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Dynamics Of C-Reactive Protein In The Early Postoperative Period As A Predictor Of Infectious Complications And A Tool For Optimizing Antibiotic Therapy

Ochakovskaya, I. N.; Onopriev, V. V.; Dovlatbekyan, N. M.; Zhuravleva, K. S.; Zamulin, G. Y.; Durleshter, V. M.

2026-04-07 infectious diseases
10.64898/2026.04.06.26350253 medRxiv
Show abstract

Objective. To evaluate the diagnostic and prognostic significance of C reactive protein (CRP) level dynamics within the first five days after surgery for the early detection of surgical site infections (SSI) and to identify independent risk factors, taking into account regional specifics of surgical management (types of surgeries, duration of procedures), as well as the local hospital microbial landscape. Materials and Methods. A single-center retrospective cohort analysis of data from 127 patients who underwent surgical procedures between 2022 and 2024 was conducted. CRP levels on postoperative days 1, 3, and 5 were assessed, and delta values were calculated. Descriptive statistics, ROC analysis, and multivariate logistic regression were used to identify predictors of SSI. Results. Patients with SSI lacked the physiological decrease in CRP levels by day 5. The most informative indicator was the CRP level on day 3: a threshold of >106 mg/L was associated with a high risk of SSI (AUC=0.76; sensitivity 85%, specificity 63%). Independent predictors of SSI included surgery duration (OR=1.015 per 1 min; p<0.001) and the increase in CRP between days 3 and 5 (delta CRP3-5: OR=1.027; p=0.023). A combined model (clinical parameters + CRP) demonstrated the highest predictive ability (AUC=0.79). Conclusion. Monitoring CRP dynamics, particularly on days 3 and 5, is a highly informative and accessible method for the early diagnosis of SSI. A CRP threshold of >100 mg/L on day 3 and its subsequent increase should serve as a trigger for in-depth diagnostic investigation and rationalization of antimicrobial therapy. Keywords: C reactive protein, postoperative complications, surgical site infection, antibiotic therapy, predictive factors, diagnosis

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