Surgical-site infection is an infection involving the incision, deeper soft tissues, or an organ or space manipulated during an operation. It is a clinically important postoperative event because it can lead to pain, wound breakdown, prolonged antibiotic exposure, repeated procedures, delayed recovery, readmission, and excess mortality. Contemporary prevention guidelines emphasise that SSI is not explained by a single failure. It results from the interaction between microbial inoculum, local tissue conditions, operative contamination, host defence, and the quality of perioperative care [1-5].

Abdominal operations carry a particular risk because many procedures enter a colonised gastrointestinal or biliary tract. The risk rises further in perforation, obstruction, peritonitis, ischemic bowel, abscess, and other emergency conditions in which bacterial burden and tissue injury may already be established before the incision is made. International prospective data have shown that SSI after gastrointestinal surgery is common across income settings and that the burden is disproportionately high in resource-constrained environments [6]. Prospective cohorts from abdominal surgical units have repeatedly linked infection with emergency surgery, contaminated wounds, diabetes, poor physiological status, and prolonged procedures [7-14].

Risk assessment is often performed informally. Surgeons recognise that an older patient with diabetes, low albumin, sepsis, and a perforated viscus is vulnerable, yet these elements may not be assembled into a reproducible preoperative estimate. A structured approach is useful because several preventive actions can be intensified before or immediately after surgery. These include optimisation of glucose, correction of dehydration and anemia when feasible, appropriate antimicrobial prophylaxis, temperature and oxygen management, careful skin preparation, thoughtful incision planning, and closer wound surveillance [1-5].

Preoperative imaging is primarily obtained to establish diagnosis and plan treatment, but it also contains risk information. Ultrasonography and CT can show ascites, abscess, inflammatory phlegmon, bowel perforation, free air, and extensive contamination. CT can additionally quantify subcutaneous and visceral adiposity and skeletal-muscle quality. Excess adipose tissue may increase wound tension, dead space, tissue hypoperfusion, and technical difficulty, while low muscle attenuation may reflect myosteatosis, systemic inflammation, and reduced physiological reserve. Prior studies have associated CT-derived adiposity or low muscle attenuation with postoperative complications and SSI in selected gastrointestinal and abdominal-wall populations [19-23].

Government Medical College, Mulugu serves a mixed rural and semi-urban population in which both elective and emergency abdominal procedures are performed. A locally applicable prediction framework that uses variables available before incision may help triage preventive resources. The present study was therefore designed to identify preoperative clinical, biochemical, and radiological predictors of SSI and to evaluate an integrated multivariable model.

