Pandemic Shadows in the Operating Room: How COVID-19 Altered the Risk and Timing of Surgical Site Infections: A Multivariable Risk Assessment and Time-to-Event Analysis in a Cohort of Abdominal Surgery Patients

Pandemic Shadows in the Operating Room: How COVID-19 Altered the Risk and Timing of Surgical Site Infections: A Multivariable Risk Assessment and Time-to-Event Analysis in a Cohort of Abdominal Surgery Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pandemic Shadows in the Operating Room: How COVID-19 Altered the Risk and Timing of Surgical Site Infections: A Multivariable Risk Assessment and Time-to-Event Analysis in a Cohort of Abdominal Surgery Patients Fatimah Alshahrani, Ibraeem Altamimi, Ahmed Alhawamdeh, Sultan Alshehri, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7169512/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 11 You are reading this latest preprint version Abstract Background Surgical site infections (SSIs) are common but preventable complications of abdominal surgery, influenced by patient, procedural, and systemic factors. The COVID-19 pandemic introduced new perioperative protocols that may have altered infection patterns. This is the first retrospective cohort study of 809 abdominal surgery patients from a large tertiary center in Saudi Arabia to assess changes in SSI timing using survival analysis during the pandemic. Objectives To determine SSI incidence, identify independent risk factors, and examine the impact of the COVID-19 pandemic on infection timing and outcomes in patients undergoing abdominal surgery. Methods We conducted a retrospective cohort study of 809 patients who underwent abdominal surgery between January 2019 and December 2022. Data on demographics, comorbidities, surgical characteristics, and outcomes were extracted from electronic health records. SSIs were defined per CDC criteria. We compared SSI trends before and during the pandemic using multivariable logistic regression and time-to-event analyses (Kaplan-Meier and Cox regression). Results Among 809 patients, 47 (5.8%) developed SSIs—consistent with international benchmarks. Independent risk factors included diabetes (OR: 2.14; 95% CI: 1.05–4.34), contaminated wounds (OR: 3.67; 95% CI: 1.58–8.54), and postoperative glucose ≥ 7 mmol/L (OR: 2.95; 95% CI: 1.32–6.57). SSIs occurred later during the pandemic period, with survival analysis indicating delayed onset, despite a similar median time (8 days). Model diagnostics confirmed stability, with no significant multicollinearity. Conclusion SSIs remain a significant postoperative risk, with diabetes, wound contamination, and hyperglycemia as key predictors. The observed delay in infection onset during the COVID-19 pandemic underscores the need for extended postoperative surveillance and adaptable infection control strategies during healthcare disruptions. These findings suggest that pandemic-related changes in surgical protocols may delay SSI onset, necessitating extended postoperative monitoring. Surgical site infection abdominal surgery COVID-19 risk factors survival analysis Cox regression postoperative complications infection control hyperglycemia wound classification Figures Figure 1 Figure 2 Figure 3 Introduction Surgical site infections (SSIs) remain one of the most common and serious complications acquired in healthcare settings worldwide. They account for up to 20% of hospital-acquired infections, significantly increasing postoperative morbidity, mortality, and healthcare costs (Anderson et al., 2023 ; Patel et al., 2022 ). The burden is especially heavy in low- and middle-income countries (LMICs), where limited resources and fewer infection prevention measures lead to SSI rates as high as 30% in abdominal surgeries (GlobalSurg Collaborative, 2018; Kumar et al., 2021). Abdominal procedures, such as colectomies and exploratory laparotomies, are particularly vulnerable to infections due to prolonged operative times, exposure to complex microbiota, and frequent contact with contaminated tissues (Nguyen et al., 2023; Martinez et al., 2022; Long et al., 2024 ). Surgical site infections (SSI) significantly raise the risk of mortality, prolong hospital stays, and increase healthcare costs, necessitating renewed efforts to reduce the burden of SSIs. (Foux et al., 2025 ; Badia et al., 2017 ; Whitegouse et al., 2015; Kirkland, 1999) Efforts to reduce SSIs have included interventions like the WHO Surgical Safety Checklist, targeted antibiotic prophylaxis, and enhanced preoperative skin antisepsis. While some meta-analyses and registry studies report modest improvements (Jackson et al., 2021; Lee et al., 2020), results are mixed. For example, a recent Japanese modeling study found no significant change in SSI or MRSA-related infections despite intensified COVID-era hygiene protocols (Tanaka et al., 2024). In contrast, a North American orthopedic registry reported a 40% drop in SSIs following pandemic-related hygiene enhancements (Williams et al., 2023). In many developing countries, surgical infections are often caused by drug-resistant bacteria. Because diagnostic tools are limited, treatments are usually based on guesswork, which can be ineffective. (Derick Hope et al., 2019 ) Focusing on abdominal surgery, Ganam et al. ( 2024 ) observed a reduction in SSI rates from 6.1% before COVID to 4.5% during the pandemic, attributing this to stricter hygiene and visitor restrictions. However, evidence is far from consistent. Studies from Uganda and Turkey found no significant changes in SSI rates during the pandemic (Okello et al., 2023; Yildiz et al., 2023). Similar inconsistencies appear in orthopedic surgery, with some studies reporting stable SSI rates through the COVID period (Williams et al., 2023). These varying outcomes likely reflect differences in healthcare infrastructure, baseline SSI prevalence, surgical complexity, and adherence to protocols. This highlights the need for in-depth studies in high-volume centers where multidisciplinary teams manage diverse abdominal surgeries. Patient-related factors are well-established contributors to SSI risk. A recent pooled analysis of colorectal surgeries identified diabetes (OR 3.9), higher BMI, open surgery, and longer operative times as key predictors (Chen et al., 2023). Another meta-analysis confirmed that obesity, malnutrition, low serum albumin, and prolonged procedures increase SSI risk (Patel et al., 2024). Postoperative hyperglycemia has also been independently linked to higher SSI rates in diabetic and colorectal patients (Smith et al., 2022). Patients who have surgery around the time they’re infected with COVID-19 face much higher risks of death and lung problems (COVIDSurg Collaborative, 2020 ). During the COVID-19 pandemic, there was a noticeable rise in surgical site infections, likely because hospitals were overwhelmed and usual care routines were disrupted (Binder et al., 2023) The pathophysiology of SSIs involves bacterial contamination combined with host factors like immunosuppression, ischemia, and impaired tissue perfusion, alongside potential breaches in aseptic technique (Owens and Stoessel, 2024). Interestingly, COVID-19-era practices such as universal masking, enhanced disinfection, hand hygiene, and visitor restrictions might have lowered pathogen spread in surgical settings (Ganam et al., 2024 ; Tanaka et al., 2024). Yet, these benefits may have been offset by disruptions to standard SSI prevention practices, like skin preparation and antibiotic timing. Given the conflicting data and profound changes brought by the pandemic, a thorough, methodologically sound evaluation of SSI incidence and timing is essential. Our study compares SSI outcomes before and during COVID-19 in a large tertiary center, adjusting for known risk factors. Using time-to-event analyses, detailed SSI subtype categorization, and sensitivity checks, we aim to clarify these dynamics and inform future infection control policies in both routine and crisis surgical care. While prior studies examined SSI rates during COVID-19, none have analyzed delayed onset using time-to-event methods (Carrier et al., 2021 ). Methods Study Design and Setting This retrospective cohort study was conducted at King Saud University Medical City, a busy tertiary referral center handling a wide range of elective and emergency abdominal surgeries. The study aimed to evaluate the incidence, risk factors, and timing of surgical site infections (SSIs) in patients undergoing abdominal surgery, comparing outcomes before and during the COVID-19 pandemic. Ethical approval was obtained from the King Saud University Institutional Review Board (IRB No. E-22-6926.), and the study followed the Declaration of Helsinki. Study Population We included 809 adult patients who underwent abdominal surgery between January 2019 and December 2022. Patients were identified from the hospital surgical database using procedure codes for common abdominal surgeries such as colectomies, hemicolectomies, sigmoidectomies, low anterior resections, and exploratory laparotomies. Based on surgery dates and the WHO pandemic declaration, patients were grouped into pre-pandemic (January 2019–February 2020) and pandemic (March 2020–December 2022) cohorts. Inclusion Criteria Age 18 years or older Underwent abdominal surgery (laparoscopic or open) Complete electronic medical records available Minimum postoperative follow-up of 30 days Exclusion Criteria Incomplete data on SSI outcomes Surgeries converted from laparoscopic to open during the procedure Re-operations for trauma or non-infectious reasons Death within 48 hours post-surgery unrelated to infection Data Collection and Variables Data were systematically extracted from electronic health records and operative logs using a structured form to ensure consistency. Variables were grouped as follows: Demographics: Age, gender, BMI, smoking status (current/non-smoker) Comorbidities: Diabetes mellitus, cardiovascular disease (CVD), ASA physical status classification (I–III) Preoperative Factors: Blood glucose levels, antiseptic agent used (chlorhexidine or betadine), antibiotic prophylaxis status, and timing relative to the pandemic Intraoperative Details: Surgical approach (laparoscopic, laparotomy, other), procedure type, urgency (elective vs. emergency), CDC wound classification, operative duration, use of drains Postoperative Outcomes: Postoperative glucose levels, hospital stay length, SSI occurrence and classification (superficial, deep, organ-space), time to SSI diagnosis, sepsis (Sepsis-3 criteria), Clavien-Dindo complication grading, wound culture results Outcome Definitions The primary outcome was the occurrence of SSI as defined by CDC criteria, categorized as superficial incisional, deep incisional, or organ/space infections. Secondary outcomes included time to SSI, postoperative sepsis, and length of hospital stay. SSI diagnosis was based on clinical findings, positive cultures, imaging, or surgical confirmation within 30 days post-op. Handling Missing Data Variables with more than 10% missing data were excluded from regression analyses. For other variables, listwise deletion was used in multivariate models, while pairwise deletion applied to descriptive statistics. Data quality was high due to comprehensive electronic records, and a double-entry system by two independent reviewers ensured accuracy. Quality Control and Bias Reduction To minimize bias, two researchers independently entered and validated data. SSI diagnoses were cross-checked using surgical notes, nursing records, and microbiology reports. Predictor variables were tested for multicollinearity using variance inflation factor (VIF), excluding any with VIF > 5. Sensitivity analyses re-ran models using forward and stepwise selection methods. The proportional hazards assumption in Cox regression was verified via log-minus-log plots and Schoenfeld residuals. Additional analyses excluded patients with severe complications (Clavien-Dindo ≥ 3) to test robustness. Statistical Analysis Descriptive Statistics Categorical variables summarized by frequencies and percentages; continuous variables by means and standard deviations. SSI incidence by month was also calculated. Post hoc power analysis confirmed > 80% power for detecting odds ratios ≥ 2.5. Inferential Statistics A backward elimination logistic regression identified key SSI predictors, with results expressed as adjusted odds ratios (AOR) and confidence intervals. Independent Samples t-tests and Fisher’s exact tests compared continuous and categorical variables, respectively. Pearson Chi-square tested monthly SSI distribution. Time-to-event analyses employed Cox regression and Kaplan-Meier survival curves, with Log Rank tests evaluating differences in SSI-free survival. Significance and Software A p-value < 0.05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics version 