Analysis of Influencing Factors for Incisional Surgical Site Infection after Laparoscopic Colorectal Cancer Surgery and Construction of a Prediction Model

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Laparoscopic surgery has become the preferred treatment due to its minimally invasive advantages, but postoperative incisional surgical site infection (I-SSI) still significantly affects patient prognosis. This study aimed to analyze the risk factors for incisional infection after laparoscopic colorectal cancer surgery and construct a risk prediction model to provide a basis for precise clinical prevention. Clinical data of 919 patients who underwent laparoscopic radical resection of colorectal cancer at Northern Jiangsu People's Hospital from February 2018 to February 2022 were retrospectively collected. Univariate and multivariate Logistic regression were used to screen infection-related risk factors. A nomogram prediction model was constructed using R software, and the model performance was evaluated by ROC curve and calibration curve. Results showed that the incidence of incisional infection among 919 patients was 6.0%. Multivariate analysis indicated that diabetes mellitus (OR = 2.05), hypoproteinemia (OR = 3.52), high BMI (OR = 1.10), emergency surgery (OR = 0.35), and longitudinal incision (OR = 2.32) were independent risk factors, while preoperative prophylactic use of antibiotics (OR = 0.44) and incision length greater than the shortest tumor diameter (especially with a difference of 1–2 cm, OR = 0.35) were protective factors. The constructed nomogram model had an AUC of 0.76 (95% CI = 0.70–0.82), with a sensitivity of 0.70 and specificity of 0.83, and good calibration effect. This study identifies key influencing factors for incisional infection after laparoscopic colorectal cancer surgery, and the established nomogram model has moderate predictive performance (AUC = 0.76), providing a scientific basis for personalized surgical planning and infection prevention. Biological sciences/Cancer Health sciences/Diseases Health sciences/Gastroenterology Health sciences/Medical research Health sciences/Oncology Health sciences/Risk factors Laparoscopy Colorectal cancer Incisional surgical site infection (I-SSI) Risk factors Prediction model Incision length Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Colorectal cancer (CRC), as one of the most common malignant tumors worldwide, has seen continuous innovations in its treatment methods with the development of minimally invasive technologies. Laparoscopic surgery, leveraging advantages such as minimal trauma, rapid recovery, and short hospital stays, has become the preferred surgical approach for radical resection of colorectal cancer [ 1 , 2 ] . Compared with traditional open surgery, laparoscopic surgery can reduce postoperative pain and alleviate systemic inflammatory responses. However, postoperative surgical site infection (SSI) remains a critical clinical challenge affecting patient prognosis [ 3 , 4 ] . According to epidemiological data, the incidence of SSI after laparoscopic colorectal cancer surgery is approximately 1%-10%. It not only prolongs the patient's hospital stay and increases medical costs but also may lead to severe complications such as intensive care and reoperation, and even endanger life [ 5 ] . Among them, incisional surgical site infection (I-SSI), as an important type of SSI, is directly related to surgical operations and incision management. Its pathogenesis involves multiple factors, including the patient's basic condition, surgical design, and perioperative management. Existing studies have confirmed that diabetes, obesity, preoperative use of antibiotics, surgical type, etc., are associated with SSI [ 6 – 8 ] . However, these studies mostly focus on single-factor analysis, lacking a systematic exploration of the interaction of multiple factors. In particular, research on the "matching relationship between incision length and tumor size"—a characteristic variable in laparoscopic surgery—is still insufficient [ 9 ] . As a key operational parameter in laparoscopic surgery, the rationality of surgical incision length directly affects incision healing and infection risk: an excessively long incision may increase the chance of bacterial colonization, while an excessively short one may raise the risk of contamination due to mechanical compression during tumor extraction [ 5 ] . However, there is currently no consensus on the impact of the quantitative relationship between incision length and tumor size on I-SSI, and there is a lack of risk prediction tools that integrate multiple factors, making it difficult to guide the formulation of personalized clinical surgical plans. Therefore, this study aims to systematically explore the risk factors for incisional infection after laparoscopic colorectal cancer surgery through retrospective analysis, focusing on clarifying the impact of the association between incision length and tumor size on infection, and constructing a prediction model based on the screened key factors. By collecting a large amount of clinicopathological data and using statistical methods to identify independent risk factors, the study ultimately provides a scientific basis for clinically identifying high-risk patients, optimizing incision design, and formulating personalized prevention strategies, in order to reduce the incidence of postoperative incisional infection, improve patient prognosis, provide quantitative standards for incision design, and further promote the standardized application of minimally invasive treatment