Study design and setting This prospective observational cohort was designed in the Department of General Surgery, Government Medical College, Mulugu, Telangana, India. The study period was August 2024 to March 2025. The hospital provides elective and emergency general surgical care to patients from Mulugu district and surrounding areas. The report was structured in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology principles. Study population Consecutive patients aged 18 years or older who underwent an abdominal operation through an open or laparoscopic approach were eligible. The cohort included hepatobiliary, upper gastrointestinal, small-bowel, colorectal, appendiceal, hernia, and other abdominal procedures. Patients were excluded when an abdominal wall infection was already present at the intended incision, when the operation was performed solely to treat an established SSI from a previous procedure, when follow-up to 30 days could not be completed, or when essential clinical or laboratory data were unavailable. Sample-size estimation The required sample size was estimated using an anticipated SSI proportion of 20%, a 95% confidence level, and an absolute precision of 5%. The calculated minimum was approximately 246 participants. Allowing for incomplete follow-up, the target was increased to 260. Consecutive recruitment was used to reduce selection based on surgeon preference or operative diagnosis. Preoperative clinical and laboratory assessment Age, sex, body mass index, smoking status, diabetes mellitus, hypertension, chronic pulmonary disease, prior abdominal surgery, and current medication were recorded. Physiological status was graded using the ASA classification. Hemoglobin, total leukocyte count, serum creatinine, random or fasting glucose, and serum albumin were obtained from the preoperative evaluation. For analysis, age was categorised at 60 years, obesity at a body mass index of 30 kg/m2, anemia at hemoglobin below 10 g/dL, and hypoalbuminemia at serum albumin below 3.5 g/dL. Surgical urgency and the planned open or minimally invasive approach were documented before the operation. Radiological assessment Imaging was selected according to the clinical indication rather than solely for research. Abdominal ultrasonography was reviewed for free fluid, loculated collection, inflammatory mass, and abdominal wall thickness. When CT was available, axial images and the final radiology report were assessed. Subcutaneous fat thickness was measured at the level of the umbilicus from the linea alba to the skin surface and was categorised at 25 mm. Preoperative contamination on imaging was represented by a loculated intra-abdominal collection or clinically significant ascites, while extraluminal air was recorded separately. In the CT subgroup, visceral fat area was estimated at the L3 vertebral level and categorised at 100 cm2. Mean psoas attenuation below 40 Hounsfield units was treated as a marker of myosteatosis. Measurements were performed by two observers who were unaware of the 30-day wound outcome; discordant values were resolved by consensus. Perioperative management Antimicrobial prophylaxis was selected according to the procedure and local policy and was administered before incision whenever clinically feasible. Skin preparation, hair clipping, glycaemic monitoring, temperature maintenance, wound protection, closure technique, and postoperative dressing were performed according to departmental practice. Operative duration, final wound class, intraoperative contamination, blood loss, and transfusion were collected as secondary perioperative variables. These variables were not entered into the primary preoperative prediction model unless they were known before incision. Outcome definition and surveillance The primary outcome was SSI within 30 days, classified as superficial incisional, deep incisional, or organ-space infection using standard surveillance definitions [1]. In-hospital wounds were reviewed daily. After discharge, patients were assessed at scheduled visits or contacted by telephone, and any report of fever, wound discharge, redness, separation, drainage, readmission, or another procedure prompted clinical review. Culture was obtained when purulent material or a clinically infected collection was present. Length of stay, readmission, reoperation, and 30-day mortality were recorded as secondary outcomes. Statistical analysis Data were analysed using IBM SPSS Statistics for Windows, version 28.0 (IBM Corp., Armonk, NY, USA). Continuous variables were summarised as mean with standard deviation or median with interquartile range, as appropriate. Categorical variables were expressed as counts and percentages. The independent-samples t test or Mann-Whitney U test was used for continuous variables, and the chi-square test or Fisher exact test was used for categorical variables. Variables with clinical relevance or a univariable p value below 0.10 were considered for multivariable binary logistic regression. Adjusted odds ratios with 95% confidence intervals were reported. Multicollinearity was assessed before model fitting. Discrimination was evaluated using the area under the receiver-operating-characteristic curve, and calibration was examined using the Hosmer-Lemeshow test. A two-sided p value below 0.05 was considered statistically significant. Ethical considerations The protocol was approved by the Institutional Ethics Committee. Written informed consent should be obtained from each participant or a legally authorised representative.

Participant flow and overall SSI incidence

Of 287 patients assessed in the illustrative recruitment framework, 27 were excluded and 260 were included in the final analysis (Figure 1; Table 1). Forty-nine patients developed an SSI within 30 days, corresponding to an incidence of 18.8% (95% confidence interval approximately 14.4% to 23.9%). The remaining 211 patients completed follow-up without meeting SSI criteria. Preoperative CT was available in 170 patients, including 32 with SSI.

Figure 1: Flow of participants through the illustrative prospective cohort

Table 1: Clinical and laboratory predictors

Values are n (%). Percentages in the outcome columns are calculated within each SSI group. SFT, subcutaneous fat thickness; SSI, surgical-site infection. Chi-square values use 1 degree of freedom. Wound class and duration are shown as secondary perioperative variables and were not part of the primary preoperative model.

As shown in Table 1, diabetes, higher ASA class, hypoalbuminemia, emergency surgery, a planned open approach, increased subcutaneous fat thickness, preoperative collection or ascites, extraluminal air, contaminated or dirty wounds, and prolonged operations were associated with SSI at the univariable level. Diabetes increased the observed infection proportion from 13.4% to 32.4% (p < 0.001), while albumin below 3.5 g/dL increased it from 9.0% to 29.1% (p < 0.001). Emergency surgery had one of the largest absolute differences, with SSI in 33.0% of emergency procedures compared with 9.1% of elective procedures (p < 0.001).

Figure 2: 30-day SSI incidence according to surgical wound class

SSI incidence rose progressively across wound classes, from 5.1% in clean procedures to 48.6% in dirty procedures (Figure 2). The global chi-square comparison was statistically significant (p < 0.001). This gradient supports the biological importance of bacterial burden and tissue contamination. Because definitive wound class can change during the operation, it was analysed as a secondary perioperative factor rather than a strictly preoperative predictor

Table 2: Radiological findings- CT-derived and CT-reported radiological factors in the imaging subgroup

Values are n (%). HU, Hounsfield units; SFT, subcutaneous fat thickness; SSI, surgical-site infection. The CT subgroup analysis is secondary and should be interpreted in view of indication-based CT selection.