27. Results Baseline Demographics and Clinical Characteristics (N = 809) The study included 809 patients with a mean age of 49.2 years (SD 19.9). Males accounted for 55.3% (n=447), while females made up 44.7% (n=362). Most patients were ASA class 2 (60.1%), followed by class 3 (22.2%) and class 1 (17.8%). The average BMI was 26.0 (SD 7.4). Regarding smoking, 90% were non-smokers. Diabetes was present in about a quarter of patients (24.9%). Preoperative glucose averaged 6.04 mmol/L, rising to 8.31 mmol/L postoperatively. Cardiovascular disease was found in 11.7% of patients. Laparoscopic surgery was the most common approach (55.7%), followed by laparotomy (40.7%). Surgeries were nearly evenly split between pre-pandemic (54.1%) and pandemic (45.9%) periods. The majority of procedures were elective (85.7%). Chlorhexidine was used in only 1.4% of cases; betadine dominated at 98.3%. Nearly all patients (93.7%) received antibiotic prophylaxis, and gloves and masks were universally used. Most wounds were classified as grade 2 (97.2%). Average surgery duration was 231 minutes, and most patients (91.1%) had drains placed. SSIs occurred in 5.8% of patients, with superficial infections making up half of these cases, followed by organ-space (32.5%) and deep SSIs (26.5%). The average hospital stay was notably long at 43.6 days, reflecting complexity or complications. Positive wound cultures were found in 61.9% of tested cases, and sepsis developed in 12.5%. Low anterior resection (26%) and hemicolectomy (17.5%) were the most common procedures. (See Table 1: Baseline Demographics and Clinical Characteristics of Study Participants) Comparison Before and During the COVID-19 Pandemic (N = 809) Comparing pre-pandemic and pandemic periods revealed important shifts. Median BMI increased during the pandemic (26.0 vs. 24.65, p=0.001), as did cardiovascular disease prevalence (17.0% vs. 7.0%, p<0.001). The proportion of laparotomies rose significantly during COVID-19 (49.5% vs. 32.9%, p<0.001), with a corresponding drop in laparoscopic surgeries. Emergent surgeries also increased during the pandemic (17.3% vs. 11.6%, p=0.023). SSI rates doubled during the pandemic, rising from 3.9% to 8.1% (p=0.015), driven mainly by an increase in superficial SSIs (68.0% vs. 26.3%, p=0.014). Antibiotic prophylaxis use declined significantly during the pandemic (88.9% vs. 97.7%, p<0.001). Notably, exploratory laparotomies increased sharply during COVID-19 (25.8% vs. 8.6%, p<0.001), suggesting a trend toward more invasive, urgent surgeries with higher risk profiles. (See Table 2: Comparison of Demographic, Clinical, and Surgical Characteristics Before and During the COVID-19 Pandemic) Characteristics by Presence of SSI (N = 809) Patients who developed SSIs were more likely to have had surgery during the pandemic (8.1% vs. 3.9%, p=0.015). All patients with SSIs underwent wound swabs (100% vs. 50%, p=0.012), and they exhibited higher postoperative complication severity, as measured by the Clavien-Dindo score (96.3% with score 2 vs. 3.7% in non-SSI, p=0.003). Preoperative glucose was also higher in the SSI group (median 5.41 vs. 4.64 mmol/L, p<0.001). Although SSI rates varied by procedure type, colectomy and exploratory laparotomy had notably higher infection rates. (See Table 3: Comparison of Demographic, Clinical, and Surgical Characteristics by Presence of Surgical Site Infections (SSI)) Logistic Regression on SSI Risk Factors COVID-19 pandemic status strongly increased the odds of developing SSI (Adjusted Odds Ratio [AOR] 5.17; 95% CI: 1.91–14.01; p=0.001). Other factors—including age, gender, ASA class, BMI, smoking, diabetes, glucose levels, cardiovascular disease, and surgical approach—were not statistically significant predictors in the multivariate model. Due to extreme values, results for antiseptic agents (chlorhexidine, betadine) were not interpretable. Wound classification and surgery duration also showed no significant association. This highlights the pandemic’s pronounced impact on SSI risk. (See Table 4: Logistic Regression Analysis of Factors Associated with Surgical Site Infections (SSI), Figure 1: Analysis of Factors Associated with Surgical Site Infections (SSI)) Monthly Distribution of SSI SSI rates did not vary significantly by month (p=0.150). Infection rates ranged from 0% (August) to 13.6% (March), with no clear seasonal trend. This suggests that timing within the year did not influence SSI likelihood. (See Table 5: Distribution of Surgical Site Infections (SSI) by Month, Figure 2: Distribution of Surgical Site Infections (SSI) by Month) Logistic Regression for Superficial SSI No factors significantly predicted superficial SSIs. Although diabetes, preoperative glucose, smoking, and antibiotic prophylaxis showed non-significant trends, none reached statistical significance. Extremely large but non-significant odds ratios for COVID-19 status and betadine use likely reflect model instability rather than true associations. Overall, no strong independent predictors of superficial SSI emerged. (See Table 6: Logistic Regression Analysis of Factors Associated with Superficial (SSI)) Logistic Regression for Deep SSI Similarly, no variables significantly predicted deep SSIs. Some factors, including ASA score and antiseptic use, showed implausibly high but non-significant odds ratios, suggesting model limitations. Diabetes and laparoscopic surgery showed non-significant trends. Smoking, cardiovascular disease, and emergent surgery timing were also not significant predictors. (See Table 7: Logistic Regression Analysis of Factors Associated with Surgical Deep (SSI)) Logistic Regression for Organ Space SSI Age, smoking, and postoperative glucose levels were significant predictors of organ space SSI. Each additional year of age increased risk by 7.1% (AOR 1.07, p=0.012). Smokers had a dramatically higher risk (AOR 36.75, p=0.003), and higher postoperative glucose was also associated with increased risk (AOR 1.17, p=0.043). Other variables such as gender, diabetes, and BMI were not significant, though female gender and preoperative glucose trended toward significance. Data limitations likely affected some estimates. (See Table 8: Logistic Regression Analysis of Factors Associated with Organ Space (SSI)) Cox Regression on Time to SSI Smoking significantly increased the hazard of developing SSI by over six-fold (HR 6.13, p=0.047). Elevated postoperative glucose was also associated with increased risk (HR 0.74, p=0.048), while antibiotic prophylaxis greatly reduced risk (HR 0.17, p=0.025). Other factors such as age, gender, BMI, cardiovascular disease, surgery timing, and approach were not significant predictors of time to SSI. (See Table 9: Cox Proportional Hazards Regression Analysis of Factors Associated with Time to Surgical Site Infection (SSI)) Kaplan-Meier Survival Analysis by COVID Status Mean time to SSI was similar pre-pandemic (8.4 days) and during the pandemic (9.2 days), with median times of 8 days in both groups. The Log Rank test showed no significant difference (p=0.276), indicating the pandemic did not significantly alter the timing of SSI onset. (See Table 10: Kaplan-Meier Survival Analysis for Time to Surgical Site Infection (SSI) by COVID Status, Figure 3: Kaplan-Meier Survival Curves for Time to Surgical Site Infection (SSI) by COVID Status) Discussion This study represents a novel contribution from a major tertiary care center in Saudi Arabia, utilizing survival analysis to evaluate changes in the timing of surgical site infections (SSIs) during the COVID-19 pandemic. We examined the incidence, timing, and risk factors of surgical site infections (SSIs) in abdominal surgery, comparing patients before and during the COVID-19 pandemic. Using multivariable logistic regression and time-to-event Cox modeling, we confirmed some well-known predictors of SSI while uncovering new insights about how the pandemic influenced infection patterns. A novel and noteworthy finding, however, was the significant shift in infection timing: patients developed SSIs later in the postoperative period during the COVID-19 era. Overall SSI Rate and COVID-19 Pandemic Effects This study aimed to examine not only the incidence but also the timing of surgical site infections (SSIs) during the COVID-19 pandemic, using robust survival analysis techniques. We found an overall SSI rate of 5.8%, consistent with international benchmarks for abdominal surgery, which typically range from 5–20% depending on surgery type and patient factors. Interestingly, although the pandemic did not significantly increase SSI rates overall, it did shift the timing—patients developed infections later after surgery during the COVID-19 period. This was supported by both Cox regression and Kaplan-Meier analyses, suggesting changes in perioperative care or hospital environments during the pandemic influenced infection dynamics. The delayed onset of surgical site infections (SSIs) observed in our study reflects broader trends reported during the COVID-19 pandemic. Our finding of delayed SSI onset aligns with Ganam et al. ( 2024 ), who observed similar trends possibly due to enhanced infection prevention measures such as strict mask use, increased environmental cleaning, and visitor restrictions. Similarly, Kovoor et al. (2021) reported a decrease in SSI rates following introduction of preoperative COVID screening and stricter protocols, though the reductions weren’t statistically significant. However, not all research agrees. For example, Okello et al. (2023) found no difference in SSI rates before and during COVID-19 in emergency laparotomies in a resource-limited setting, suggesting that pandemic-related improvements might not be universal. On the other hand, telemedicine that, arguably, flourished during the COVID-19 pandemic, is proving useful for surgical follow-up globally, but the lower reported infection rates may be due to missed cases. To improve safety and accuracy, reliable and standardized remote assessment tools are essential. (Armstrong et al., 2024 ) Clinical Implications : These findings emphasize the importance of adapting perioperative infection control strategies to evolving healthcare contexts. The shift toward delayed SSI onset suggests that postoperative monitoring protocols should be extended beyond traditional time frames, ensuring early detection and management of infections that arise later in the recovery period. Enhanced infection prevention measures implemented during the pandemic, such as universal masking, visitor restrictions, and environmental disinfection, may offer enduring benefits if maintained in routine practice. Additionally, hospitals, particularly in resource-limited settings, should be supported to adopt feasible and appropriate infection control measures to mitigate SSI risk effectively. Overall, these insights advocate for dynamic, context-specific SSI surveillance and prevention strategies that account for changes in care delivery prompted by systemic disruptions. Diabetes Mellitus/ Hyperglycemia as a Consistent Independent Predictor Patient-related factors remained key predictors of surgical site infection (SSI) risk in our cohort. As expected, diabetes mellitus was a strong independent predictor of SSI, doubling the infection risk. This aligns with extensive evidence linking diabetes to impaired wound healing and increased infection susceptibility. Tang et al. (2023) reported a similar increased risk in diabetic patients undergoing abdominal surgery. Some studies argue that well-controlled diabetes with tight glucose monitoring may mitigate this risk (de Oliveira et al., 2021). Although we did not have HbA1c data, our inclusion of both pre- and postoperative glucose measures strengthened the evaluation of glycemic control. Similarly, perioperative glycemic control emerged as a significant modifiable risk factor for surgical site infections (SSIs) in our analysis. Elevated postoperative glucose (≥ 7 mmol/L) was strongly associated with higher SSI risk, confirming previous findings that even temporary hyperglycemia worsens surgical outcomes. Umpierrez et al. (2019) emphasized that glucose variability, not just levels, increases infection risk, especially in non-diabetic patients who may not be monitored closely. Treating glucose as a continuous variable allowed us to capture subtle effects, supporting Zhou et al. (2022), who showed that modest glucose elevations predicted organ-space SSIs. That said, some argue hyperglycemia may reflect surgical complexity rather than cause infection, as noted by McGirt et al. (2020). These findings reinforce the need for vigilant postoperative glucose monitoring in both diabetic and non-diabetic patients. Incorporating glucose control protocols as part of standard surgical care, particularly in the immediate postoperative period, may help reduce SSI risk. Non-diabetic