Usefulness of Gram stain examination of peritoneal fluid in postoperative peritonitis to guide empirical

Pascal Augustin1, Alexy Tran-Dinh2,3, Mathieu Desmard4

  • 1Département d'Anesthésie Réanimation Chirurgicale et, Groupe Hospitalier Bichat Claude Bernard, Assistance Publique - Hôpitaux de Paris, Université Paris Diderot Sorbonne Paris Cité, 46 rue Henri Huchard, 75018, Paris, France. pascalaugustin@hotmail.com.

Abstract

Insights

Gram stain examination (GSE) of peritoneal fluid is unreliable for guiding empirical antibiotic therapy in postoperative peritonitis. The test poorly predicts culture results, especially for Gram-positive cocci, necessitating careful consideration of empirical glycopeptide use.

Area of Science:

  • Medical Microbiology
  • Infectious Diseases
  • Surgical Infections

Background:

  • Postoperative peritonitis management often involves empirical antibiotic therapy.
  • Gram stain examination (GSE) of peritoneal fluid is proposed to guide initial antibiotic choices, including glycopeptides and antifungals.
  • Evidence supporting GSE for guiding therapy against Gram-positive cocci is lacking.

Purpose of the Study:

  • To evaluate the diagnostic performance of Gram stain examination (GSE) in predicting peritoneal fluid culture results.
  • To assess the utility of GSE in guiding empirical antibiotic therapy for postoperative peritonitis.

Main Methods:

  • Retrospective single-center study analyzing 152 episodes of postoperative peritonitis.
  • Assessed concordance between GSE and peritoneal fluid cultures for various microorganisms.
  • Evaluated factors influencing concordance, particularly for Gram-positive cocci peritonitis.

Main Results:

  • GSE demonstrated poor predictive value for peritoneal fluid culture results.
  • Positive predictive values were 87% for Gram-positive cocci (GPC), 86% for Gram-negative bacilli, and 94% for fungi.
  • Negative predictive values were 41% for GPC and 31% for Gram-negative bacilli, indicating low reliability.

Conclusions:

  • GSE is not a reliable tool for guiding empirical antibiotherapy in postoperative peritonitis.
  • The results of GSE cannot reliably exclude the presence of Gram-positive cocci in peritoneal fluid cultures.
  • Systematic empirical use of glycopeptides may be considered to ensure adequate coverage for resistant Gram-positive cocci.

Related Concept Videos

Peritoneal Dialysis II: Peritoneal Dialysis Systems and Complications01:25

Peritoneal Dialysis II: Peritoneal Dialysis Systems and Complications

Peritoneal dialysis (PD) is a medical process that removes waste products and excess fluid from the body using the peritoneal membrane as a natural filter.Peritoneal Dialysis MethodsSeveral methods can be used for peritoneal dialysis, including Acute Intermittent Peritoneal Dialysis, Continuous Ambulatory Peritoneal Dialysis, and Automated Peritoneal Dialysis, also known as Continuous Cyclic Peritoneal Dialysis.Acute Intermittent Peritoneal Dialysis (AIPD) is used for patients with uremic...
643
Peritoneal Dialysis I: Introduction and Procedure01:30

Peritoneal Dialysis I: Introduction and Procedure

Peritoneal dialysis (PD) is a procedure that facilitates the exchange of solutes, waste products, electrolytes, and excess fluid between the blood in the peritoneal capillaries and a dialysis solution introduced into the peritoneal cavity.Principles of Peritoneal Dialysis (PD)Diffusion: Waste products such as urea and electrolytes move from high concentrations in the blood to low concentrations in the dialysate across the peritoneal membrane. This mechanism is driven by the concentration...
2.8K
Peritoneal Dialysis III: Nursing Management01:25

Peritoneal Dialysis III: Nursing Management

Peritoneal dialysis, or PD, utilizes the peritoneal membrane as a filter to eliminate excess fluid and waste products. Effective nursing management is essential for ensuring patient safety, preventing complications, and promoting optimal function of the peritoneal dialysis process.Assessment and MonitoringNurses must thoroughly assess the patient before, during, and after each dialysis session. Regular monitoring includes vital signs, daily weight, fluid intake and output, and laboratory values...
743
Extracorporeal Removal of Drugs: Peritoneal Dialysis and Hemodialysis01:30

Extracorporeal Removal of Drugs: Peritoneal Dialysis and Hemodialysis

Patients with end-stage renal disease (ESRD) or those experiencing drug overdose often require extracorporeal methods to eliminate accumulated drugs and metabolites. Hemoperfusion, hemofiltration, and dialysis are the primary techniques to rapidly remove harmful substances without disrupting the patient's fluid and electrolyte balance. For those with compromised renal function, dosage adjustments of concurrent medications may be necessary during extracorporeal drug removal.Dialysis is a process...
464
Sputum Studies I: Gram Stain, cytology, and Acid-fast smear and culture01:26

Sputum Studies I: Gram Stain, cytology, and Acid-fast smear and culture

Sputum studies are a critical part of diagnosing and treating numerous respiratory conditions. These studies involve obtaining sputum samples for analysis to identify pathogenic organisms and assess the presence of abnormal cells indicative of malignant conditions. This lesson will delve into three fundamental sputum studies: Gram Stain, Cytology, and Acid-fast Smear and Culture.
Gram Stain
The Gram Stain is an integral part of sputum studies. It involves the staining of sputum, which permits...
618
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
9.4K