Surgical site infections (SSIs) continue to be of great concern as they significantly contribute to morbidity, hospitalization extensions, and healthcare costs, particularly in low-resource regions such as Ethiopia. These preventable infections persist at a high rate, especially within sub-Saharan Africa due to restricted healthcare resources and inconsistent adherence to surgical protocols. A crucial challenge for Ethiopia, like many similar settings, is the absence of comprehensive, context-specific predictive tools for managing the risk of SSIs.
Existing predictive models for SSI risk commonly originate from high-resource environments and may not accurately depict risk factors specific to Ethiopia. This study aimed to develop and validate a nomogram for surgical site infection prediction among general surgery patients, specifically in the Amhara region of Ethiopia. The researchers conducted a prospective follow-up study with patients at referral hospitals in the region. The investigation identified predictors of SSIs using logistic regression analysis, based on which a nomogram was constructed.
This model demonstrated impressive internal validation and accuracy. Its metrics included a 39.6% incidence of SSIs and identified sex, age, diabetes, wound classification, wound care, American Society of Anesthesiologists (ASA) score, patient residence, surgery duration, preoperative hospital stays, alcohol consumption, and previous surgical history as key predictors. Furthermore, the model displayed an excellent discriminative ability (AUC of 0.87) and showed an impressive fit in calibration, reinforcing its predictive reliability. It has the potential to serve as a practical tool for healthcare professionals to identify high-risk patients, implement preventive measures accordingly, and thus reduce the burden of SSIs in Ethiopia’s healthcare system. The researchers do, however, recommend external validation to ensure broader applicability.
The threats of SSIs are not only limited to patients’ lives but also contribute to antibiotic resistance. SSIs account for an estimated 20% of healthcare-associated infections in low-income countries, severely affecting surgical outcomes and patient recovery. Effective preventive measures are therefore essential to curb the high morbidity and mortality rates associated with SSIs and their increasing global prevalence. This study provides a pathway to such proactive infection control and risk management for general surgery patients in similar resource-restricted settings.