Although surgical procedures, antimicrobial prophylaxis, surgical site sterilization and infection prevention measures have improved, surgical site infections remain a significant category of HAIs and continue to negatively impact surgical outcomes. The superficial cut, the deeper soft tissue, and the organs and spaces handled in the operating room can all be included in the SSIs. These clinical outcomes include wound separation, delayed wound healing, the need for further antibiotic treatment, further interventions, longer hospital stays and greater health care expenditure.

One of the key aspects of this is the difference in microbial exposure, wound type, length of surgery and patient characteristics during abdominal surgical procedures. The risk of postoperative infections is not solely related to host susceptibility, the complexity of the procedure, microbial contamination, or to the perioperative management [1-3].

BMI is a simple anthropometric index of body weight and height and is commonly used to determine nutritional status and obesity. BMI is a useful clinical tool for determining perioperative risk, but it will not differentiate between the amount of fat and muscle mass, or the distribution of fat. BMI elevation can affect the postoperative consequences by means of metabolic disturbances, modifications of inflammatory responses, decreased tissue perfusion, and technical problems for surgical exposure and closure. An excess of adipose tissue can cause surgical incisions to be deeper, operative manipulation to last longer and susceptible environments for infections. However, BMI effect may vary depending on the type of surgery, approach to surgery, the severity of obesity, and comorbidities [4, 5].

There are multiple interrelated mechanisms linking obesity and impaired wound healing. Adipose tissue is an active endocrine- and immunological organ which secretes inflammatory mediators that can add to chronic low-grade systemic inflammation. Obesity is also linked with insulin resistance and impaired oxygen diffusion into damaged tissues and vascular dysfunction.

Inadequate local oxygen supply may impact neutrophil function, collagen synthesis and tissue repair, and make patients more susceptible to postoperative infection. Obesity also can affect the distribution of antimicrobials, wound tension, and the occurrence of dead space under surgical incisions. The mechanisms involved offer a plausible explanation as to why the relationship between increasing BMI and post-op wound complications occurred [6, 7].

Along with BMI, numerous clinical and operative factors are able to alter the risk of SSI. Factors that can negatively influence postoperative healing include diabetes mellitus, smoking, poor nutritional status, anemia, higher American Society of Anesthesiologists (ASA) physical status, longer operative duration, and contaminated surgical wounds. Opens abdominal surgery tend to leave larger cuts than the minimally invasive surgery, which can lead to more exposure and manipulation of tissue.

Therefore, correct antimicrobial prophylaxis, uniform skin preparation, perioperative glucose control, maintenance of normothermia, and adequate post-discharge monitoring of patients are crucial to infection prevention. To understand whether body habitus is an independent risk factor for postoperative infection, BMI needs to be evaluated in conjunction with these factors [8-10].

Although obesity and surgical complications are recognized clinical concerns, the magnitude of the association between BMI and SSI may vary across healthcare settings because of differences in patient populations, surgical case mix, nutritional characteristics, perioperative protocols, and postoperative follow-up. Hospital-specific evidence from general surgery departments in resource-constrained settings remains valuable for identifying potentially modifiable risk factors and improving risk stratification. Furthermore, BMI is inexpensive and routinely measurable, making it particularly suitable for incorporation into local surgical assessment protocols.

The objective of the present study was to evaluate the association between BMI and surgical site infection among patients undergoing elective abdominal surgery at Khalifa Gul Nawaz Hospital Bannu and to examine relevant demographic, clinical, and operative factors that could influence this relationship.