technologies for colorectal cancer [ 4 , 10 ] . Materials and Methods 1.1 Selection and Criteria of Study Subjects Clinical data of patients who underwent laparoscopic radical resection of colorectal cancer in the Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University from February 2018 to February 2022 were retrospectively collected. Inclusion criteria: ① Aged ≥ 18 years; ② Pathologically confirmed as colon cancer or rectal cancer after surgery; ③ Surgical method was laparoscopic radical resection of colorectal cancer (including laparoscopically assisted or totally laparoscopic surgery); ④ Complete clinicopathological data (including baseline data, surgical records, postoperative follow-up records, etc.). Exclusion criteria: ① Converted to open surgery during operation or failed to complete the scheduled surgical plan; ② Complicated with severe infectious diseases (such as sepsis, abscess, etc.) or immunodeficiency diseases (such as AIDS, long-term use of immunosuppressants, etc.); ③ Complicated with severe liver and kidney dysfunction (Child-Pugh grade C); ④ Received neoadjuvant chemoradiotherapy before surgery; ⑤ Distant tumor metastasis (stage Ⅳ) or combined with other malignant tumors; ⑥ Missing clinical data or interrupted follow-up. 1.2 Data Collection The following information was extracted through the hospital electronic medical record system: Baseline data: age, gender, body mass index (BMI, kg/m²); past medical history (hypertension, diabetes, hypoproteinemia, where hypoproteinemia was defined as preoperative serum albumin < 35g/L). Collection of all patients' clinical data obtained informed consent and complied with the requirements of the Declaration of Helsinki. Preoperative preparation: ① Tumor size: the shortest diameter of the tumor was measured by preoperative enhanced CT (measurement method: the phase with the best contrast after enhancement was selected, sagittal or coronal reconstruction was performed, and measurement was conducted at the smallest layer of the lesion; measured independently by 2 attending radiologists, with intraclass correlation coefficient (ICC) ≥ 0.85 to ensure consistency); ② Surgical type (elective/emergency, emergency surgery was defined as surgery required within 24 hours due to tumor obstruction, bleeding, etc.); ③ Use of prophylactic antibiotics (intravenous drip 0.5-1.0 hours before surgery; drug selection: cefazolin 1g or cefuroxime 1g [ 11 ] ; levofloxacin 0.5g was used for patients allergic to cephalosporins, and whether the administration timing was standardized was recorded). Surgical conditions: surgical site (colon/rectum), direction of auxiliary incision (longitudinal incision/transverse incision) (longitudinal incision refers to the incision parallel to the midline of the abdomen, and transverse incision refers to the incision perpendicular to the midline of the abdomen), incision length (actual incision length recorded during surgery, accurate to 0.1cm). Primary outcome indicator: postoperative incisional infection (I-SSI). The diagnostic criteria refer to the 2017 Guidelines for the Prevention of Surgical Site Infection by the US Centers for Disease Control and Prevention (CDC) [ 12 ] : any of the following conditions occurring at the incision site within 30 days after surgery: ① Pus is drained; ② Pathogens are detected in fluid/tissue culture; ③ Presence of infection signs (pain, tenderness, swelling, redness, fever); ④ The incision spontaneously dehisces or is opened by a surgeon due to infection signs. 1.3 Statistical Methods and Model Construction Data processing: SPSS 27.0 software was used for analysis. Normality of quantitative data was first tested by Shapiro-Wilk test. Those conforming to normal distribution were expressed as (x ± s), and independent sample t-test was used for comparison between groups; non-normally distributed data were expressed as M (P25, P75), and Mann-Whitney U test was used for comparison between groups. Qualitative data were expressed as [cases (%)], and chi-square test or Fisher's exact probability method (when n < 5) was used for comparison between groups. Screening of risk factors: ① Univariate analysis: variables that may affect I-SSI (age, gender, BMI, underlying diseases, etc.) were included one by one, and variables with P < 0.1 entered multivariate analysis; ② Multivariate Logistic regression: stepwise regression (αin = 0.05, αout = 0.10) was used to screen independent risk factors, and odds ratio (OR) and 95% confidence interval (95% CI) were calculated. Model construction and evaluation: A nomogram prediction model was constructed using R 4.2.2 software (rms package). The weight of nomogram variables was determined based on multivariate Logistic regression coefficients, and Bootstrap sampling was used for internal validation. The following indicators were used to evaluate model performance: ① Discrimination: a receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC) was calculated. AUC > 0.7 indicated that the model had certain discrimination ability; ② Calibration: a calibration curve was drawn using Bootstrap method (1000 repeated samplings), and Harrell's concordance index (C-index) was calculated. A C-index closer to 1 indicated better calibration effect. Significance level: all tests were two-tailed, and P < 0.05 was considered statistically significant. Results 2.1 Clinicopathological Characteristics of Patients and Incidence of Postoperative Incisional Infection A total of 919 patients who underwent laparoscopic radical resection of colorectal cancer were included in this study, among whom 55 developed postoperative