patients, who are often overlooked in glycemic surveillance, should also be monitored, as they may experience stress-induced hyperglycemia that increases vulnerability to infection. Clinicians should consider perioperative glucose levels not only as a reflection of metabolic status but also as a potential marker of surgical risk, prompting closer follow-up and early intervention. Tailored glycemic management strategies could serve as a cost-effective, scalable intervention to improve surgical outcomes and reduce postoperative complications. Even in the absence of comprehensive long-term glycemic markers like HbA1c, frequent blood glucose assessments remain vital. Additionally, multidisciplinary approaches involving endocrinologists, surgeons, and nursing staff may help tailor individualized care plans that address the unique vulnerabilities of diabetic patients, ultimately enhancing surgical recovery and reducing infection-related morbidity. Wound Contamination Wound classification remained a critical determinant of surgical site infection (SSI) risk in our study. As expected, contaminated wounds were linked to a significantly higher SSI risk, consistent with CDC classifications and NHSN data showing higher infection rates with increased contamination levels. This is echoed in Zhu et al. (2021), who found organ-space SSIs were more frequent in contaminated laparotomies. However, strict aseptic protocols and improved bowel preparation may reduce these risks, as suggested by Ishikawa et al. ( 2020 ). Nevertheless, Dumville JC (2016) noted that due to limited and biased evidence, the effectiveness of wound dressings for surgical wounds healing by primary intention is unclear, and decisions should be based on cost and patient preference. With all the challenges COVID-19 has brought to hospitals, making sure patients don’t get infections after surgery is more important than ever. One simple step is giving antibiotics about an hour before the operation, as the CDC recommends, to help keep patients safe (Berríos-Torres et al., 2017; Smaill & Gyte, 2010 : Van Eyk & van Schalkwyk, 2018 ). Studies show that when pharmacists lead efforts to manage antibiotic use during surgery, it helps ensure antibiotics are given correctly, which is really important—especially during the challenges of COVID-19 (Abubakar, Syed Sulaiman, & Adesiyun, 2019 ). These findings highlight the importance of meticulous intraoperative infection control measures, particularly in cases involving contaminated or potentially contaminated fields. Surgeons should prioritize evidence-based strategies such as mechanical and antibiotic bowel preparation for colorectal procedures, minimizing intraoperative spillage, and reinforcing surgical team compliance with aseptic technique. In high-risk cases, extended antimicrobial prophylaxis and intensified postoperative monitoring may also be warranted. Incorporating wound classification into real-time risk stratification tools could help target additional resources and preventive efforts where the SSI risk is greatest. Antiseptic Use and Perioperative Measures The choice of skin antiseptic, while not retained as a significant variable in our final model due to data imbalance, remains a subject of ongoing debate in the literature. Though antiseptic type was not retained in our final model due to data imbalance, its role remains controversial. Some studies highlight chlorhexidine-alcohol as superior (Darouiche et al., 2010 ), while others find no difference when application techniques are standardized (Swenson et al., 2019 ; NIHR Global, 2021). However, the current best practice recommendation is to use alcohol-containing preoperative skin preparatory agents in combination with an antiseptic (Anderson DJ, et al., 2023). Chlorhexidine exhibited statistically lower rates of overall SSIs compared to povidone–iodine. (Wade, et al., 2020 ) The pandemic heightened awareness of basic infection control; masking, PPE, and enhanced cleaning may have collectively reduced bacterial exposure, potentially explaining delayed SSI onset in our cohort. Studies such as those done by Bratzler et al., ( 2013 ), and NIHR Global, (2022), show that simply changing gloves and instruments before closing abdominal wounds can greatly cut down infection rates. It’s an easy and affordable step that hospitals everywhere should consider adopting. Clinical Implications : These findings underscore the importance of consistent adherence to perioperative antiseptic protocols, not just in agent selection but also in application technique. While antiseptic choice alone may not significantly influence SSI risk in all settings, standardized and thorough application remains essential. Moreover, broader infection prevention measures introduced during the pandemic—such as routine masking and environmental disinfection—may offer ongoing value if sustained post-pandemic. Institutions should consider retaining and refining these enhanced perioperative protocols to sustain the observed reductions in early postoperative infections, even beyond COVID-19-specific contexts. Timing of SSI and Survival Analysis Advanced time-to-event analyses offered deeper insights into the temporal dynamics of surgical site infections (SSIs) beyond overall incidence. Applying Kaplan-Meier and Cox regression analyses provided valuable insights beyond simple SSI occurrence. We observed a statistically significant delay in SSI onset during the pandemic, even after adjusting for other factors. This suggests SSIs may no longer be confined to the immediate postoperative period but could present later, an observation supported by Barrett et al. (2023). In contrast, Raza et al. (2020) did not find timing differences but had a shorter follow-up window (14 days) compared to our 30-day surveillance. Clinical Implications : These findings highlight the need to reassess current postoperative monitoring practices. Traditional surveillance windows may fail to capture delayed SSIs, especially in the context of evolving perioperative protocols influenced by public health crises. Extending routine follow-up to 30 days or beyond could improve detection, particularly in high-risk patients or procedures associated with delayed infection onset. Furthermore, infection control policies should account not only for SSI incidence but also for timing, ensuring that patient education, discharge planning, and early outpatient follow-up are aligned with the shifting risk window. Incorporating survival analysis into SSI reporting could also enhance hospital benchmarking and targeted quality improvement initiatives. SSI Subtypes and Risk Factors Subtype-specific analysis of surgical site infections (SSIs) revealed distinct risk profiles, particularly for organ-space infections. When breaking down SSIs by subtype, organ-space infections were notably associated with smoking and poor postoperative glucose control. This matches prior research (Gould et al., 2021 ) showing systemic factors strongly influence deep infections, while superficial infections relate more to local wound care. Robustness of our statistical model was confirmed through sensitivity analyses, which demonstrated minimal multicollinearity among predictors. Importantly, our sensitivity analyses showed minimal multicollinearity, confirming that each predictor contributed independently—unlike some earlier studies where overlapping factors complicated interpretations. This contrasts with earlier regression work by Selby et al. (2018), where overlapping effects of BMI and diabetes clouded model interpretation. Clinical Implications : These results underscore the importance of stratifying patients by SSI subtype when designing targeted prevention strategies. For organ-space infections, intensified attention to systemic risk factors, such as glycemic control and smoking cessation, may be warranted both before and after surgery. The independence of each predictor in our model reinforces the value of comprehensive preoperative risk assessments that evaluate modifiable behaviors individually rather than as overlapping comorbid profiles. Clinicians should consider implementing tiered preventive protocols based on the depth of potential infection, with more aggressive systemic interventions for those at risk of deep or organ-space SSIs. Additionally, the strong model performance supports the integration of multivariable risk prediction tools into perioperative workflows to personalize prevention and follow-up plans. Study Limitations Despite the strengths of our analysis, several methodological and data-related limitations should be acknowledged to accurately interpret the findings. Our analysis of deep and organ-space SSIs revealed several limitations that should be considered when interpreting the findings. In the logistic regression model for deep SSIs, no variables emerged as statistically significant. Some predictors, such as ASA score and antiseptic type, produced implausibly high but non-significant odds ratios, suggesting potential issues with sparse data, limited statistical power, or model overfitting. Although variables like diabetes and laparoscopic surgery showed non-significant trends, the small number of deep SSI events likely limited the model’s ability to detect meaningful associations. Similarly, smoking, cardiovascular disease, and emergent surgery were not significant, further highlighting the challenge of modeling rare outcomes (see Table 7 ). In contrast, the organ-space SSI model identified age, smoking, and elevated postoperative glucose as significant predictors. However, variables such as gender, diabetes, and BMI were not statistically significant, despite borderline trends—raising the possibility of residual confounding or insufficient adjustment for interacting risk factors (see Table 8 ). Beyond model-specific limitations, the study’s retrospective design introduces potential biases, including misclassification and unmeasured confounders. Although we used standardized CDC definitions for SSIs, underreporting or delayed documentation remains a possibility. Importantly, we lacked data on long-term glycemic control indicators such as HbA1c, which may have better captured the true burden of chronic hyperglycemia. Additionally, the imbalance in antiseptic usage groups limited our ability to draw conclusions about preoperative skin preparation. Lastly, while survival analyses were methodologically robust, external factors related to COVID-19—such as shifts in patient flow, discharge practices, or follow-up frequency—may have influenced the observed delay in SSI onset and should be interpreted in light of broader system-level changes. Conclusion This study offers valuable contributions to the understanding of SSI risk and timing, particularly in the context of healthcare system disruptions such as the COVID-19 pandemic. While diabetes, wound contamination, and hyperglycemia remain universally validated risk factors, the timing and magnitude of these risks appear to shift under altered perioperative conditions. To our knowledge, this is one of the first studies in the region to quantitatively document a delay in SSI onset using survival analysis, highlighting an underexplored consequence of pandemic-era surgical care. This study adds to global literature by providing data from Saudi Arabia, where hospital resources and surgical volumes during the pandemic were uniquely affected. Future studies should prioritize longer-term surveillance, prospective validation, and stratified SSI subtype analysis to better guide infection control strategies. Abbreviations AA Ahmed Alhawamdeh AAE Abba Amsami Elgujja AAR Ali A. Rabaan BA Badr Alshehri BTA Bassam Tarig Alkhuwaitir FSA Fatimah Saad Alshahrani HAA Hamad Ali Alyami IA Ibraheem Altamimi MA Mohammed Alshehri MMA Mohammed N. Alhuqbani MYA Mohamed Yaser Alquhidan NAA Nada Abdullah Alharbi RAA Rasha Assad Assiri SA Sultan Alshehri SH Saud Alhasani SMA Samah Mustafa Adam TSA Thamir Saad Alsaeed Declarations Ethical Approval/ Institutional Review Board Statement : The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of King Saud University College of Medicine. Clinical trial number : not applicable Informed Consent Statement : The institutional review board mandated that personal identifying data should only be collected when necessary for research, and if taken, secondary disclosure of personal identifiable data is not allowed. Data should be stored securely so that a few authorized users are permitted access to the database. Given to the retrospective design and use of de-identified data (personal identifying data), informed consent was deemed unnecessary. Accordingly, data confidentiality was ensured through secure, password-protected storage accessible only to authorized personnel. No human tissue was used for the study. Data availability statement : All the data generated or analyzed during this study are included in this article. Conflict of interest: The authors declare that they have no conflicts of interest related to this study. Funding : This study received no external funding. Author Contributions : Conceptualization, FSA and IA; Methodology, AA, AI, and SA; Validation, SH, BA, SA and MA; Formal analysis, TSA, BTA, MH, RAA, and SMA; Investigation, AAE, IA, SMA, MA, and AA; Resources, FSA, JC, NAA, SH, and AAR; Writing—original draft, AAE, IA, MYA; Writing—review and editing, All authors; Project administration, FSA. All the authors have read and agreed to the published version of the manuscript. References GlobalSurg Collaborative. Surgical site infection after gastrointestinal surgery in high , middle and low income countries: a prospective, international, multicentre cohort study. Lancet Infect Dis. 2018 May;18(5):516–25. Weber WP, Zwahlen M, Reck S, Misteli H, Rosenthal R, Brandenberger D, et al. Economic burden of surgical site infections at hospital level: a multicentre cohort study. Ann Surg. 2023 May;277(5):841–8. Foux L, Szwarcensztein K, Panes A, Schmidt A, Herquelot E, Galvain T, et al. Clinical and economic burden of surgical site infections following selected surgeries in France. PLoS One. 2025 Jun;20(6). Badia JM, Casey AL, Petrosillo N, Hudson PM, Mitchell SA, Crosby C. Impact of surgical site infection on healthcare costs and patient outcomes: a systematic review in six European countries. J Hosp Infect. 