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A Rare Hemorrhagic, Orange-Colored Ascites, Challenging Traditional Ascitic Fluid Analysis.

Huma QuanungoHuda QuanungoElena NaderzadFrederick VenterElaine DeemerGreti PetersenAlan Ragland
Published in: Journal of investigative medicine high impact case reports (2023)
Analysis of ascitic fluid can offer useful information in developing and supporting a differential diagnosis. As one of the most prevalent complications in patients with cirrhosis, ascitic fluid aids in differentiating a benign condition from malignancy. Both the gross appearance of the ascitic fluid, along with fluid analysis, play a major role in diagnosis. Here, we discuss a patient with liver cirrhosis, esophageal varices, hepatitis C, and alcohol abuse, who had a paracentesis performed, which revealed a turbid, viscous, orange-colored ascitic fluid that has not been documented in literature. Ascitic fluid is routinely analyzed based on gross appearance, cell count, and serum ascites albumin gradient (SAAG) score. An appearance of turbidity or cloudiness has commonly suggested an inflammatory process. In our case, fluid analysis revealed a red blood cell count of 24 250/mcL, further suggesting inflammation. However, it also revealed an insignificant number of inflammatory cells, with a total nucleated cell count of 14/mcL. This rich-orange color has posed a challenge in classification and diagnosis of the underlying cause of ascites, with one classification system suggesting inflammation, while another suggesting portal hypertension. Furthermore, we have traditionally relied on the SAAG score to aid in determining portal hypertension as an underlying cause of ascites. With a 96.7% accuracy rate, the SAAG score incorrectly diagnosed portal hypertension in this patient. In this article, we aim to explore how this rare, orange-colored ascitic fluid has challenged the traditional classification system of ascites.
Keyphrases
  • blood pressure
  • single cell
  • oxidative stress
  • red blood cell
  • systematic review
  • stem cells
  • healthcare
  • machine learning
  • mesenchymal stem cells
  • computed tomography
  • bone marrow
  • signaling pathway