incisional infection, with an overall infection incidence of 6.0%. The analysis of baseline characteristics of patients and their association with incisional infection is shown in Table 1 . Demographics and underlying diseases: There were no statistically significant differences between the infection group and the non-infection group in age (62.3±11.2 years vs 60.9±13.6 years, t=0.570, P=0.570), gender (male proportion: 56.4% vs 59.4%, χ²=0.194, P=0.660), and complicated hypertension (43.6% vs 43.1%, χ 2 =0.007, P=0.933). However, the proportions of complicated diabetes (36.4% vs 20.1%, χ²=8.174, P=0.004) and hypoproteinemia (32.7% vs 20.3%, χ²=4.849, P=0.028) in the infection group were significantly higher, and BMI was higher (26.3±4.2 kg/m² vs 25.0±4.6 kg/m², t=2.046, P=0.041). Surgery-related factors: The proportions of emergency surgery (16.4% vs 6.7%, χ²=7.125, P=0.008) and longitudinal incision (83.6% vs 69.0%, χ²=5.274, P=0.022) in the infection group were significantly higher than those in the non-infection group. The infection rate of longitudinal incision (46/596≈7.7%) was significantly higher than that of transverse incision (9/268≈3.4%). The proportion of preoperative prophylactic use of antibiotics was lower (52.7% vs 67.7%, χ²=5.234, P=0.022). The shortest diameter of the tumor measured by preoperative CT in the infection group was larger (4.4±1.6 cm vs 3.6±1.3 cm, t=4.19, P<0.001), and there was a significant difference in the matching relationship between incision length and the shortest tumor diameter: the proportion of "incision length ≤ shortest tumor diameter" in the infection group was higher (41.8% vs 22.8%), while the proportion of "incision length > shortest tumor diameter by more than 2 cm" was lower (23.6% vs 34.7%), with χ²=11.222 and P=0.011 in the distribution of differences between the two groups. Other factors: There were no statistically significant differences in surgical site (colon vs rectum) and absolute value of incision length (5.8±2.7 cm vs 5.4±1.8 cm, t=1.77, P=0.077) between the two groups. Table 1. Analysis of baseline characteristics of patients and their association with incisional infection Characteristics I-SSI Group (n=55) Non-I-SSI Group (n=864) t / χ² p Age (x̄ ± s, years) 62.3±11.2 60.9±13.6 0.570 0.570 Sex, n (%) Male 31 (56.4%) 513 (59.4%) 0.194 0.660 Female 24 (43.6%) 351 (40.6%) Hypertension, n (%) 24(43.6%) 372 (43.1%) 0.007 0.933 Diabetes, n (%) 20 (36.4%) 174 (20.1%) 8.174 0.004 Hypoproteinemia, n (%) 18 (32.7%) 175 (20.3%) 4.849 0.028 BMI (kg/m², x̄ ± s) 26.3±4.2 25.0±4.6 2.046 0.041 Surgical Site, n (%) Colon Rectum 29 (52.7%) 26 (47.3%) 477 (55.2%) 387 (44.8%) 0129 0.720 Incision Orientation, n (%) Vertical Transverse Incision Length (x̄ ± s, cm) 46 (83.6%) 9 (16.4%) 5.8±2.7 596 (69.0%) 268 (31.0%) 5.4±1.8 5.274 1.770 0.022 0.077 Surgery Type, n (%) Emergency Elective 9 (16.4%) 46 (83.6%) 58 (6.7%) 806 (93.3%) 7.125 0.008 Prophylactic Antibiotics, n (%) Yes No 29 (52.7%) 26 (47.3%) 585 (67.7%) 279 (32.3%) 5.234 0.022 Radiological Tumor Size (cm, x̄ ± s) 4.4±1.6 3.6±1.3 4.19 ＜0.001 Incision Length - Min Tumor Diameter, n (%) ≤0（cm） 0-1（cm） 1-2（cm） ＞2（cm） 23 (41.8%) 11 (20.0%) 8 (14.5%) 13 (23.6%) 197 (22.8%) 171 (19.8%) 196 (22.7%) 300 (34.7%) 11.222 0.011 Note: Hypoproteinemia was defined as preoperative serum albumin <35g/L. 2.2 Analysis of Risk Factors for Postoperative Incisional Infection Univariate analysis ( Figure 1 ) showed that factors significantly associated with incisional infection included: complicated diabetes (OR=2.27, 95% CI=1.28-4.02, P=0.005), hypoproteinemia (OR=1.92, 95% CI=1.06-3.45, P=0.030), high BMI (OR=1.07, 95% CI=1.01-1.14, P=0.042), and longitudinal incision (OR=2.30, 95% CI=1.11-4.76, P=0.025). Elective surgery (OR=0.37, 95% CI=0.17-0.79, P=0.010), preoperative prophylactic use of antibiotics (OR=0.53, 95% CI=0.31-0.92, P=0.024), and incision length > shortest tumor diameter (especially with a difference of 1-2 cm: OR=0.35, 95% CI=0.15-0.80, P=0.013; difference >2 cm: OR=0.37, 95% CI=0.18-0.75, P=0.006) were protective factors. Gender, age, complicated hypertension, and surgical site were not significantly associated with infection (P>0.05). Multivariate Logistic regression analysis ( Figure 2 ) further showed that the following factors were independent risk factors for postoperative incisional infection (P<0.05): complicated diabetes (OR=2.05, 95% CI=1.10-3.79, P=0.023), hypoproteinemia (OR=3.52, 95% CI=1.73-7.17, P<0.001), high BMI (OR=1.10, 95% CI=1.02-1.19, P=0.011), and longitudinal incision (OR=2.32, 95% CI=1.09-4.92, P=0.029). Elective surgery (OR=0.35, 95% CI=0.16-0.80, P=0.012), preoperative prophylactic use of antibiotics (OR=0.44, 95% CI=0.24-0.78, P=0.005), and incision length > shortest tumor diameter (difference of 1-2 cm: OR=0.35, 95% CI=0.15-0.81, P=0.015; difference >2 cm: OR=0.36, 95% CI=0.17-0.75, P=0.007) remained independent protective factors. 2.3 Construction of Nomogram Prediction Model for Postoperative Incisional Infection Based on the independent risk factors screened by multivariate regression (diabetes, hypoproteinemia, BMI, surgical type, incision direction, prophylactic antibiotics, difference between incision length and shortest tumor diameter), a nomogram prediction model was constructed using R software ( Figure 3 ). The model calculates the infection risk through the following steps: each variable corresponds to a score on the "Points" axis (e.g., BMI=26 kg/m² corresponds to 10 points, diabetes (yes) corresponds to 20 points); the total score of all variables is summed to obtain "Total Points"; the predicted probability of postoperative incisional infection (ranging from 0.1 to 0.5) is obtained from the "Risk" axis corresponding to "Total Points". 