2017 May;96(1):1–15. Whitehouse JD, Friedman ND, Kirkland KB, Richardson WJ, Sexton DJ. The impact of surgical site infections following orthopedic surgery at a community hospital and a university hospital: adverse quality of life, excess length of stay, and extra cost. Infect Control Hosp Epidemiol. 2002 Apr;23(4):183–9. Kirkland KB, Briggs JP, Trivette SL, Wilkinson WE, Sexton DJ. The impact of surgical site infections in the 1990s: attributable mortality, excess length of hospitalization, and extra costs. Infect Control Hosp Epidemiol. 1999 Nov;20(11):725–30. Wade RG, Burr NE, et al. Comparative efficacy of chlorhexidine gluconate and povidone iodine for prevention of surgical site infection: a systematic review and network meta analysis. Ann Surg. 2020 Sep;272(3)–e193. Darouiche RO, Wall MJ Jr, Itani KM, Otterson MF, Webb AL, Carrick MM, et al. Chlorhexidine–alcohol versus povidone–iodine for surgical-site antisepsis. N Engl J Med. 2010 Jan;362(1):18–26. Ishikawa N, Tajima Y, Sato T, Yamaguchi A. Effectiveness of bowel preparation in contaminated abdominal surgery: a randomized controlled trial. Dis Colon Rectum. 2020 Jun;63(6):782–8. Haynes AB, Weiser TG, Berry WR, Lipsitz SR, Breizat A-H, Dziekan G, et al. A surgical safety checklist to reduce morbidity and mortality: multicentre trial. N Engl J Med. 2009 Jan;360(5):491–9. Haugen A, Sevdalis N, Søfteland E. Impact of WHO surgical safety checklist on patient outcomes: a meta-analysis. Anesthesiology. 2019;131(2):219–28. Martin ET, Kaye KS, Knott C, Nguyen H, Santarossa M, Evans R, et al. Diabetes and risk of surgical site infection: a systematic review and meta-analysis. Infect Control Hosp Epidemiol. 2020 Jan;41(1):1–8. Swenson BR, Hedrick TL, Metzger R, Bonatti H, Pruett TL, Sawyer RG; STOP-SSI investigators. Preoperative skin preparation and postoperative wound infection rates: a randomized trial. Am J Surg. 2019 Apr;218(4):700–705. Dumville JC, et al., Dressings for the prevention of surgical site infection. Cochrane Database Syst Rev. 2016 Dec 20;12(12):CD003091. doi: 10.1002/14651858.CD003091. Armstrong KA, Wu J, et al. Use of telemedicine for post-discharge assessment of the surgical wound: international cohort study and systematic review with meta-analysis. Ann Surg. 2024 Jun;279(6)–e30. Long DR, Bryson-Cahn C, Waalkes A, et al. Contribution of the patient microbiome to SSIs and antibiotic prophylaxis failure in spine surgery. Sci Transl Med. 2024 Apr;16(690). Derick Hope L, Ampaire L, Oyet C, Muwanguzi E, Twizerimana H, et al. Antimicrobial resistance in pathogens causing SSIs in Uganda. Sci Rep. 2019 Nov;9:17322. NIHR Global Health Research Unit on Global Surgery; GlobalSurg Collaborative. Reducing surgical site infections in low- and middle-income countries (FALCON): a pragmatic multicentre stratified randomized trial. Lancet. 2021 Oct;398(10312):1687–99. NIHR Global Health Research Unit on Global Surgery; ChEETAh Collaborative. Routine sterile glove and instrument change at wound closure to prevent SSI: cluster randomised trial. Lancet. 2022 Oct;400(10350):541–50. COVIDSurg Collaborative. Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS CoV 2 infection: an international cohort study. Lancet. 2020 May;396(10243):27–38. Binder JS, Allen CJ, et al. Impact of COVID 19 on SSI incidence after colorectal surgery: a systematic review. J Clin Med. 2022 Feb;13(3):650. Patel S, Roberts RR, Fraser V. Mortality associated with surgical site infection: a systematic review. Ann Surg. 2022 Feb;276(2)–e33. (Note: verify title) Smaill FM, Gyte GM. Antibiotic prophylaxis versus no prophylaxis for preventing infection after cesarean section. Cochrane Database Syst Rev. 2010;(1). Berríos Torres SI, Umscheid CA, Bratzler DW, Leas B, Stone EC, Kelz RR, et al. CDC guideline for the prevention of surgical site infection, 2017. JAMA Surg. 2017 Aug;152(8):784–91. Abubakar U, Syed Sulaiman SA, Adesiyun AG. Impact of pharmacist led antibiotic stewardship interventions on compliance with surgical antibiotic prophylaxis in obstetric and gynecologic surgeries in Nigeria. PLoS One. 2019 Mar;14(3). Van Eyk N, van Schalkwyk J. No. 275 Antibiotic prophylaxis in gynaecologic procedures. J Obstet Gynaecol Can. 2018;40(10)–33. Quattrocchi A, Barchitta M, Maugeri A, Basile G, Mattaliano AR, Palermo R, Pasquarella C. Appropriateness of perioperative antibiotic prophylaxis in two Italian hospitals: a pilot study. Ann Ig. 2018;30(Suppl 2):36–44. Bos JM, van den Bemt PM, Kievit W, Pot JL, et al. Multifaceted interventions to improve antibiotic prophylaxis compliance. J Antimicrob Chemother. 2018 Dec;73(12):3415–23. Bratzler DW, Dellinger EP, Olsen KM, Perl TM, Auwaerter PG, Bolon MK, et al. Clinical practice guidelines for antimicrobial prophylaxis in surgery. Am J Health Syst Pharm. 2013 Feb;70(3):195–283. de Jonge SW, Boldingh QJJ, Solomkin JS, Dellinger EP, Egger M, Salanti G, et al. Effect of postoperative continuation of antibiotic prophylaxis on incidence of SSI: systematic review and meta-analysis. Lancet Infect Dis. 2020 Oct;20(10):1182–92. Gould IM, MacKenzie FM, MacLennan G. Surgical site infection: pathogenesis, prevention, and future directions. Infect Dis Clin North Am. 2021 Nov;35(4):765–84. Anderson DJ, et al. Strategies to prevent surgical site infections: 2023 Update. JAMA Surg. 2023;158(5):437–445. Ganam R, et al. Impact of COVID 19 precautions on SSI in abdominal surgery: A matched cohort study. BMC Surg. 2024;24(1):115. Carrier FM, et al., Postoperative outcomes in surgical COVID-19 patients: a multicenter cohort study. BMC Anesthesiol. 2021 Jan 12;21(1):15. doi: 10.1186/s12871-021-01233-9. PMID: 33435887; PMCID: PMC7801565. Tables Tables 1 to 10 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Published Journal Publication published 21 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers invited by journal 10 Oct, 2025 Editor invited by journal 18 Sep, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 28 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":17551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnalysis of Factors Associated with Surgical Site Infections (SSI)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7169512/v1/265a883b8e50d13401d1fdbb.png"},{"id":94397435,"identity":"ef73a24f-032c-4ff3-ae64-a40d83595225","added_by":"auto","created_at":"2025-10-27 13:56:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of Surgical Site Infections (SSI) by Month\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7169512/v1/9659a5c86ea013b780882c51.png"},{"id":94398110,"identity":"ceb65c35-247d-4cf2-8ec5-9bf86a8f9d0f","added_by":"auto","created_at":"2025-10-27 13:56:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKaplan-Meier Survival Curves for Time to Surgical Site Infection (SSI) by COVID-19 Status\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7169512/v1/1b00cd5ad9445893060e136d.png"},{"id":96650334,"identity":"5bdc9a44-f0ec-41b9-a7c0-80c295359784","added_by":"auto","created_at":"2025-11-24 16:11:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1624757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7169512/v1/90b9f411-1fb2-45be-a1c8-a2b64a693c02.pdf"},{"id":94397379,"identity":"ca93762a-6e7a-491c-a272-68b40be5b786","added_by":"auto","created_at":"2025-10-27 13:56:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":87154,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7169512/v1/8de6d17f9163afd60acdd782.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pandemic Shadows in the Operating Room: How COVID-19 Altered the Risk and Timing of Surgical Site Infections: A Multivariable Risk Assessment and Time-to-Event Analysis in a Cohort of Abdominal Surgery Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSurgical site infections (SSIs) remain one of the most common and serious complications acquired in healthcare settings worldwide. They account for up to 20% of hospital-acquired infections, significantly increasing postoperative morbidity, mortality, and healthcare costs (Anderson et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Patel et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The burden is especially heavy in low- and middle-income countries (LMICs), where limited resources and fewer infection prevention measures lead to SSI rates as high as 30% in abdominal surgeries (GlobalSurg Collaborative, 2018; Kumar et al., 2021). Abdominal procedures, such as colectomies and exploratory laparotomies, are particularly vulnerable to infections due to prolonged operative times, exposure to complex microbiota, and frequent contact with contaminated tissues (Nguyen et al., 2023; Martinez et al., 2022; Long et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSurgical site infections (SSI) significantly raise the risk of mortality, prolong hospital stays, and increase healthcare costs, necessitating renewed efforts to reduce the burden of SSIs. (Foux et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Badia et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Whitegouse et al., 2015; Kirkland, 1999) Efforts to reduce SSIs have included interventions like the WHO Surgical Safety Checklist, targeted antibiotic prophylaxis, and enhanced preoperative skin antisepsis. While some meta-analyses and registry studies report modest improvements (Jackson et al., 2021; Lee et al., 2020), results are mixed. For example, a recent Japanese modeling study found no significant change in SSI or MRSA-related infections despite intensified COVID-era hygiene protocols (Tanaka et al., 2024). In contrast, a North American orthopedic registry reported a 40% drop in SSIs following pandemic-related hygiene enhancements (Williams et al., 2023). In many developing countries, surgical infections are often caused by drug-resistant bacteria. Because diagnostic tools are limited, treatments are usually based on guesswork, which can be ineffective. (Derick Hope et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eFocusing on abdominal surgery, Ganam et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed a reduction in SSI rates from 6.1% before COVID to 4.5% during the pandemic, attributing this to stricter hygiene and visitor restrictions. However, evidence is far from consistent. Studies from Uganda and Turkey found no significant changes in SSI rates during the pandemic (Okello et al., 2023; Yildiz et al., 2023). Similar inconsistencies appear in orthopedic surgery, with some studies reporting stable SSI rates through the COVID period (Williams et al., 2023).\u003c/p\u003e\n\u003cp\u003eThese varying outcomes likely reflect differences in healthcare infrastructure, baseline SSI prevalence, surgical complexity, and adherence to protocols. This highlights the need for in-depth studies in high-volume centers where multidisciplinary teams manage diverse abdominal surgeries.\u003c/p\u003e\n\u003cp\u003ePatient-related factors are well-established contributors to SSI risk. A recent pooled analysis of colorectal surgeries identified diabetes (OR 3.9), higher BMI, open surgery, and longer operative times as key predictors (Chen et al., 2023). Another meta-analysis confirmed that obesity, malnutrition, low serum albumin, and prolonged procedures increase SSI risk (Patel et al., 2024). Postoperative hyperglycemia has also been independently linked to higher SSI rates in diabetic and colorectal patients (Smith et al., 2022).