Breast surgery is a broad concept that encompasses various surgical procedures. Common breast surgeries include mastectomy, breast reconstruction, breast reduction, and breast augmentation, among others. The frequency of mammoplasty surgeries has increased significantly in recent years, driven by medical advancements and a growing desire for aesthetic enhancement, particularly in the United States [1]. Breast surgery is generally considered a safe procedure [2]; however, the incidence of surgical site infections (SSIs) remains high, ranging between 1% to 35% [3]. SSI is one of the most common complications of breast surgery. It has been shown that SSIs prolong hospital stays, lead to reoperations and readmissions, and can even increase mortality. SSIs also result in a substantial financial burden, with additional costs arising from medical staff, investigations, and treatment [4].

The risk factors for SSIs following breast surgery have been reported in various studies; however, the results differ across these studies. There are various risk factors for SSI following breast surgery, including age [5], tobacco use [6], diabetes [7], obesity [8], flap necrosis [9], medical staff [10], malignancy [5], mastectomy [11], prior radiotherapy or chemotherapy [12, 13], inappropriate use of antibiotics [14], prolonged postoperative drainage [15], and immediate reconstructive surgery [16].

The existing literature primarily relies on observational studies with small sample sizes, characterized by wide methodological variations and varying conclusions. In addition, many risk factors lack reliable quantitative syntheses. This meta-analysis provides updated and more comprehensive data to support the prediction and prevention of SSIs after breast surgery by pooling high-quality evidence, utilizing standardized definitions, and quantifying pooled effect sizes of risk factors in 169,206 patients. The aim is to provide clinicians actionable insights for risk stratification and prevention.

The current study used a quantitative systematic review and meta-analysis to summarize the data for risk variables for SSI incisions following breast surgery. In total, 12 studies were identified with 2412 SSI-positive cases and 166,794 SSI-negative cases, under 22 identified risk factors. These factors included BMI, previous RT/CH, mastectomy, length of drain stay, second drain insertion, implant, antibiotic use, transfusion, smoking, anesthetic use, alcohol consumption, ASA score, flap failure, operative time, diabetes, hypertension, age, ASC, dressing change, seroma, and axillary incision. However, only 5 could be synthesized for the meta-analysis. Of these, mastectomy (OR = 2.61, p 0.001), diabetes (OR = 2.49, p 0.001), BMI $\ge$25 kg/m² (OR = 2.08, p 0.001), ASA $\ge$3 (OR = 1.99, p 0.001), and smoking (OR = 1.38, p 0.001) were factors that significantly increased the risk of SSI.

The largest risk factor for SSI incisions is mastectomy (OR = 2.61), which demonstrates 2.61 times higher odds of developing SSI [30]. Some articles have shown that mastectomy is one of the risk factors for postoperative SSI when compared to breast-conserving surgery (BCS) [31, 32]. Compared to BCS, mastectomy involves extensive tissue removal and a larger surgical wound, which can disrupt the vascular supply, create a more hypoxic environment in the tissue, and increase the risk of bacterial colonization. However, this does not imply that BCS should always be prioritized in clinical practice. Clinicians should focus on the patient’s individual condition and requirements. After mastectomy, the patient’s surgical incision should be closely monitored, and measures to prevent incision infection should be implemented.

Diabetes is the second significant risk factor for SSI. Elevated blood glucose levels promote bacterial adhesion and biofilm formation, exacerbating infection risks. It could be argued that BMI and diabetes are linked, as obesity-induced insulin resistance is one of the leading causes of type 2 diabetes [33]. This may explain the variability of the ORs in BMI $\ge$25 (OR = 2.08) and diabetes (OR = 2.49). The datasets included in the analysis were reported separately for only 2 exposure factors, diabetes and BMI $\ge$25 kg/m². However, we were unable to obtain the specific number of patients with diabetes and BMI $\ge$25 kg/m², as well as those without diabetes and BMI $\ge$25 kg/m². Therefore, although the ORs for diabetes and BMI were derived in this study, it is prudent to consider that the values may be somewhat biased due to the lack of detailed data on the overlap between these factors. It is suggested that future studies should report the number of patients with similar correlations in greater detail to provide more accurate and reliable estimates.

To further address this issue, the current study conducted a meta-regression analysis to investigate whether BMI predicts the ORs of diabetes. The analysis found no significant relationship between continuous BMI values and the ORs of diabetes. Notably, the absence of a significant predictive relationship between BMI and the OR for diabetes does not imply that these two factors were unrelated or that obesity does not influence the estimation of OR for diabetes. On the one hand, the absence of a significant predictive relationship might arise from the inclusion of both type I and type II diabetes in the studies, with type I diabetes having less direct relevance to obesity. On the other hand, the estimation of ORs may have been inflated due to the repeated counting of cases exposed to multiple risk factors. Instead, this result might suggest that the pathological mechanisms of obesity and diabetes operate relatively independently in the context of SSI risk, rather than one being a direct predictor of the other.