2.4 Validation of the Nomogram Model ROC curve analysis of the model showed that the AUC was 0.76 (95% CI=0.70-0.82), indicating moderate discrimination ability; the sensitivity was 0.70 (i.e., 70% of infected patients could be correctly identified), and the specificity was 0.83 (i.e., 83% of non-infected patients could be correctly excluded), suggesting high predictability of the model ( Figure 4 ). Calibration: Bootstrap validation (1000 samplings) showed that the curve of predicted risk and actual infection probability in the calibration chart was close to the ideal diagonal ( Figure 5 ), with a Harrell C-index of 0.73, indicating good consistency between the predicted values of the model and the actual results. Discussion Laparoscopic radical resection of colorectal cancer has become the mainstream surgical procedure for the treatment of colorectal cancer; however, postoperative incisional surgical site infection (I-SSI) remains a significant issue affecting prognosis. Through the analysis of 919 patients, this study identified diabetes mellitus, high BMI, hypoproteinemia, emergency surgery, and longitudinal incision as independent risk factors for postoperative incisional infection. In contrast, preoperative prophylactic use of antibiotics and an incision length greater than the shortest tumor diameter (especially with a difference of 1–2 cm) were identified as protective factors. A nomogram model with good predictive performance (AUC = 0.76) was constructed based on these factors. First, we discuss the clinical significance and mechanism analysis of each independent risk factor. Diabetes mellitus, a confirmed high-risk factor in this study, is consistent with the conclusions of previous studies [ 6 ] . The hyperglycemic state in diabetic patients can impair the body's anti-infective capacity by inhibiting leukocyte recruitment, reducing neutrophil phagocytic function, and decreasing antibody activity [ 13 ] . Meanwhile, hyperglycemia-induced microangiopathy reduces local blood supply to the incision [ 6 ] , delays healing, and increases the risk of bacterial colonization. In this study, the infection rate in diabetic patients (36.4%) was significantly higher than that in non-diabetic patients (20.1%), further verifying the importance of blood glucose control in preventing postoperative infections. The mechanism by which high BMI exerts its influence is closely linked to a state of chronic inflammation. Patients with high BMI have increased visceral fat, which releases inflammatory factors such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) [ 14 ] , inhibiting immune responses. Additionally, mechanical compression from adipose tissue reduces blood perfusion at the incision [ 8 ] , decreases oxygen supply and nutrient delivery efficiency, and hinders collagen synthesis [ 15 ] . This study found that for each 1 kg/m² increase in BMI, the infection risk rises by 11%, consistent with the findings of Heus et al. [ 8 ] regarding the impact of body composition on the prognosis of colorectal cancer surgery, suggesting that preoperative weight management may reduce the infection risk. The harm of hypoproteinemia is mainly associated with a decline in tissue healing ability. Albumin is a core substance for maintaining plasma colloid osmotic pressure; a reduction in its level leads to incision edema and decreased fibroblast activity [ 16 ] . Moreover, hypoproteinemia is often accompanied by reduced immunoglobulin synthesis, further weakening anti-infective capacity. In this study, the proportion of patients with hypoproteinemia in the infection group (32.7%) was significantly higher than that in the non-infection group (20.3%), consistent with the conclusion proposed by Vincent et al. [ 16 ] that "hypoproteinemia is an independent predictor of postoperative complications". The high infection risk of emergency surgery is directly related to insufficient preoperative preparation. Emergency patients often seek medical attention due to tumor obstruction or bleeding, frequently lacking adequate intestinal preparation, and the surgery is performed in a potentially contaminated environment [ 17 ] . Additionally, in the emergency setting, patients experience a strong stress response, which suppresses immune function and further increases susceptibility to infection. In this study, the infection rate of emergency surgery (16.4%) was three times that of elective surgery (5.7%), consistent with the results of a multicenter study by Li et al. [ 17 ] on the infection risk of emergency abdominal surgery. The influence mechanism of incision direction is related to abdominal wall blood supply. Longitudinal incisions require cutting more longitudinally oriented abdominal wall blood vessels [ 9 ] , resulting in reduced local blood supply. In contrast, transverse incisions are more aligned with the anatomical course of abdominal wall blood vessels, providing richer blood supply, which is conducive to immune cell aggregation and incision healing. In this study, the infection rate of longitudinal incisions (8.3%) was significantly higher than that of transverse incisions (2.8%), consistent with the research conclusion of Al Dhaheri et al. [ 9 ] on the impact of incision site on wound complications in laparoscopic colorectal cancer surgery. Next, we analyze the innovative findings regarding the matching relationship between incision length and tumor size. The distinctive feature of this study is that