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Updated: Jan 15, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Evaluation of Sepsis Outcomes Using SOFA, APACHE II, and SAPS Indices: A Retrospective Study in a Quaternary Care
Melvin George1, D K Sriram2, Deepalaxmi Rathakrishnan3
1Clinical Research Consultant, Department of Clinical Research, Hindu Mission Hospital, Kancheepuram, Tamil Nadu, India, Corresponding Author, Orcid: https://orcid.org/0000-0001-7101-8513.
Background:
Sepsis is a leading cause of mortality globally, yet obtaining accurate population-level data remains challenging. According to a 2020 report, there were approximately 48.9 million cases of sepsis and 11 million sepsis-related fatalities worldwide, accounting for 20% of all deaths globally. This study aims to assess the diagnostic efficacy of patient evaluation in comparison with the Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Simplified Acute Physiology Score (SAPS) indices, in a quaternary care hospital, and to analyze the impact of various clinical parameters and comorbidities on patient outcomes.
Materials And Methods:
The study was conducted at Hindu Mission Hospital in Chennai and used a retrospective design to analyze septicemia patients' data from June 2018 to January 2020. The database included clinical presentation, vital signs, comorbidities, laboratory values, and septicemia features. Specimens underwent smear microscopic analysis of the mycobacterial culture.
Results:
The study found that elevated SOFA and APACHE II scores, comorbidities, prompt antibiotic administration, and infection characteristics significantly impact sepsis patient outcomes, emphasizing the importance of timely intervention and comprehensive scoring systems.
Conclusion:
The study emphasizes the significance of a comprehensive approach to sepsis management, including early detection, prompt intervention, and managing comorbid conditions, and suggests future research should focus on accurate predictive models and personalized medicine approaches.
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