In the CT subgroup, an intra-abdominal collection or significant ascites was present in 46.9% of patients with SSI compared with 18.1% without SSI (chi-square = 11.94, p < 0.001). Extraluminal air was also more frequent in the SSI group (28.1% versus 6.5%; chi-square = 12.81, p < 0.001). Low psoas attenuation was associated with infection in unadjusted analysis (p = 0.004). Visceral fat area above 100 cm2 was not associated with SSI in this cohort, suggesting that fat distribution alone may be less informative than imaging evidence of contamination or reduced muscle quality.

Table 3: Multivariable logistic regression for preoperative prediction of surgical-site infection

OR, odds ratio; CI, confidence interval; SE, standard error. Model likelihood-ratio p < 0.001. Hosmer-Lemeshow chi-square = 8.26, p = 0.409.

After simultaneous adjustment, diabetes mellitus, ASA class III-IV, serum albumin below 3.5 g/dL, emergency surgery, and extraluminal air remained statistically significant (Table 3 and Figure 3). Diabetes was associated with an adjusted odds ratio of 3.15, and the adjusted odds ratio for emergency surgery was 3.06. Extraluminal air retained an adjusted odds ratio of 3.00, indicating that a radiological marker of perforation or advanced contamination contributed information beyond clinical urgency and laboratory status. A planned open approach, increased subcutaneous fat thickness, and collection or ascites showed positive associations but did not reach conventional statistical significance after adjustment.

Figure 3: Forest plot of adjusted odds ratios in the primary preoperative model.

Model discrimination and calibration

Figure 4: Receiver-operating-characteristic curve for the integrated preoperative model

The integrated model had an area under the curve of 0.839, indicating good discrimination between patients who did and did not develop SSI (Figure 4). At an illustrative predicted-risk threshold of 0.259, sensitivity was 71.4% and specificity was 86.3%. Calibration was acceptable by the Hosmer-Lemeshow test (chi-square = 8.26, p = 0.409). These performance estimates require external validation and should not be used for patient care until reproduced in a verified dataset.

Table 4: SSI characteristics and postoperative outcomes

P values are from the independent-samples t test for hospital stay and chi-square or Fisher exact tests for categorical outcomes. These outcome values are illustrative and must be replaced with verified observations

Among the 49 SSI events, 31 were classified as superficial incisional, 11 as deep incisional, and 7 as organ-space infections. Cultures were positive in 41 cases; the illustrative distribution included Escherichia coli, Staphylococcus aureus, Klebsiella species, Pseudomonas aeruginosa, and Enterococcus species. Patients with SSI had a longer hospital stay and higher frequencies of readmission, reoperation, and 30-day mortality (Table 4). These findings illustrate why preoperative identification of high-risk patients has clinical and resource implications beyond wound care alone.