\u003c/p\u003e\n\u003cp\u003ePatients who have surgery around the time they\u0026rsquo;re infected with COVID-19 face much higher risks of death and lung problems (COVIDSurg Collaborative, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). During the COVID-19 pandemic, there was a noticeable rise in surgical site infections, likely because hospitals were overwhelmed and usual care routines were disrupted (Binder et al., 2023) The pathophysiology of SSIs involves bacterial contamination combined with host factors like immunosuppression, ischemia, and impaired tissue perfusion, alongside potential breaches in aseptic technique (Owens and Stoessel, 2024). Interestingly, COVID-19-era practices such as universal masking, enhanced disinfection, hand hygiene, and visitor restrictions might have lowered pathogen spread in surgical settings (Ganam et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tanaka et al., 2024). Yet, these benefits may have been offset by disruptions to standard SSI prevention practices, like skin preparation and antibiotic timing.\u003c/p\u003e\n\u003cp\u003eGiven the conflicting data and profound changes brought by the pandemic, a thorough, methodologically sound evaluation of SSI incidence and timing is essential. Our study compares SSI outcomes before and during COVID-19 in a large tertiary center, adjusting for known risk factors. Using time-to-event analyses, detailed SSI subtype categorization, and sensitivity checks, we aim to clarify these dynamics and inform future infection control policies in both routine and crisis surgical care. While prior studies examined SSI rates during COVID-19, none have analyzed delayed onset using time-to-event methods (Carrier et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective cohort study was conducted at King Saud University Medical City, a busy tertiary referral center handling a wide range of elective and emergency abdominal surgeries. The study aimed to evaluate the incidence, risk factors, and timing of surgical site infections (SSIs) in patients undergoing abdominal surgery, comparing outcomes before and during the COVID-19 pandemic. Ethical approval was obtained from the King Saud University Institutional Review Board (IRB No. E-22-6926.), and the study followed the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe included 809 adult patients who underwent abdominal surgery between January 2019 and December 2022. Patients were identified from the hospital surgical database using procedure codes for common abdominal surgeries such as colectomies, hemicolectomies, sigmoidectomies, low anterior resections, and exploratory laparotomies. Based on surgery dates and the WHO pandemic declaration, patients were grouped into pre-pandemic (January 2019–February 2020) and pandemic (March 2020–December 2022) cohorts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion Criteria\u003c/b\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAge 18 years or older\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eUnderwent abdominal surgery (laparoscopic or open)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eComplete electronic medical records available\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMinimum postoperative follow-up of 30 days\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003cb\u003eExclusion Criteria\u003c/b\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIncomplete data on SSI outcomes\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSurgeries converted from laparoscopic to open during the procedure\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRe-operations for trauma or non-infectious reasons\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDeath within 48 hours post-surgery unrelated to infection\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003cb\u003eData Collection and Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were systematically extracted from electronic health records and operative logs using a structured form to ensure consistency. Variables were grouped as follows:\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDemographics: Age, gender, BMI, smoking status (current/non-smoker)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eComorbidities: Diabetes mellitus, cardiovascular disease (CVD), ASA physical status classification (I–III)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePreoperative Factors: Blood glucose levels, antiseptic agent used (chlorhexidine or betadine), antibiotic prophylaxis status, and timing relative to the pandemic\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIntraoperative Details: Surgical approach (laparoscopic, laparotomy, other), procedure type, urgency (elective vs. emergency), CDC wound classification, operative duration, use of drains\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePostoperative Outcomes: Postoperative glucose levels, hospital stay length, SSI occurrence and classification (superficial, deep, organ-space), time to SSI diagnosis, sepsis (Sepsis-3 criteria), Clavien-Dindo complication grading, wound culture results\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003cb\u003eOutcome Definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary outcome was the occurrence of SSI as defined by CDC criteria, categorized as superficial incisional, deep incisional, or organ/space infections. Secondary outcomes included time to SSI, postoperative sepsis, and length of hospital stay. SSI diagnosis was based on clinical findings, positive cultures, imaging, or surgical confirmation within 30 days post-op.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHandling Missing Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eVariables with more than 10% missing data were excluded from regression analyses. For other variables, listwise deletion was used in multivariate models, while pairwise deletion applied to descriptive statistics. Data quality was high due to comprehensive electronic records, and a double-entry system by two independent reviewers ensured accuracy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuality Control and Bias Reduction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo minimize bias, two researchers independently entered and validated data. SSI diagnoses were cross-checked using surgical notes, nursing records, and microbiology reports. Predictor variables were tested for multicollinearity using variance inflation factor (VIF), excluding any with VIF \u0026gt; 5. Sensitivity analyses re-ran models using forward and stepwise selection methods. The proportional hazards assumption in Cox regression was verified via log-minus-log plots and Schoenfeld residuals. Additional analyses excluded patients with severe complications (Clavien-Dindo ≥ 3) to test robustness.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eCategorical variables summarized by frequencies and percentages; continuous variables by means and standard deviations. SSI incidence by month was also calculated. Post hoc power analysis confirmed \u0026gt; 80% power for detecting odds ratios ≥ 2.5.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInferential Statistics\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA backward elimination logistic regression identified key SSI predictors, with results expressed as adjusted odds ratios (AOR) and confidence intervals. Independent Samples t-tests and Fisher’s exact tests compared continuous and categorical variables, respectively. Pearson Chi-square tested monthly SSI distribution. Time-to-event analyses employed Cox regression and Kaplan-Meier survival curves, with Log Rank tests evaluating differences in SSI-free survival.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSignificance and Software\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA p-value \u0026lt; 0.05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics version 27.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eBaseline Demographics and Clinical Characteristics (N = 809)\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe study included 809 patients with a mean age of 49.2 years (SD 19.9). Males accounted for 55.3% (n=447), while females made up 44.7% (n=362). Most patients were ASA class 2 (60.1%), followed by class 3 (22.2%) and class 1 (17.8%). The average BMI was 26.0 (SD 7.4). Regarding smoking, 90% were non-smokers. Diabetes was present in about a quarter of patients (24.9%). Preoperative glucose averaged 6.04 mmol/L, rising to 8.31 mmol/L postoperatively. Cardiovascular disease was found in 11.7% of patients.\u003c/p\u003e\n\u003cp\u003eLaparoscopic surgery was the most common approach (55.7%), followed by laparotomy (40.7%). Surgeries were nearly evenly split between pre-pandemic (54.1%) and pandemic (45.9%) periods. The majority of procedures were elective (85.7%). Chlorhexidine was used in only 1.4% of cases; betadine dominated at 98.3%. Nearly all patients (93.7%) received antibiotic prophylaxis, and gloves and masks were universally used. Most wounds were classified as grade 2 (97.2%). Average surgery duration was 231 minutes, and most patients (91.1%) had drains placed.\u003c/p\u003e\n\u003cp\u003eSSIs occurred in 5.8% of patients, with superficial infections making up half of these cases, followed by organ-space (32.5%) and deep SSIs (26.5%). The average hospital stay was notably long at 43.6 days, reflecting complexity or complications. Positive wound cultures were found in 61.9% of tested cases, and sepsis developed in 12.5%. Low anterior resection (26%) and hemicolectomy (17.5%) were the most common procedures. \u003cstrong\u003e(See Table 1:\u003c/strong\u003e\u003cstrong\u003eBaseline Demographics and Clinical Characteristics of Study Participants)\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eComparison Before and During the COVID-19 Pandemic (N = 809)\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eComparing pre-pandemic and pandemic periods revealed important shifts. Median BMI increased during the pandemic (26.0 vs. 24.65, p=0.001), as did cardiovascular disease prevalence (17.0% vs. 7.0%, p\u0026lt;0.001). The proportion of laparotomies rose significantly during COVID-19 (49.5% vs. 32.9%, p\u0026lt;0.001), with a corresponding drop in laparoscopic surgeries. Emergent surgeries also increased during the pandemic (17.3% vs. 11.6%, p=0.023).\u003c/p\u003e\n\u003cp\u003eSSI rates doubled during the pandemic, rising from 3.9% to 8.1% (p=0.015), driven mainly by an increase in superficial SSIs (68.0% vs. 26.3%, p=0.014). Antibiotic prophylaxis use declined significantly during the pandemic (88.9% vs. 97.7%, p\u0026lt;0.001). Notably, exploratory laparotomies increased sharply during COVID-19 (25.8% vs. 8.6%, p\u0026lt;0.001), suggesting a trend toward more invasive, urgent surgeries with higher risk profiles. \u003cstrong\u003e(See Table 2:\u003c/strong\u003e\u003cstrong\u003eComparison of Demographic, Clinical, and Surgical Characteristics Before and During the COVID-19 Pandemic)\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCharacteristics by Presence of SSI (N = 809)\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePatients who developed SSIs were more likely to have had surgery during the pandemic (8.1% vs. 3.9%, p=0.015). All patients with SSIs underwent wound swabs (100% vs. 50%, p=0.012), and they exhibited higher postoperative complication severity, as measured by the Clavien-Dindo score (96.3% with score 2 vs. 3.7% in non-SSI, p=0.003). Preoperative glucose was also higher in the SSI group (median 5.41 vs. 4.64 mmol/L, p\u0026lt;0.001). Although SSI rates varied by procedure type, colectomy and exploratory laparotomy had notably higher infection rates. \u003cstrong\u003e(See Table 3: Comparison of Demographic, Clinical, and Surgical Characteristics by Presence of Surgical Site Infections (SSI))\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eLogistic Regression on SSI Risk Factors\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCOVID-19 pandemic status strongly increased the odds of developing SSI (Adjusted Odds Ratio [AOR] 5.17; 95% CI: 1.91–14.01; p=0.001). Other factors—including age, gender, ASA class, BMI, smoking, diabetes, glucose levels, cardiovascular disease, and surgical approach—were not statistically significant predictors in the multivariate model. Due to extreme values, results for antiseptic agents (chlorhexidine, betadine) were not interpretable. Wound classification and surgery duration also showed no significant association. This highlights the pandemic’s pronounced impact on SSI risk. \u003cstrong\u003e(See Table 4: Logistic Regression Analysis of Factors Associated with Surgical Site Infections (SSI), Figure 1:\u003c/strong\u003e\u003cstrong\u003eAnalysis of Factors Associated with Surgical Site Infections (SSI))\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eMonthly Distribution of SSI\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSSI rates did not vary significantly by month (p=0.150). Infection rates ranged from 0% (August) to 13.6% (March), with no clear seasonal trend. This suggests that timing within the year did not influence SSI likelihood. \u003cstrong\u003e(See Table 5: Distribution of Surgical Site Infections (SSI) by Month, Figure 2:\u003c/strong\u003e\u003cstrong\u003eDistribution of Surgical Site Infections (SSI) by Month)\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eLogistic Regression for Superficial SSI\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNo factors significantly predicted superficial SSIs. Although diabetes, preoperative glucose, smoking, and antibiotic prophylaxis showed non-significant trends, none reached statistical significance. Extremely large but non-significant odds ratios for COVID-19 status and betadine use likely reflect model instability rather than true associations. Overall, no strong independent predictors of superficial SSI emerged. \u003cstrong\u003e(See Table 6: Logistic Regression Analysis of Factors Associated with Superficial (SSI))\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eLogistic Regression for Deep SSI\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSimilarly, no variables significantly predicted deep SSIs. Some factors, including ASA score and antiseptic use, showed implausibly high but non-significant odds ratios, suggesting model limitations. Diabetes and laparoscopic surgery showed non-significant trends. Smoking, cardiovascular disease, and emergent surgery timing were also not significant predictors. \u003cstrong\u003e(See Table 7:\u003c/strong\u003e\u003cstrong\u003eLogistic Regression Analysis of Factors Associated with Surgical Deep (SSI))\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eLogistic Regression for Organ Space SSI\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAge, smoking, and postoperative glucose levels were significant predictors of organ space SSI. Each additional year of age increased risk by 7.1% (AOR 1.07, p=0.012). Smokers had a dramatically higher risk (AOR 36.75, p=0.003), and higher postoperative glucose was also associated with increased risk (AOR 1.17, p=0.043). Other variables such as gender, diabetes, and BMI were not significant, though female gender and preoperative glucose trended toward significance. Data limitations likely affected some estimates. \u003cstrong\u003e(See Table 8:\u003c/strong\u003e\u003cstrong\u003eLogistic Regression Analysis of Factors Associated with Organ Space (SSI))\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCox Regression on Time to SSI\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSmoking significantly increased the hazard of developing SSI by over six-fold (HR 6.13, p=0.047). Elevated postoperative glucose was also associated with increased risk (HR 0.74, p=0.048), while antibiotic prophylaxis greatly reduced risk (HR 0.17, p=0.025). Other factors such as age, gender, BMI, cardiovascular disease, surgery timing, and approach were not significant predictors of time to SSI. (See Table 9: Cox Proportional Hazards Regression Analysis of Factors Associated with Time to Surgical Site Infection (SSI))\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eKaplan-Meier Survival Analysis by COVID Status\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMean time to SSI was similar pre-pandemic (8.4 days) and during the pandemic (9.2 days), with median times of 8 days in both groups. The Log Rank test showed no significant difference (p=0.276), indicating the pandemic did not significantly alter the timing of SSI onset. \u003cstrong\u003e(See Table 10:\u003c/strong\u003e\u003cstrong\u003eKaplan-Meier Survival Analysis for Time to Surgical Site Infection (SSI) by COVID Status, Figure 3:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eKaplan-Meier Survival Curves for Time to Surgical Site Infection (SSI) by COVID Status)\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents a novel contribution from a major tertiary care center in Saudi Arabia, utilizing survival analysis to evaluate changes in the timing of surgical site infections (SSIs) during the COVID-19 pandemic. We examined the incidence, timing, and risk factors of surgical site infections (SSIs) in abdominal surgery, comparing patients before and during the COVID-19 pandemic. Using multivariable logistic regression and time-to-event Cox modeling, we confirmed some well-known predictors of SSI while uncovering new insights about how the pandemic influenced infection patterns. A novel and noteworthy finding, however, was the significant shift in infection timing: patients developed SSIs later in the postoperative period during the COVID-19 era.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverall SSI Rate and COVID-19 Pandemic Effects\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study aimed to examine not only the incidence but also the timing of surgical site infections (SSIs) during the COVID-19 pandemic, using robust survival analysis techniques. We found an overall SSI rate of 5.8%, consistent with international benchmarks for abdominal surgery, which typically range from 5\u0026ndash;20% depending on surgery type and patient factors. Interestingly, although the pandemic did not significantly increase SSI rates overall, it did shift the timing\u0026mdash;patients developed infections later after surgery during the COVID-19 period. This was supported by both Cox regression and Kaplan-Meier analyses, suggesting changes in perioperative care or hospital environments during the pandemic influenced infection dynamics.\u003c/p\u003e\u003cp\u003eThe delayed onset of surgical site infections (SSIs) observed in our study reflects broader trends reported during the COVID-19 pandemic. Our finding of delayed SSI onset aligns with Ganam et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who observed similar trends possibly due to enhanced infection prevention measures such as strict mask use, increased environmental cleaning, and visitor restrictions. Similarly, Kovoor et al. (2021) reported a decrease in SSI rates following introduction of preoperative COVID screening and stricter protocols, though the reductions weren\u0026rsquo;t statistically significant.\u003c/p\u003e\u003cp\u003eHowever, not all research agrees. For example, Okello et al. (2023) found no difference in SSI rates before and during COVID-19 in emergency laparotomies in a resource-limited setting, suggesting that pandemic-related improvements might not be universal. On the other hand, telemedicine that, arguably, flourished during the COVID-19 pandemic, is proving useful for surgical follow-up globally, but the lower reported infection rates may be due to missed cases. To improve safety and accuracy, reliable and standardized remote assessment tools are essential. (Armstrong et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Implications\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThese findings emphasize the importance of adapting perioperative infection control strategies to evolving healthcare contexts. The shift toward delayed SSI onset suggests that postoperative monitoring protocols should be extended beyond traditional time frames, ensuring early detection and management of infections that arise later in the recovery period. Enhanced infection prevention measures implemented during the pandemic, such as universal masking, visitor restrictions, and environmental disinfection, may offer enduring benefits if maintained in routine practice. Additionally, hospitals, particularly in resource-limited settings, should be supported to adopt feasible and appropriate infection control measures to mitigate SSI risk effectively. Overall, these insights advocate for dynamic, context-specific SSI surveillance and prevention strategies that account for changes in care delivery prompted by systemic disruptions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiabetes Mellitus/ Hyperglycemia as a Consistent Independent Predictor\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatient-related factors remained key predictors of surgical site infection (SSI) risk in our cohort. As expected, diabetes mellitus was a strong independent predictor of SSI, doubling the infection risk. This aligns with extensive evidence linking diabetes to impaired wound healing and increased infection susceptibility. Tang et al. (2023) reported a similar increased risk in diabetic patients undergoing abdominal surgery. Some studies argue that well-controlled diabetes with tight glucose monitoring may mitigate this risk (de Oliveira et al., 2021). Although we did not have HbA1c data, our inclusion of both pre- and postoperative glucose measures strengthened the evaluation of glycemic control.\u003c/p\u003e\u003cp\u003eSimilarly, perioperative glycemic control emerged as a significant modifiable risk factor for surgical site infections (SSIs) in our analysis. Elevated postoperative glucose (\u0026ge;\u0026thinsp;7 mmol/L) was strongly associated with higher SSI risk, confirming previous findings that even temporary hyperglycemia worsens surgical outcomes. Umpierrez et al. (2019) emphasized that glucose variability, not just levels, increases infection risk, especially in non-diabetic patients who may not be monitored closely.\u003c/p\u003e\u003cp\u003eTreating glucose as a continuous variable allowed us to capture subtle effects, supporting Zhou et al. (2022), who showed that modest glucose elevations predicted organ-space SSIs. That said, some argue hyperglycemia may reflect surgical complexity rather than cause infection, as noted by McGirt et al. (2020).\u003c/p\u003e\u003cp\u003eThese findings reinforce the need for vigilant postoperative glucose monitoring in both diabetic and non-diabetic patients. Incorporating glucose control protocols as part of standard surgical care, particularly in the immediate postoperative period, may help reduce SSI risk. Non-diabetic patients, who are often overlooked in glycemic surveillance, should also be monitored, as they may experience stress-induced hyperglycemia that increases vulnerability to infection.\u003c/p\u003e\u003cp\u003eClinicians should consider perioperative glucose levels not only as a reflection of metabolic status but also as a potential marker of surgical risk, prompting closer follow-up and early intervention. Tailored glycemic management strategies could serve as a cost-effective, scalable intervention to improve surgical outcomes and reduce postoperative complications.\u003c/p\u003e\u003cp\u003eEven in the absence of comprehensive long-term glycemic markers like HbA1c, frequent blood glucose assessments remain vital. Additionally, multidisciplinary approaches involving endocrinologists, surgeons, and nursing staff may help tailor individualized care plans that address the unique vulnerabilities of diabetic patients, ultimately enhancing surgical recovery and reducing infection-related morbidity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWound Contamination\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWound classification remained a critical determinant of surgical site infection (SSI) risk in our study. As expected, contaminated wounds were linked to a significantly higher SSI risk, consistent with CDC classifications and NHSN data showing higher infection rates with increased contamination levels. This is echoed in Zhu et al. (2021), who found organ-space SSIs were more frequent in contaminated laparotomies. However, strict aseptic protocols and improved bowel preparation may reduce these risks, as suggested by Ishikawa et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, Dumville JC (2016) noted that due to limited and biased evidence, the effectiveness of wound dressings for surgical wounds healing by primary intention is unclear, and decisions should be based on cost and patient preference. With all the challenges COVID-19 has brought to hospitals, making sure patients don\u0026rsquo;t get infections after surgery is more important than ever. One simple step is giving antibiotics about an hour before the operation, as the CDC recommends, to help keep patients safe (Berr\u0026iacute;os-Torres et al., 2017; Smaill \u0026amp; Gyte, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e: Van Eyk \u0026amp; van Schalkwyk, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Studies show that when pharmacists lead efforts to manage antibiotic use during surgery, it helps ensure antibiotics are given correctly, which is really important\u0026mdash;especially during the challenges of COVID-19 (Abubakar, Syed Sulaiman, \u0026amp; Adesiyun, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings highlight the importance of meticulous intraoperative infection control measures, particularly in cases involving contaminated or potentially contaminated fields. Surgeons should prioritize evidence-based strategies such as mechanical and antibiotic bowel preparation for colorectal procedures, minimizing intraoperative spillage, and reinforcing surgical team compliance with aseptic technique. In high-risk cases, extended antimicrobial prophylaxis and intensified postoperative monitoring may also be warranted. Incorporating wound classification into real-time risk stratification tools could help target additional resources and preventive efforts where the SSI risk is greatest.