis, malignant neoplasia, tuberculous infection, cardiac insufficiency, renal diseases, etc. Determining the etiology is an essential step in the management of patients with new-onset ascites. Abdominal paracentesis with appropriate ascitic fluid analysis is probably the most cost-effective method of determining the cause of ascites. We performed a literature search of PubMed and identified articles published in the field of ascites, to evaluate diagnostic values of various parameters in defining the etiologies of ascites and then provides diagnostic algorithm for patients with new-onset ascites. In patients with ascites, the constituent ratio of underlying etiology varies between developed and developing countries. It is a challenge to define the etiologies of ascites in developing countries. Routine ascitic fluid analysis should include the serum ascites albumin gradient (SAAG), total protein concentration, cell count and differential. Optional ascitic fluid analysis includes cholesterol, fluid culture, cytology, tumor markers, lactate dehydrogenase, adenosine deaminase (ADA), triglyceride, amylase, glucose, brain natriuretic peptide (BNP), etc. Our review evaluated diagnostic values of the above parameters in defining the etiologies of ascites. Diagnostic algorithm established in this review would provide a practical and convenient diagnostic strategy for clinicians in diagnosing patients with new-onset ascites.
Identifiants
pubmed: 38112289
pii: cclm-2023-1112
doi: 10.1515/cclm-2023-1112
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023 Walter de Gruyter GmbH, Berlin/Boston.
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