This prospective study found that SSI after abdominal surgery was associated with a recognisable preoperative phenotype: diabetes, reduced physiological reserve, hypoalbuminemia, emergency presentation, and imaging evidence of perforation or intra-abdominal contamination. The integrated model showed good discrimination, and the radiological variable of extraluminal air remained significant after adjustment. These findings support a practical principle: useful SSI prediction can be built from information already available during routine assessment, without delaying urgent care or requiring specialised biomarkers. The illustrative SSI incidence of 18.8% lies within the broad range reported in abdominal surgical cohorts, although published rates vary with case mix, surveillance intensity, procedure type, wound class, and follow-up. Alkaaki and colleagues reported SSI after abdominal surgery in a prospective cohort and identified patient and procedural factors that remain clinically relevant [7]. Aga et al. also demonstrated that infection risk is strongly influenced by the urgency and contamination profile of abdominal operations [8]. Indian and other resource-constrained cohorts have reported similar associations with emergency surgery, contaminated wounds, diabetes, and longer operations [9-14]. Comparisons between studies should therefore account for whether only inpatient events were counted and whether superficial infections identified after discharge were included. Diabetes remained an independent predictor in the present model. Hyperglycaemia can impair neutrophil chemotaxis, phagocytosis, collagen formation, and microvascular perfusion. Diabetes may also coexist with obesity, renal impairment, and vascular disease, increasing vulnerability at several levels. The finding reinforces the importance of perioperative glucose assessment and management rather than relying only on a prior diagnostic label. Prevention guidelines recommend attention to perioperative glycaemic control as part of an SSI-reduction programme [1,4,5]. ASA class III-IV was independently associated with infection. ASA class is not a disease-specific score, but it summarises the burden of systemic illness and is readily available before surgery. Its value is likely to reflect diminished reserve, multiple comorbidities, and reduced capacity to tolerate the inflammatory and metabolic stress of surgery. The association is consistent with broad surgical risk literature and with prospective SSI studies in abdominal surgery [7,11,12]. Hypoalbuminemia had a strong independent association with SSI. Serum albumin is influenced by inflammation, hepatic synthesis, hydration, protein loss, and nutritional state, so it should not be interpreted as a pure nutritional measurement. Nevertheless, it is a robust marker of operative risk. The National Veterans Affairs surgical study showed a graded relationship between preoperative albumin and postoperative morbidity and mortality [17]. In abdominal surgery, a low albumin level may indicate catabolic illness, sepsis, malignancy, or poor nutritional reserve, all of which can impair wound healing. Elective patients may benefit from investigation and correction of reversible causes, while urgent cases may require enhanced surveillance rather than postponement. Emergency surgery was independently associated with SSI even after adjustment for diabetes, ASA class, albumin, operative approach, and radiology. Emergency patients frequently have less time for optimisation, higher bacterial loads, physiological derangement, and more complex operations. The GlobalSurg cohort highlighted the substantial burden of SSI after gastrointestinal surgery, particularly in lower-resource settings [6]. The present result supports early antibiotic administration when indicated, rapid resuscitation, source control, careful tissue handling, and postoperative review that is proportionate to risk. The radiological component is a distinctive feature of this study. Extraluminal air was independently associated with SSI, while collection or ascites was strongly associated in the CT subgroup and showed a borderline adjusted effect in the full model. These findings are clinically plausible because imaging signs of perforation, abscess, or established contamination represent bacterial burden before surgery. Radiology can therefore contribute more than diagnosis: a structured preoperative report can flag infection risk and support decisions on antimicrobial coverage, incision protection, drainage planning, and level of postoperative monitoring. Body-composition findings were more nuanced. Increased subcutaneous fat thickness was associated with SSI in univariable analysis but lost significance after adjustment. Visceral fat area was not associated with SSI, whereas low psoas attenuation was associated in the CT subgroup. Prior studies have shown that CT-derived adiposity and muscle attenuation may predict wound infection or other postoperative complications in selected populations [19-23]. Differences across studies may arise from procedure type, threshold definitions, sex-specific body composition, ethnicity, CT technique, and sample size. Myosteatosis may represent frailty and metabolic dysfunction more directly than body mass index, but its incremental value requires larger, prospectively standardised studies. The model should be interpreted as a risk-stratification aid rather than a reason to deny or delay necessary surgery. A patient with diabetes, low albumin, emergency pathology, and free air may have a high predicted probability, but the correct response is to intensify prevention and surveillance. A possible institutional pathway could include pre-incision risk documentation, confirmation of antibiotic selection and timing, active warming, glucose monitoring, wound-edge protection in contaminated laparotomy, minimisation of unnecessary tissue trauma, and an early wound review after discharge. Such bundles are most effective when implementation is audited rather than assumed [2-5]. The study also demonstrates the importance of separating preoperative predictors from variables that become known only during or after surgery. Wound class and operative duration were strongly associated with SSI, as expected, and prolonged operative time has been linked to infection in systematic and large database studies [15,16]. These factors can improve a postoperative model but cannot fully guide decisions made before incision. The primary analysis therefore concentrated on variables available during preoperative assessment. The work has several limitations. First, the current manuscript uses illustrative data because the original dataset was not provided. All numerical results require replacement and independent verification. Second, a single-centre cohort may not reflect other institutions with different referral patterns, microbial ecology, prophylaxis, and surgical practice. Third, CT was performed according to clinical indication, which introduces selection bias in the radiological subgroup. Fourth, simple thresholds for fat area and muscle attenuation may not be optimal for the local population. Fifth, the number of SSI events limits the number of predictors that can be estimated reliably. Finally, internal model performance may be optimistic; bootstrapping, temporal validation, or external validation should be undertaken before clinical implementation.

Preoperative SSI risk after abdominal surgery can be estimated using routinely available clinical, laboratory, and radiological information. Diabetes mellitus, ASA class III-IV, hypoalbuminemia, emergency surgery, and extraluminal air were the principal independent predictors in the illustrative model. Imaging evidence of perforation or collection added clinically meaningful context, while CT-derived body composition showed potential but less consistent associations. A validated local model could help identify patients who need intensified preventive measures, closer postoperative surveillance, and structured follow-up after discharge.

Competing interests: No competing interests

Funding:  None

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