Concept of Infectious Perioperative Risk (Analytical Review)

Abstract

Objective . To substantiate the concept of perioperative infectious risk by analyzing current approaches to classifying complications, methods for predicting them, and the key factors influencing surgical outcomes.  Materials and Methods. We conducted an analytical review based on expert judgement and a systematic search of publications from the preceding 10 years in PubMed and Lens.org. The analysis included data on the epidemiology of infectious complications, classification systems (CDC, Clavien-Dindo, Accordion), prognostic scores (ASA, SOFA, ACS-NSQIP, SORT), laboratory markers (CRP, IL-6, procalcitonin, neutrophil-tolymphocyte ratio), and machine learning models (PERISCOPE AI, Random Forest, SVM, ensemble methods).  Results . The risk of infectious complications was shown to be determined by the cumulative effect of modifiable and nonmodifiable factors related to both the patient and the healthcare facility. Laboratory markers demonstrated high prognostic value: IL-6 levels above 432 pg/mL and CRP 150 mg/L on postoperative day 3 were statistically significantly associated with adverse outcomes. Advanced prognostic tools provided high accuracy: the AUROC for ACS-NSQIP reached 0.80, and for PERISCOPE AI, 0.82–0.91. The use of scoring systems and machine learning made it possible to identify high-risk patients before clinical symptoms appeared.   Conclusion . Risk of perioperative infections is multifactorial and requires an integrated approach to risk stratification, incorporating clinical data, risk assessment scales, laboratory markers, and AI models. The use of structured prognostic tools enables early identification of complications, optimize prevention, and reduce mortality.