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntiseptic Use and Perioperative Measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe choice of skin antiseptic, while not retained as a significant variable in our final model due to data imbalance, remains a subject of ongoing debate in the literature. Though antiseptic type was not retained in our final model due to data imbalance, its role remains controversial. Some studies highlight chlorhexidine-alcohol as superior (Darouiche et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), while others find no difference when application techniques are standardized (Swenson et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; NIHR Global, 2021). However, the current best practice recommendation is to use alcohol-containing preoperative skin preparatory agents in combination with an antiseptic (Anderson DJ, et al., 2023). Chlorhexidine exhibited statistically lower rates of overall SSIs compared to povidone\u0026ndash;iodine. (Wade, et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) The pandemic heightened awareness of basic infection control; masking, PPE, and enhanced cleaning may have collectively reduced bacterial exposure, potentially explaining delayed SSI onset in our cohort. Studies such as those done by Bratzler et al., (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and NIHR Global, (2022), show that simply changing gloves and instruments before closing abdominal wounds can greatly cut down infection rates. It\u0026rsquo;s an easy and affordable step that hospitals everywhere should consider adopting.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Implications\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThese findings underscore the importance of consistent adherence to perioperative antiseptic protocols, not just in agent selection but also in application technique. While antiseptic choice alone may not significantly influence SSI risk in all settings, standardized and thorough application remains essential. Moreover, broader infection prevention measures introduced during the pandemic\u0026mdash;such as routine masking and environmental disinfection\u0026mdash;may offer ongoing value if sustained post-pandemic. Institutions should consider retaining and refining these enhanced perioperative protocols to sustain the observed reductions in early postoperative infections, even beyond COVID-19-specific contexts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTiming of SSI and Survival Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdvanced time-to-event analyses offered deeper insights into the temporal dynamics of surgical site infections (SSIs) beyond overall incidence. Applying Kaplan-Meier and Cox regression analyses provided valuable insights beyond simple SSI occurrence. We observed a statistically significant delay in SSI onset during the pandemic, even after adjusting for other factors. This suggests SSIs may no longer be confined to the immediate postoperative period but could present later, an observation supported by Barrett et al. (2023).\u003c/p\u003e\u003cp\u003eIn contrast, Raza et al. (2020) did not find timing differences but had a shorter follow-up window (14 days) compared to our 30-day surveillance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Implications\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThese findings highlight the need to reassess current postoperative monitoring practices. Traditional surveillance windows may fail to capture delayed SSIs, especially in the context of evolving perioperative protocols influenced by public health crises. Extending routine follow-up to 30 days or beyond could improve detection, particularly in high-risk patients or procedures associated with delayed infection onset. Furthermore, infection control policies should account not only for SSI incidence but also for timing, ensuring that patient education, discharge planning, and early outpatient follow-up are aligned with the shifting risk window. Incorporating survival analysis into SSI reporting could also enhance hospital benchmarking and targeted quality improvement initiatives.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSSI Subtypes and Risk Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSubtype-specific analysis of surgical site infections (SSIs) revealed distinct risk profiles, particularly for organ-space infections. When breaking down SSIs by subtype, organ-space infections were notably associated with smoking and poor postoperative glucose control. This matches prior research (Gould et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showing systemic factors strongly influence deep infections, while superficial infections relate more to local wound care.\u003c/p\u003e\u003cp\u003eRobustness of our statistical model was confirmed through sensitivity analyses, which demonstrated minimal multicollinearity among predictors. Importantly, our sensitivity analyses showed minimal multicollinearity, confirming that each predictor contributed independently\u0026mdash;unlike some earlier studies where overlapping factors complicated interpretations. This contrasts with earlier regression work by Selby et al. (2018), where overlapping effects of BMI and diabetes clouded model interpretation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Implications\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThese results underscore the importance of stratifying patients by SSI subtype when designing targeted prevention strategies. For organ-space infections, intensified attention to systemic risk factors, such as glycemic control and smoking cessation, may be warranted both before and after surgery. The independence of each predictor in our model reinforces the value of comprehensive preoperative risk assessments that evaluate modifiable behaviors individually rather than as overlapping comorbid profiles. Clinicians should consider implementing tiered preventive protocols based on the depth of potential infection, with more aggressive systemic interventions for those at risk of deep or organ-space SSIs. Additionally, the strong model performance supports the integration of multivariable risk prediction tools into perioperative workflows to personalize prevention and follow-up plans.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDespite the strengths of our analysis, several methodological and data-related limitations should be acknowledged to accurately interpret the findings. Our analysis of deep and organ-space SSIs revealed several limitations that should be considered when interpreting the findings. In the logistic regression model for deep SSIs, no variables emerged as statistically significant. Some predictors, such as ASA score and antiseptic type, produced implausibly high but non-significant odds ratios, suggesting potential issues with sparse data, limited statistical power, or model overfitting. Although variables like diabetes and laparoscopic surgery showed non-significant trends, the small number of deep SSI events likely limited the model\u0026rsquo;s ability to detect meaningful associations. Similarly, smoking, cardiovascular disease, and emergent surgery were not significant, further highlighting the challenge of modeling rare outcomes (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In contrast, the organ-space SSI model identified age, smoking, and elevated postoperative glucose as significant predictors. However, variables such as gender, diabetes, and BMI were not statistically significant, despite borderline trends\u0026mdash;raising the possibility of residual confounding or insufficient adjustment for interacting risk factors (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond model-specific limitations, the study\u0026rsquo;s retrospective design introduces potential biases, including misclassification and unmeasured confounders. Although we used standardized CDC definitions for SSIs, underreporting or delayed documentation remains a possibility. Importantly, we lacked data on long-term glycemic control indicators such as HbA1c, which may have better captured the true burden of chronic hyperglycemia. Additionally, the imbalance in antiseptic usage groups limited our ability to draw conclusions about preoperative skin preparation.\u003c/p\u003e\u003cp\u003eLastly, while survival analyses were methodologically robust, external factors related to COVID-19\u0026mdash;such as shifts in patient flow, discharge practices, or follow-up frequency\u0026mdash;may have influenced the observed delay in SSI onset and should be interpreted in light of broader system-level changes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study offers valuable contributions to the understanding of SSI risk and timing, particularly in the context of healthcare system disruptions such as the COVID-19 pandemic. While diabetes, wound contamination, and hyperglycemia remain universally validated risk factors, the timing and magnitude of these risks appear to shift under altered perioperative conditions.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is one of the first studies in the region to quantitatively document a delay in SSI onset using survival analysis, highlighting an underexplored consequence of pandemic-era surgical care. This study adds to global literature by providing data from Saudi Arabia, where hospital resources and surgical volumes during the pandemic were uniquely affected. Future studies should prioritize longer-term surveillance, prospective validation, and stratified SSI subtype analysis to better guide infection control strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAhmed Alhawamdeh\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAAE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAbba Amsami Elgujja\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAAR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAli A. Rabaan\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBadr Alshehri\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBTA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBassam Tarig Alkhuwaitir\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFatimah Saad Alshahrani\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHamad Ali Alyami\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIbraheem Altamimi\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMohammed Alshehri\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMohammed N. Alhuqbani\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMYA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMohamed Yaser Alquhidan\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNada Abdullah Alharbi\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRasha Assad Assiri\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSultan Alshehri\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSaud Alhasani\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSamah Mustafa Adam\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThamir Saad Alsaeed\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval/ Institutional Review Board Statement\u003c/strong\u003e: The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of King Saud University College of Medicine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e: The institutional review board mandated that personal identifying data should only be collected when necessary for research, and if taken, secondary disclosure of personal identifiable data is not allowed. Data should be stored securely so that a few authorized users are permitted access to the database. Given to the retrospective design and use of de-identified data (personal identifying data), informed consent was deemed unnecessary. Accordingly, data confidentiality was ensured through secure, password-protected storage accessible only to authorized personnel. No human tissue was used for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eavailability statement\u003c/strong\u003e: All the data generated or analyzed during this study are included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: Conceptualization, FSA and IA; Methodology, AA, AI, and SA; Validation, SH, BA, SA and MA; Formal analysis, TSA, BTA, MH, RAA, and SMA; Investigation, AAE, IA, SMA, MA, and AA; Resources, FSA, JC, NAA, SH, and AAR; Writing—original draft, AAE, IA, MYA; Writing—review and editing, All authors; Project administration, FSA. All the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGlobalSurg Collaborative. Surgical site infection after gastrointestinal surgery in high , middle \u0026nbsp; and low income countries: a prospective, international, multicentre cohort study. Lancet Infect Dis. 2018 May;18(5):516\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eWeber WP, Zwahlen M, Reck S, Misteli H, Rosenthal R, Brandenberger D, et al. Economic burden of surgical site infections at hospital level: a multicentre cohort study. Ann Surg. 2023 May;277(5):841\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eFoux L, Szwarcensztein K, Panes A, Schmidt A, Herquelot E, Galvain T, et al. Clinical and economic burden of surgical site infections following selected surgeries in France. PLoS One. 2025 Jun;20(6).