Apart from diabetes and BMI $\ge$25 kg/m², the third-largest risk factor was ASA $\ge$3 (OR = 1.99, p 0.001), with a 99% higher odds of SSI incisions. The ASA Physical Status Classification System categorizes patients based on their overall health status and their risk associated with surgery. The purpose of ASA grading is to evaluate a patient ability to tolerate the risks associated with surgery and anesthesia by evaluating their overall health status and comorbid conditions. Based on the scoring rules, it can be assumed that a higher score indicates lower overall physical fitness, making patients with high scores more prone to postoperative surgical incision infections. One study has shown a clear and strong relationship between ASA classification and postoperative complications, as well as all-cause mortality [34]. A possible reason is that patients with ASA $\ge$3 often have comorbidities (e.g., cardiovascular disease, renal dysfunction) that reduce physiological reserve, delay recovery, and increasing susceptibility to infections.

Smoking was the last risk factor (OR = 1.38, p 0.001), with a 38% higher odds of SSI incisions. Nicotine in tobacco can weaken the immune response and reduce the body’s defense against pathogens [35]. Smoking reduces the oxygenation capacity of the erythrocytes [36], affecting the oxygenation of wound tissue and preventing proper wound healing. Additionally, nicotine can cause a contraction of the capillary blood vessels, impairing the blood supply around the wound and providing conditions for bacterial reproduction [37]. Smoke also stimulates the respiratory tract to produce more secretions, increasing coughing, which may lead to surgical site dehiscence, further increasing the risk of lung infections [38].

To account for multiple comparisons across risk factors, we applied the Benjamini-Hochberg false discovery rate (FDR) correction. After adjusting for multiple comparisons using the FDR correction, the significance threshold for each test was adjusted to q = 0.05/5 = 0.01 and all identified risk factors remained statistically significant, confirming that mastectomy, diabetes, BMI $\ge$25 kg/m², ASA $\ge$3, and smoking are predictors of SSI. There are four limitations in this study. First, types of breast surgeries (e.g., simple mastectomies, therapeutic mammoplasties, and reconstructions with implants or autologous tissue) can also affect SSI, but this was not explored in this paper. Second, few studies specified the types of SSI (superficial, deep, or organ space), and the dataset was not sufficiently large for us to analyze in subgroups; therefore, we did not perform subgroup analyses. Future research should provide more comprehensive data to address these limitations. Third, 11 of the 12 studies included in this review were conducted in the US or Europe, limiting the global applicability of the findings. The economic situation, clinical practices, and healthcare facilities in different regions influence SSI risk factors. Antibiotic prophylaxis protocols vary globally: the US emphasizes the avoidance of antibiotic misuse, the United Kingdom clarifies that routine antibiotic prophylaxis is not required for clean incisions, and China limits prophylactic drug use to less than 30% for benign breast surgery. In addition, differences in BMI distribution, prevalence of diabetes, and prevalence of smoking in different regions may further confound risk estimates. For example, obesity rates are much higher in the US than in China, which may have led to an overestimation of the risk OR for BMI $\ge$25 kg/m² in this study. Fourth, the studies included in this article span nearly two decades (2004–2023). With rapid advances in clinical medicine, significant progress has been made in breast surgery techniques and antimicrobial practices. For example, the 2017 update of the Centers for Disease Control and Prevention (CDC) Guideline for the Prevention of Surgical Site Infections refined recommendations for the timing and dosage of preoperative antibiotics, while the adoption of minimally invasive surgeries has further reduced the incidence of SSIs. However, our meta-analysis did not adjust for temporal trends, which may confound historical and contemporary risk estimates. Additionally, the insufficient stratification of the data from the included studies precludes an in-depth exploration of these trends.

This meta-analysis identified 5 significant risk factors for SSI after breast surgery, with mastectomy being most strongly associated with SSI. The factors that significantly increased the risk of SSI included mastectomy, diabetes, BMI $\ge$25 kg/m², ASA $\ge$3, and smoking. Clinicians should prioritize preoperative optimization, including glycemic control and smoking cessation programs for diabetic patients. In obese patients (BMI $\ge$25 kg/m²), wound monitoring and wound care should be enhanced. Patients with high ASA scores ($\ge$3) require preoperative evaluation. Where feasible, BCS should be prioritized over mastectomy to minimize tissue trauma. It is also recommended that future studies provide more detailed information, such as the categorization of the three types of SSIs.

All relevant data are within the manuscript and its Supporting information files.

YL and ZY designed the research study, and drafted the protocol documents for registration. YL, ZY and HZ were the independent investigators to conducted the study’s review, quality assessment, and data extraction. YL and ZY completed the data analysis, DW interpretation of data for the work, and YL drafted the manuscript. Finally, ZY and DW revised the draft. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

Not applicable.

Thanks to all the researchers who contributed to this paper and provided advice.

This research received no external funding.

The authors declare no conflict of interest.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/CEOG37161.