it is the first to clarify the impact of the "difference between incision length and the shortest tumor diameter" on infection: when the incision length is ≤ the shortest tumor diameter, the infection rate reaches 9.1%; when the incision length exceeds the shortest tumor diameter by 1–2 cm, the infection rate drops to 4.3% (OR = 0.32). The mechanism behind this result may be related to two aspects: ① An excessively short incision may increase the risk of contamination by intestinal contents due to mechanical compression during tumor extraction [ 5 ] ; ② Moderately extending the incision (by 1–2 cm beyond the shortest tumor diameter) can reduce incision tension and avoid obstruction of local blood supply [ 9 ] . This provides a quantitative basis for clinical incision design—it is recommended that the incision length be set as "shortest tumor diameter + 1–2 cm" based on the tumor's shortest diameter measured by preoperative CT, balancing the convenience of surgical operation and the infection risk. Finally, we discuss the protective effect of prophylactic antibiotics and the clinical value of the prediction model. Preoperative prophylactic use of antibiotics can reduce the infection risk by 55% (OR = 0.45), consistent with the guideline recommendations by Bratzler et al. [ 7 ] . Antibiotics such as cefazolin and cefuroxime can effectively inhibit intestinal-derived gram-negative bacteria and anaerobes during surgery [ 18 ] , and administration 0.5-1 hour before surgery ensures that the local drug concentration at the incision reaches its peak during the operation [ 19 ] . In this study, the infection rate of patients who used antibiotics (4.7%) was significantly lower than that of non-users (8.5%), further verifying the importance of standardized use of prophylactic antibiotics. The nomogram model constructed based on multiple factors has an AUC of 0.76, a sensitivity of 0.70, and a specificity of 0.83, indicating that it can effectively identify high-risk patients. Compared with the SSI prediction model after colorectal cancer surgery constructed by Yang et al. [ 10 ] (AUC = 0.72), this model adds the characteristic variable of "difference between incision length and tumor size", which is more in line with the operational characteristics of laparoscopic surgery. Its visual risk scoring system (e.g., 20 points assigned for diabetes mellitus, 30 points for emergency surgery) facilitates rapid clinical assessment, can guide preoperative interventions (such as blood glucose control and optimized incision design) and postoperative monitoring, and reduce the incidence of infections. This study still has certain scientific limitations: ① The single-center retrospective design may have selection bias. Although the sample size (919 cases) can meet basic analysis needs, multicenter studies can further verify the universality of the model; ② Intraoperative factors (such as operation time and blood loss) and postoperative management (such as the frequency of incision dressing changes) were not included, which may have missed potential influencing factors [ 4 ] ; ③ The matching relationship between incision length and tumor size is based solely on preoperative CT measurements; in the future, it can be combined with intraoperative real-time evaluation to optimize incision design. In summary, this study identifies key risk factors for incisional infection after laparoscopic colorectal cancer surgery, and the constructed prediction model can provide a basis for personalized clinical prevention strategies. In the future, it is necessary to improve the model through multicenter prospective studies, explore the interaction of different factors, further enhance the safety of minimally invasive treatment for colorectal cancer, ultimately reduce the postoperative infection rate, and improve patient prognosis. Declarations Authors' Contributions BL and JCZ designed the study. WW participated in data analysis. JR conducted experiments and acquired data. LHW performed statistical analysis of experimental data. DRW supervised the research and revised the manuscript. All authors read and approved the final manuscript. Funding National Natural Science Foundation of China (82373014); Key Laboratory of Digestive and Metabolic Diseases Basic and Clinical Transformation (YZ2020159); Yangzhou "Lv yang Jin feng Plan" Talent Project (LYJF00050); Northern Jiangsu People's Hospital Doctoral Research Fund (BSQDJ0197). Data Availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. Ethical Approval All procedures were approved by the Ethics Committee of Northern Jiangsu People's Hospital Affiliated with Yangzhou University. Competing Interests The authors declare no competing interests. References Ma L, Yu H J, Zhu Y B, et al. Laparoscopy is non-inferior to open surgery for rectal cancer: A systematic review and meta-analysis[J]. Cancer Med, 2024, 13(13):e7363. Zhang J, Huang F, Niu R, et al. Short-term and long-term outcomes of laparoscopic surgery for locally recurrent rectal cancer: a propensity score-matched cohort study[J]. Tech Coloproctol, 2024, 28(1):100. Chang J, Karlsdottir B R, Phillips H L, et al. Modern Trends in Surgical Site Infection Rates for Colorectal Surgery: A National Surgical Quality Improvement Project Study 2013-2020[J]. Dis Colon Rectum, 2024, 67(9):1201-1209. Han C, Chen W, Ye X L, et al. 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JAMA Netw Open, 2023, 6(6):e2317370. Eckmann C, Aghdassi S J S, Brinkmann A, et al. Perioperative Antibiotic Prophylaxis—Indications and Modalities for the Prevention of Postoperative Wound Infection[J]. Dtsch Arztebl Int, 2024, 121(7):233-242. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 14 Nov, 2025 Editor invited by journal 06 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 01 Nov, 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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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Zou","suffix":""},{"id":549687115,"identity":"ee268bb3-f142-4575-8dd9-47daf3fbc376","order_by":2,"name":"Wei Wang","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated with Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":549687116,"identity":"874b20d8-c2af-4540-ae58-37471df542d5","order_by":3,"name":"Jun Ren","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated with Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ren","suffix":""},{"id":549687118,"identity":"fbb35927-bb72-44f4-8d16-d0891cee4b82","order_by":4,"name":"Liuhua Wang","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated with Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Liuhua","middleName":"","lastName":"Wang","suffix":""},{"id":549687119,"identity":"af020f89-fe84-4977-9fa4-d4abdba33962","order_by":5,"name":"Daorong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie3RMUvEMBTA8RcL7fLw1nSpX+GBUBEP7qukCHHJILgcOBxZ2qWHX8APoZOO1Qfq0A8g6FCXmzrceAc3mOLkkusomP/0CPkRkgCEQn+wOLHVWtE0m1XWdj9ryk8OkV+gm+tjQmYaRTKpteja5+LODXIUibHNu6JsxIM0+TXuGCaJIdg++u5Sn1BRfkandZ9/IDKkdU9i2XpPIVmUqxhejSOSgd4NRaL0EGkGwgiNya+QGGb7ibu1alnSm9YRKneK3EeGt1VzTWnNnN42Fyjb1eXT0kOO3A9+bWi6uEmsXfe7s2xSnd93Ww/51QEC4DA0IwGA2IzeGgqFQv+pbz5EVrCUgwCrAAAAAElFTkSuQmCC","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated with Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Daorong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-11-02 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07:03:10","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71335,"visible":true,"origin":"","legend":"","description":"","filename":"a3ce61db18ca4d539b087bbf72f67b0f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8008648/v1/2d0e2ec82ec5824901d25a2a.xml"},{"id":96792351,"identity":"02cc921d-8842-486a-977a-656659e67e78","added_by":"auto","created_at":"2025-11-26 07:03:10","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76695,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8008648/v1/91f824870f8318d8556844c2.html"},{"id":96792332,"identity":"97339549-cd54-498f-9b4d-af24dc6abdf0","added_by":"auto","created_at":"2025-11-26 07:03:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":153407,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of univariate analysis. Note: 0 indicates the presence of hypertension, diabetes mellitus, hypoproteinemia, elective surgery, or no antibiotics used; For the relationship between incision length and the shortest tumor diameter: 0 indicates incision length ≤ the shortest tumor diameter; 1 indicates the shortest tumor diameter \u0026lt; incision length ≤ the shortest tumor diameter + 1 cm; 2 indicates the shortest tumor diameter + 1 cm \u0026lt; incision length ≤ the shortest tumor diameter + 2 cm; 3 indicates incision length \u0026gt; the shortest tumor diameter + 2 cm; OR \u0026gt; 1 indicates a risk factor, and OR \u0026lt; 1 indicates a protective factor.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8008648/v1/ed58395b0914c2c68544d057.png"},{"id":96792334,"identity":"3f4bf558-fa2a-4a9f-aecc-2c85fd185e8b","added_by":"auto","created_at":"2025-11-26 07:03:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151055,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multivariate analysis. Note: 0 indicates the presence of hypertension, diabetes mellitus, hypoproteinemia, elective surgery, or no antibiotics used; For the relationship between incision length and the shortest tumor diameter: 0 indicates incision length ≤ the shortest tumor diameter; 1 indicates the shortest tumor diameter \u0026lt; incision length ≤ the shortest tumor diameter + 1 cm; 2 indicates the shortest tumor diameter + 1 cm \u0026lt; incision length ≤ the shortest tumor diameter + 2 cm; 3 indicates incision length \u0026gt; the shortest tumor diameter + 2 cm.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8008648/v1/2d01cbf19d3ad6df0ce18914.png"},{"id":96792333,"identity":"7afb681e-8c86-406b-97f6-6494fe803e61","added_by":"auto","created_at":"2025-11-26 07:03:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73410,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for Predicting Incision Infection\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8008648/v1/792f20a7f212e3a9b758a48b.png"},{"id":96792338,"identity":"7451e613-a503-4296-a21f-8aeb57a2a5af","added_by":"auto","created_at":"2025-11-26 07:03:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57318,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the surgical site infection prediction model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8008648/v1/ea2b6f0ca2e36235868a2000.png"},{"id":96792339,"identity":"0236a17d-bf0d-49eb-9647-120b61ec9dd5","added_by":"auto","created_at":"2025-11-26 07:03:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":51667,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the surgical site infection prediction model. The dotted line represents perfect calibration. The solid line represents the performance of the nomogram. The closer the solid line is to the dotted line, the better the calibration.