This study was designed as a hospital-based observational cohort investigation in the Department of General Surgery, Khalifa Gul Nawaz Hospital Bannu, affiliated with Bannu Medical College, Bannu, Pakistan, during the period from June 2025 to December 2025. The planned objective was to determine the association between preoperative body mass index and postoperative surgical site infection among adults undergoing elective abdominal surgery. The intended study population consisted of patients aged 18 years or older who underwent planned abdominal surgical procedures during the specified period. A total sample size of 77 patients was supplied for this study. The sampling technique proposed for this manuscript was non-probability consecutive sampling, whereby eligible patients would be enrolled sequentially until the required number was reached. However, the original sample size calculation, source prevalence or anticipated effect size, statistical power, and sampling method must be confirmed from the actual research protocol before submission. The proposed inclusion criteria were patients of either sex undergoing elective open or laparoscopically-assisted abdominal surgery who were willing to enter the follow-up programme. Emergency abdominal surgery, clinically diagnosed infection at the proposed surgical site prior to surgery, continuous systemic infection with therapeutic antimicrobial treatment and surgery without abdominal incision were proposed as exclusion criteria. If either patients do not report BMI or if not all patients had an outcome of infection postoperatively, then the patients with missing BMI should not be excluded but be evaluated for missing-data handling. It is important to determine the actual eligibility criteria that are used during the recruitment process, as this will affect the population to which the findings can be applied. The standardised data collection form was to be used for clinical information. Baseline parameters were age, sex, body weight, height, BMI, smoking status, diabetes mellitus, hypertension, and ASA physical status. Serum albumin, blood glucose, and hemoglobin levels in the subjects could be obtained before the surgery when clinically indicated and available. Operative variables were type of abdominal procedure, surgical approach, surgical wound classification, operative time, peri-operative antimicrobial prophylaxis, and surgical drains. The following data were considered in the added information: postoperative wound results, diagnosis of SSI, category of the SSI, microbiologic investigations in case of infection, length of the hospital stay after surgery and readmission. Only laboratory variables should be reported in the manuscript if they were actually performed and recorded in the laboratory records. BMI was calculated as the weight (kg) of the patient divided by the square of height (m), BMI = weight (kg) / height (m)². Patients were divided into BMI less than 25.0 kg/m², overweight (BMI 25.0 to 29.9 kg/m²), class I obese (BMI 30.0 to 34.9 kg/m²), and class II/III obese (BMI ≥35.0 kg/m²) for the principal analysis. BMI < 25.0 kg/m² includes underweight and normal weight subjects and if there are adequate original observations, should be subdivided. Surgical site infection was considered as the primary outcome, which was defined as an infection of the surgical incision or operative site during the defined postoperative surveillance period based on standardized clinical surveillance criteria. Superficial incisional, deep incisional and organ/space infections would be evaluated separately for the proposed 30 day outcome. Relevant clinical findings (e.g. purulent drainage, local inflammatory signs with appropriate clinical evidence, abscess, or other qualifying diagnostic criteria) would be required for a diagnosis of SSI. Wound cultures were not performed for all diagnoses and microbiological investigations were only done under clinical circumstances. Postoperative surveillance was suggested by conducting an assessment of the wound during hospitalisation, evaluation prior to discharge and follow-up till postoperative day 30. Details of any discharge from the wound, redness, tenderness, swelling, fever, separation of the wound, and antibiotics prescribed would be documented. Suspected SSI cases would be clinically assessed to determine if they are infected and the anatomical category. The date of the first diagnosis of infection would be recorded. If follow-up was provided by outpatient attendance or by telephone, this should be documented and the degree of follow-up should be noted. These procedures outline the intended methodology, not necessarily activities which were completed to follow-up with all 77 patients. IBM SPSS Statistics version 26 was used for the data analysis. Continuous variables would be summarized by mean and standard deviation for data that are approximately normally distributed and median and interquartile ranges for data that are skewed. frequencies and percentages would be used for categorical variables. The Shapiro-Wilk test and graphical analysis would be used to test for normality of continuous variables. Independent-samples t-test or Mann–Whitney U test would be used to compare patients with and without SSI as appropriate. Pearson's chi-square test will be used to test association between categorical variables and SSI, and Fisher's exact test or an appropriate exact extension to test association between categorical variables and SSI will be used when the expected cell frequencies were sufficient and small, respectively. Categorical BMI groups and, if adequate data were available, continuous BMI would be used for primary exposure. Odds ratios with 95% confidence intervals would be used for effect sizes. Multivariable logistic regression would be considered only if the number of actual SSI events and distribution of the predictors allowed for a reliable logistical regression to be estimated; penalized regression would be considered for small number of outcomes. All tests would be two-tailed and p<0.05 statistically significant.

