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Colon Ascendens Stent Peritonitis CASP - a Standardized Model for Polymicrobial Abdominal Sepsis
Published on: December 18, 2010
Literature Review of Prognostic Factors in Secondary Generalized Peritonitis
Valerii Luțenco1,2, Adrian Beznea1,2, Raul Mihailov1,2
1Faculty of Medicine and Pharmacy, "Dunărea de Jos" University of Galati, 800008 Galați, Romania.
Abstract:
Generalized secondary peritonitis is a life-threatening intra-abdominal infection requiring urgent surgical intervention. Despite advances in surgical and antimicrobial therapy, morbidity and mortality remain high. Identifying key prognostic factors is crucial for improving patient outcomes. This review examines significant prognostic indicators and explores the potential role of scoring systems and artificial intelligence in risk stratification. A review was conducted using PubMed, Web of Science, Scopus, and Medline databases. Studies published from 2000 to 2024 focusing on prognostic factors in secondary peritonitis were included. A total of 145 studies were identified, with 40 selected based on relevance and methodological quality. Data extraction included patient demographics, comorbidities, severity scores, microbiological profiles, and artificial intelligence applications in peritonitis management. Poor prognosis was associated with advanced age, severe sepsis, organ failure, chronic kidney disease, cardiovascular comorbidities, and diabetes mellitus. The Mannheim Peritonitis Index (MPI) remains a widely validated prognostic tool, while APACHE II and SOFA scores also provide valuable risk estimates. Increasing multidrug-resistant infections further complicate management and impact outcomes. Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. Identifying prognostic factors remains essential for optimizing outcomes in secondary peritonitis, and future research should prioritize the clinical validation and integration of AI-based models into perioperative management protocols.
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