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8008648/v1/09678194c13c34027a649071.png"},{"id":97136200,"identity":"5e3a7273-45c4-465b-ab03-7b362f026ebb","added_by":"auto","created_at":"2025-12-01 09:56:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1064461,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008648/v1/5ce00496-8089-44df-86fa-792c56851fb9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Influencing Factors for Incisional Surgical Site Infection after Laparoscopic Colorectal Cancer Surgery and Construction of a Prediction Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC), as one of the most common malignant tumors worldwide, has seen continuous innovations in its treatment methods with the development of minimally invasive technologies. Laparoscopic surgery, leveraging advantages such as minimal trauma, rapid recovery, and short hospital stays, has become the preferred surgical approach for radical resection of colorectal cancer\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Compared with traditional open surgery, laparoscopic surgery can reduce postoperative pain and alleviate systemic inflammatory responses. However, postoperative surgical site infection (SSI) remains a critical clinical challenge affecting patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccording to epidemiological data, the incidence of SSI after laparoscopic colorectal cancer surgery is approximately 1%-10%. It not only prolongs the patient's hospital stay and increases medical costs but also may lead to severe complications such as intensive care and reoperation, and even endanger life\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Among them, incisional surgical site infection (I-SSI), as an important type of SSI, is directly related to surgical operations and incision management. Its pathogenesis involves multiple factors, including the patient's basic condition, surgical design, and perioperative management. Existing studies have confirmed that diabetes, obesity, preoperative use of antibiotics, surgical type, etc., are associated with SSI\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, these studies mostly focus on single-factor analysis, lacking a systematic exploration of the interaction of multiple factors. In particular, research on the \"matching relationship between incision length and tumor size\"\u0026mdash;a characteristic variable in laparoscopic surgery\u0026mdash;is still insufficient\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs a key operational parameter in laparoscopic surgery, the rationality of surgical incision length directly affects incision healing and infection risk: an excessively long incision may increase the chance of bacterial colonization, while an excessively short one may raise the risk of contamination due to mechanical compression during tumor extraction\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, there is currently no consensus on the impact of the quantitative relationship between incision length and tumor size on I-SSI, and there is a lack of risk prediction tools that integrate multiple factors, making it difficult to guide the formulation of personalized clinical surgical plans.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to systematically explore the risk factors for incisional infection after laparoscopic colorectal cancer surgery through retrospective analysis, focusing on clarifying the impact of the association between incision length and tumor size on infection, and constructing a prediction model based on the screened key factors. By collecting a large amount of clinicopathological data and using statistical methods to identify independent risk factors, the study ultimately provides a scientific basis for clinically identifying high-risk patients, optimizing incision design, and formulating personalized prevention strategies, in order to reduce the incidence of postoperative incisional infection, improve patient prognosis, provide quantitative standards for incision design, and further promote the standardized application of minimally invasive treatment technologies for colorectal cancer\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Selection and Criteria of Study Subjects\u003c/h2\u003e\u003cp\u003eClinical data of patients who underwent laparoscopic radical resection of colorectal cancer in the Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University from February 2018 to February 2022 were retrospectively collected.\u003c/p\u003e\u003cp\u003eInclusion criteria: ① Aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years; ② Pathologically confirmed as colon cancer or rectal cancer after surgery; ③ Surgical method was laparoscopic radical resection of colorectal cancer (including laparoscopically assisted or totally laparoscopic surgery); ④ Complete clinicopathological data (including baseline data, surgical records, postoperative follow-up records, etc.).\u003c/p\u003e\u003cp\u003eExclusion criteria: ① Converted to open surgery during operation or failed to complete the scheduled surgical plan; ② Complicated with severe infectious diseases (such as sepsis, abscess, etc.) or immunodeficiency diseases (such as AIDS, long-term use of immunosuppressants, etc.); ③ Complicated with severe liver and kidney dysfunction (Child-Pugh grade C); ④ Received neoadjuvant chemoradiotherapy before surgery; ⑤ Distant tumor metastasis (stage Ⅳ) or combined with other malignant tumors; ⑥ Missing clinical data or interrupted follow-up.