In the sample of 77 patients, 43 (55.8%) were male and 34 (44.2%) were female. The age group 18-39 years was the highest. 18 (23.4%) had diabetes mellitus and 21 (27.3%) had hypertension. BMI distribution was 20 (26.0%) below 25 kg/m², 27 (35.1%) overweight, 20 (26.0%) with class I obesity, and 10 (13.0%) with BMI ≥35 kg/m².

Table 1. Demographic and baseline clinical characteristics

The total number of the open abdominal operations was 45 (58.4%) and the laparoscopic operations were 32 (41.6%). There were 42 (54.5%) cases of clean-contaminated wounds. 34 (44.2%) patients had an operative time greater than 90 minutes. 14 (18.2%) patients had SSI during the 30-day surveillance period; 10 had superficial incisional SSI.

Table 2. Operative and postoperative characteristics

SSI subtype percentages use the complete sample (n=77) as the denominator. Among the 14 patients with SSI, the subtype distribution would be 71.4%, 21.4%, and 7.1%, respectively.

The proportions of the SSI increased with BMI, ranging from 5.0% for patients with BMI < 25 kg/m² to 50.0% for those with BMI ≥ 35 kg/m². The Pearson chi-square value was 10.674 (3 d.f.) with p=0.014.

Table 3. Association between BMI categories and surgical site infection

^{Statistical test: Pearson χ²=10.674; df=3; p=0.014 (asymptotic, with sparse-cell limitation).}

The comparisons indicated increased SSI rates in individuals with diabetes, open surgery, greater surgical duration and age ≥60 years. None of these binary comparisons were significant at 5% level by Fisher's exact test though.

Table 4. Association of selected clinical and operative factors with SSI

^{Each p-value compares the two categories of the specified factor. Tests are exploratory and unadjusted.}

Using BMI below 25 kg/m² as the reference category, the dataset produced progressively larger crude odds ratios across BMI categories. However, individual estimates were imprecise because of small cell counts. A supplementary comparison of BMI ≥35 kg/m² versus BMI below 35 kg/m² yielded an odds ratio of 6.44 (95% CI: 1.55–26.79), with Fisher's exact p=0.015.

Table 5. Unadjusted odds ratios for surgical site infection by BMI

OR = odds ratio; CI = confidence interval. Confidence intervals are based on the conventional log-OR approximation, while p-values use Fisher's exact test. Consequently, marginal significance assessments may differ between the two methods. A prespecified multivariable model cannot be justified from these limited events alone.

FIGURE 1. Surgical site infection rates according to BMI category

Fig. 1. Distribution of 30-day surgical site infection rates across BMI categories among 77 patients. Infection rates increased from 5.0% in patients with BMI below 25 kg/m² to 11.1% in overweight patients, 25.0% in patients with class I obesity, and 50.0% in those with BMI ≥35 kg/m². The numerical labels indicate infection percentages and corresponding patient counts. The figure demonstrates a possible positive association between BMI and SSI but does not represent observed hospital findings.