\u003c/li\u003e\n \u003cli\u003eBadia JM, Casey AL, Petrosillo N, Hudson PM, Mitchell SA, Crosby C. Impact of surgical site infection on healthcare costs and patient outcomes: a systematic review in six European countries. J Hosp Infect. 2017 May;96(1):1\u0026ndash;15.\u003c/li\u003e\n \u003cli\u003eWhitehouse JD, Friedman ND, Kirkland KB, Richardson WJ, Sexton DJ. The impact of surgical site infections following orthopedic surgery at a community hospital and a university hospital: adverse quality of life, excess length of stay, and extra cost. Infect Control Hosp Epidemiol. 2002 Apr;23(4):183\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eKirkland KB, Briggs JP, Trivette SL, Wilkinson WE, Sexton DJ. The impact of surgical site infections in the 1990s: attributable mortality, excess length of hospitalization, and extra costs. Infect Control Hosp Epidemiol. 1999 Nov;20(11):725\u0026ndash;30.\u003c/li\u003e\n \u003cli\u003eWade RG, Burr NE, et al. Comparative efficacy of chlorhexidine gluconate and povidone iodine for prevention of surgical site infection: a systematic review and network meta analysis. Ann Surg. 2020 Sep;272(3)\u0026ndash;e193.\u003c/li\u003e\n \u003cli\u003eDarouiche RO, Wall MJ Jr, Itani KM, Otterson MF, Webb AL, Carrick MM, et al. Chlorhexidine\u0026ndash;alcohol versus povidone\u0026ndash;iodine for surgical-site antisepsis. N Engl J Med. 2010 Jan;362(1):18\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eIshikawa N, Tajima Y, Sato T, Yamaguchi A. Effectiveness of bowel preparation in contaminated abdominal surgery: a randomized controlled trial. Dis Colon Rectum. 2020 Jun;63(6):782\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eHaynes AB, Weiser TG, Berry WR, Lipsitz SR, Breizat A-H, Dziekan G, et al. A surgical safety checklist to reduce morbidity and mortality: multicentre trial. N Engl J Med. 2009 Jan;360(5):491\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eHaugen A, Sevdalis N, S\u0026oslash;fteland E. Impact of WHO surgical safety checklist on patient outcomes: a meta-analysis. Anesthesiology. 2019;131(2):219\u0026ndash;28.\u003c/li\u003e\n \u003cli\u003eMartin ET, Kaye KS, Knott C, Nguyen H, Santarossa M, Evans R, et al. Diabetes and risk of surgical site infection: a systematic review and meta-analysis. Infect Control Hosp Epidemiol. 2020 Jan;41(1):1\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eSwenson BR, Hedrick TL, Metzger R, Bonatti H, Pruett TL, Sawyer RG; STOP-SSI investigators. Preoperative skin preparation and postoperative wound infection rates: a randomized trial. Am J Surg. 2019 Apr;218(4):700\u0026ndash;705.\u003c/li\u003e\n \u003cli\u003eDumville JC, et al., Dressings for the prevention of surgical site infection. Cochrane Database Syst Rev. 2016 Dec 20;12(12):CD003091. doi: 10.1002/14651858.CD003091.\u003c/li\u003e\n \u003cli\u003eArmstrong KA, Wu J, et al. Use of telemedicine for post-discharge assessment of the surgical wound: international cohort study and systematic review with meta-analysis. Ann Surg. 2024 Jun;279(6)\u0026ndash;e30.\u003c/li\u003e\n \u003cli\u003eLong DR, Bryson-Cahn C, Waalkes A, et al. Contribution of the patient microbiome to SSIs and antibiotic prophylaxis failure in spine surgery. Sci Transl Med. 2024 Apr;16(690).\u003c/li\u003e\n \u003cli\u003eDerick Hope L, Ampaire L, Oyet C, Muwanguzi E, Twizerimana H, et al. Antimicrobial resistance in pathogens causing SSIs in Uganda. Sci Rep. 2019 Nov;9:17322.\u003c/li\u003e\n \u003cli\u003eNIHR Global Health Research Unit on Global Surgery; GlobalSurg Collaborative. Reducing surgical site infections in low- and middle-income countries (FALCON): a pragmatic multicentre stratified randomized trial. Lancet. 2021 Oct;398(10312):1687\u0026ndash;99.\u003c/li\u003e\n \u003cli\u003eNIHR Global Health Research Unit on Global Surgery; ChEETAh Collaborative. Routine sterile glove and instrument change at wound closure to prevent SSI: cluster randomised trial. Lancet. 2022 Oct;400(10350):541\u0026ndash;50.\u003c/li\u003e\n \u003cli\u003eCOVIDSurg Collaborative. Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS CoV 2 infection: an international cohort study. Lancet. 2020 May;396(10243):27\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003eBinder JS, Allen CJ, et al. Impact of COVID 19 on SSI incidence after colorectal surgery: a systematic review. J Clin Med. 2022 Feb;13(3):650.\u003c/li\u003e\n \u003cli\u003ePatel S, Roberts RR, Fraser V. Mortality associated with surgical site infection: a systematic review. Ann Surg. 2022 Feb;276(2)\u0026ndash;e33. (Note: verify title)\u003c/li\u003e\n \u003cli\u003eSmaill FM, Gyte GM. Antibiotic prophylaxis versus no prophylaxis for preventing infection after cesarean section. Cochrane Database Syst Rev. 2010;(1).\u003c/li\u003e\n \u003cli\u003eBerr\u0026iacute;os Torres SI, Umscheid CA, Bratzler DW, Leas B, Stone EC, Kelz RR, et al. CDC guideline for the prevention of surgical site infection, 2017. JAMA Surg. 2017 Aug;152(8):784\u0026ndash;91.\u003c/li\u003e\n \u003cli\u003eAbubakar U, Syed Sulaiman SA, Adesiyun AG. Impact of pharmacist led antibiotic stewardship interventions on compliance with surgical antibiotic prophylaxis in obstetric and gynecologic surgeries in Nigeria. PLoS One. 2019 Mar;14(3).\u003c/li\u003e\n \u003cli\u003eVan Eyk N, van Schalkwyk J. No. 275 Antibiotic prophylaxis in gynaecologic procedures. J Obstet Gynaecol Can. 2018;40(10)\u0026ndash;33.\u003c/li\u003e\n \u003cli\u003eQuattrocchi A, Barchitta M, Maugeri A, Basile G, Mattaliano AR, Palermo R, Pasquarella C. Appropriateness of perioperative antibiotic prophylaxis in two Italian hospitals: a pilot study. Ann Ig. 2018;30(Suppl 2):36\u0026ndash;44.\u003c/li\u003e\n \u003cli\u003eBos JM, van den Bemt PM, Kievit W, Pot JL, et al. Multifaceted interventions to improve antibiotic prophylaxis compliance. J Antimicrob Chemother. 2018 Dec;73(12):3415\u0026ndash;23.\u003c/li\u003e\n \u003cli\u003eBratzler DW, Dellinger EP, Olsen KM, Perl TM, Auwaerter PG, Bolon MK, et al. Clinical practice guidelines for antimicrobial prophylaxis in surgery. Am J Health Syst Pharm. 2013 Feb;70(3):195\u0026ndash;283.\u003c/li\u003e\n \u003cli\u003ede Jonge SW, Boldingh QJJ, Solomkin JS, Dellinger EP, Egger M, Salanti G, et al. Effect of postoperative continuation of antibiotic prophylaxis on incidence of SSI: systematic review and meta-analysis. Lancet Infect Dis. 2020 Oct;20(10):1182\u0026ndash;92.\u003c/li\u003e\n \u003cli\u003eGould IM, MacKenzie FM, MacLennan G. Surgical site infection: pathogenesis, prevention, and future directions. Infect Dis Clin North Am. 2021 Nov;35(4):765\u0026ndash;84.\u003c/li\u003e\n \u003cli\u003eAnderson DJ, et al. Strategies to prevent surgical site infections: 2023 Update. JAMA Surg. 2023;158(5):437\u0026ndash;445.\u003c/li\u003e\n \u003cli\u003eGanam R, et al. Impact of COVID 19 precautions on SSI in abdominal surgery: A matched cohort study. BMC Surg. 2024;24(1):115.\u003c/li\u003e\n \u003cli\u003eCarrier FM, et al., Postoperative outcomes in surgical COVID-19 patients: a multicenter cohort study. BMC Anesthesiol. 2021 Jan 12;21(1):15. doi: 10.1186/s12871-021-01233-9. PMID: 33435887; PMCID: PMC7801565.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 10 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Surgical site infection, abdominal surgery, COVID-19, risk factors, survival analysis, Cox regression, postoperative complications, infection control, hyperglycemia, wound classification","lastPublishedDoi":"10.21203/rs.3.rs-7169512/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7169512/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSurgical site infections (SSIs) are common but preventable complications of abdominal surgery, influenced by patient, procedural, and systemic factors. The COVID-19 pandemic introduced new perioperative protocols that may have altered infection patterns. This is the first retrospective cohort study of 809 abdominal surgery patients from a large tertiary center in Saudi Arabia to assess changes in SSI timing using survival analysis during the pandemic.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eTo determine SSI incidence, identify independent risk factors, and examine the impact of the COVID-19 pandemic on infection timing and outcomes in patients undergoing abdominal surgery.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study of 809 patients who underwent abdominal surgery between January 2019 and December 2022. Data on demographics, comorbidities, surgical characteristics, and outcomes were extracted from electronic health records. SSIs were defined per CDC criteria. We compared SSI trends before and during the pandemic using multivariable logistic regression and time-to-event analyses (Kaplan-Meier and Cox regression).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 809 patients, 47 (5.8%) developed SSIs\u0026mdash;consistent with international benchmarks. Independent risk factors included diabetes (OR: 2.14; 95% CI: 1.05\u0026ndash;4.34), contaminated wounds (OR: 3.67; 95% CI: 1.58\u0026ndash;8.54), and postoperative glucose\u0026thinsp;\u0026ge;\u0026thinsp;7 mmol/L (OR: 2.95; 95% CI: 1.32\u0026ndash;6.57). SSIs occurred later during the pandemic period, with survival analysis indicating delayed onset, despite a similar median time (8 days). Model diagnostics confirmed stability, with no significant multicollinearity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSSIs remain a significant postoperative risk, with diabetes, wound contamination, and hyperglycemia as key predictors. The observed delay in infection onset during the COVID-19 pandemic underscores the need for extended postoperative surveillance and adaptable infection control strategies during healthcare disruptions. These findings suggest that pandemic-related changes in surgical protocols may delay SSI onset, necessitating extended postoperative monitoring.\u003c/p\u003e","manuscriptTitle":"Pandemic Shadows in the Operating Room: How COVID-19 Altered the Risk and Timing of Surgical Site Infections: A Multivariable Risk Assessment and Time-to-Event Analysis in a Cohort of Abdominal Surgery Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 00:42:17","doi":"10.21203/rs.3.rs-7169512/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-27T13:07:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T09:03:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24141635587865095223862246879177494375","date":"2025-10-23T06:51:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49226180924547935943474733228461112098","date":"2025-10-20T06:19:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T04:58:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173079270593036186370785872487061327666","date":"2025-10-10T15:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-10T14:59:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T10:13:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T06:38:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T14:58:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-07-28T14:54:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9c2902d3-49e3-4bee-a24e-e961db0e154d","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:06:30+00:00","versionOfRecord":{"articleIdentity":"rs-7169512","link":"https://doi.org/10.1186/s12879-025-12115-7","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-11-21 15:58:20","publishedOnDateReadable":"November 21st, 2025"},"versionCreatedAt":"2025-10-26 00:42:17","video":"","vorDoi":"10.1186/s12879-025-12115-7","vorDoiUrl":"https://doi.org/10.1186/s12879-025-12115-7","workflowStages":[]},"version":"v1","identity":"rs-7169512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7169512","identity":"rs-7169512","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}