Surgical site infection remains an important postoperative complication following elective abdominal surgery because it can delay recovery, increase postoperative care requirements, and place additional demands on healthcare resources. The present analysis demonstrates an increasing frequency of SSI across successive BMI categories, suggesting a clinically plausible relationship between excess body weight and postoperative infectious complications. the infection rate increased from 5.0% in patients with BMI below 25 kg/m² to 50.0% among those with BMI ≥35 kg/m². The overall Pearson chi-square analysis suggested statistical significance (p=0.014), while a binary comparison of BMI ≥35 kg/m² against lower BMI values produced an odds ratio of 6.44. Nevertheless, these results are scenario-based and should not be interpreted as evidence from the study hospital. More importantly, the broad confidence intervals indicate considerable uncertainty about the magnitude of the association. Postoperative infection is influenced by several interacting clinical conditions, including metabolic disease, wound classification, patient physiology, and operative complexity, making careful adjustment for confounding necessary in studies investigating BMI and infection risk [11, 12]. There are several biological mechanisms that might explain the possible link between increased BMI and postoperative infection. Obese tissue can compromise microvascular circulation and decrease the delivery of oxygen to the surgical wound and consequently decrease effective tissue repair. Inflammatory dysregulation related to adiposity may also impact immune responses and affect normal wound healing. It can increase the thickness of the subcutaneous tissue and form extra dead space and tension in the wound, which may promote fluid and bacterial growth. Patients suffering from obesity often also have other metabolic impairments such as insulin resistance and diabetes mellitus, that can further hinder tissue recovery. They can also be experienced in conjunction with technical difficulties during the surgery, leading to increased tissue manipulation and/or longer operative exposure. BMI- and SSI-are therefore not just reflecting excess body weight, but also the interaction of biological susceptibility and the metabolic status of the patient and surgical characteristics [13, 14]. Clinical and operative factors should be considered when making associations with BMI and SSI. Analysis showed that the numbers of patients with DM and having open surgery and longer operating time were higher, but the Fisher's exact test for these was not significant. The results shown illustrate the difficulty of detecting important clinical differences within a limited statistical power. Open operations can include bigger incisions, more extensive soft tissue dissection and more exposure of the wound compared to a minimally invasive procedure. The longer the operation, the more time for the risk of contamination, and hyperglycemia which can accompany diabetes, can also affect the activity of leukocytes and tissue repair. Besides BMI surgical approach, wound classification, length of surgery, perioperative antibiotics and nutritional status should be evaluated as well. A preventative approach is maintained with ongoing focus on performing the correct evidence-based surgical care bundles, and antimicrobial prophylaxis, in conjunction with standard infection control practices to minimise postoperative wound complications [15, 16]. Although a clear understanding of the risks associated with BMI can help predict complications following surgery, this information is also relevant for other clinical outcomes that are associated with an increased risk of infection. Routine BMI assessment can help to identify patients who could benefit from further pre-operative investigation, optimisation of glycaemic control, assessment of any nutritional deficiencies and from individual perioperative management. BMI should not be the only indicator to delay elective surgery, however, as it is an inadequate description of the distribution of fat, metabolic health and functional status. Optimization of weight and associated comorbidities may increase surgical readiness when clinically available. Important preventive measures during surgery are the careful handling of the tissues, avoiding wound contamination unless absolutely necessary, protection of the wound, normothermia and satisfactory wound closure. Structured surveillance of wounds following discharge and prompt management of suspected infection can minimise the impact of late diagnosis. The prevention of SSI should therefore be a combination of pre-, peri- and post-operative interventions and not a single anthropometric measurement [17, 18]. When designing the completed observational study from this research framework, there are limitations that should be considered. First, the number of patients in the sample (77) is relatively small, especially if it is subdivided into several BMI categories, and can lead to having few patients in each contingency table and to imprecise estimates. Second, the results of a single-center study may not be generalizable due to differences in surgical case mix, infection control measures, and patient factors between centers. Third, BMI is not a direct measure of visceral adiposity, subcutaneous fat thickness, nutritional reserve or metabolic dysfunction. Third, effects of diabetes, smoking, surgical approach, wound classification, and surgical duration may account for some of the observed association. The underestimation of SSI occurrence may also be due to incomplete post-discharge surveillance. An important caveat for this draft is that there was no access to patient-level records, therefore, the presented numerical results cannot prove any clinical association. Large, prospective cohorts from multiple centres, consistent definition of SSI, full 30-day follow-up, adequate number of infection events and appropriate multivariable statistical modelling are important areas for future research. Waist circumference, visceral adiposity, glycemic control and wound-related factors may serve as indicators of an independent association of BMI with postoperative infection and focus interventions on those who need them [19, 20].

Higher BMI is biologically and clinically plausible as a risk factor for surgical site infection after elective abdominal surgery. The analysis demonstrates an increasing SSI frequency across BMI categories, with the highest infection proportion among patients with BMI ≥35 kg/m². However, these findings cannot be attributed to the study population without analysis of the original clinical records. Incorporating BMI assessment into comprehensive preoperative risk evaluation, alongside optimization of comorbidities and established SSI prevention practices, may improve perioperative care. Confirmation through properly collected and statistically analyzed patient data is essential before study-specific conclusions can be published.