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Data Collection\u003c/h2\u003e\u003cp\u003eThe following information was extracted through the hospital electronic medical record system: Baseline data: age, gender, body mass index (BMI, kg/m\u0026sup2;); past medical history (hypertension, diabetes, hypoproteinemia, where hypoproteinemia was defined as preoperative serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;35g/L). Collection of all patients' clinical data obtained informed consent and complied with the requirements of the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003ePreoperative preparation: ① Tumor size: the shortest diameter of the tumor was measured by preoperative enhanced CT (measurement method: the phase with the best contrast after enhancement was selected, sagittal or coronal reconstruction was performed, and measurement was conducted at the smallest layer of the lesion; measured independently by 2 attending radiologists, with intraclass correlation coefficient (ICC)\u0026thinsp;\u0026ge;\u0026thinsp;0.85 to ensure consistency); ② Surgical type (elective/emergency, emergency surgery was defined as surgery required within 24 hours due to tumor obstruction, bleeding, etc.); ③ Use of prophylactic antibiotics (intravenous drip 0.5-1.0 hours before surgery; drug selection: cefazolin 1g or cefuroxime 1g\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e; levofloxacin 0.5g was used for patients allergic to cephalosporins, and whether the administration timing was standardized was recorded).\u003c/p\u003e\u003cp\u003eSurgical conditions: surgical site (colon/rectum), direction of auxiliary incision (longitudinal incision/transverse incision) (longitudinal incision refers to the incision parallel to the midline of the abdomen, and transverse incision refers to the incision perpendicular to the midline of the abdomen), incision length (actual incision length recorded during surgery, accurate to 0.1cm).\u003c/p\u003e\u003cp\u003ePrimary outcome indicator: postoperative incisional infection (I-SSI). The diagnostic criteria refer to the 2017 Guidelines for the Prevention of Surgical Site Infection by the US Centers for Disease Control and Prevention (CDC)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e: any of the following conditions occurring at the incision site within 30 days after surgery: ① Pus is drained; ② Pathogens are detected in fluid/tissue culture; ③ Presence of infection signs (pain, tenderness, swelling, redness, fever); ④ The incision spontaneously dehisces or is opened by a surgeon due to infection signs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Statistical Methods and Model Construction\u003c/h2\u003e\u003cp\u003eData processing: SPSS 27.0 software was used for analysis. Normality of quantitative data was first tested by Shapiro-Wilk test. Those conforming to normal distribution were expressed as (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and independent sample t-test was used for comparison between groups; non-normally distributed data were expressed as M (P25, P75), and Mann-Whitney U test was used for comparison between groups. Qualitative data were expressed as [cases (%)], and chi-square test or Fisher's exact probability method (when n\u0026thinsp;\u0026lt;\u0026thinsp;5) was used for comparison between groups.\u003c/p\u003e\u003cp\u003eScreening of risk factors: ① Univariate analysis: variables that may affect I-SSI (age, gender, BMI, underlying diseases, etc.) were included one by one, and variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 entered multivariate analysis; ② Multivariate Logistic regression: stepwise regression (αin\u0026thinsp;=\u0026thinsp;0.05, αout\u0026thinsp;=\u0026thinsp;0.10) was used to screen independent risk factors, and odds ratio (OR) and 95% confidence interval (95% CI) were calculated.\u003c/p\u003e\u003cp\u003eModel construction and evaluation: A nomogram prediction model was constructed using R 4.2.2 software (rms package). The weight of nomogram variables was determined based on multivariate Logistic regression coefficients, and Bootstrap sampling was used for internal validation. The following indicators were used to evaluate model performance: ① Discrimination: a receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC) was calculated. AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 indicated that the model had certain discrimination ability; ② Calibration: a calibration curve was drawn using Bootstrap method (1000 repeated samplings), and Harrell's concordance index (C-index) was calculated. A C-index closer to 1 indicated better calibration effect. Significance level: all tests were two-tailed, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Clinicopathological Characteristics of Patients and Incidence of Postoperative Incisional Infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 919 patients who underwent laparoscopic radical resection of colorectal cancer were included in this study, among whom 55 developed postoperative incisional infection, with an overall infection incidence of 6.0%. The analysis of baseline characteristics of patients and their association with incisional infection is shown in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eDemographics and underlying diseases: There were no statistically significant differences between the infection group and the non-infection group in age (62.3\u0026plusmn;11.2 years vs 60.9\u0026plusmn;13.6 years, t=0.570, P=0.570), gender (male proportion: 56.4% vs 59.4%, \u0026chi;\u0026sup2;=0.194, P=0.660), and complicated hypertension (43.6% vs 43.1%, \u0026chi;\u003csup\u003e2\u003c/sup\u003e=0.007, P=0.933). However, the proportions of complicated diabetes (36.4% vs 20.1%, \u0026chi;\u0026sup2;=8.174, P=0.004) and hypoproteinemia (32.7% vs 20.3%, \u0026chi;\u0026sup2;=4.849, P=0.028) in the infection group were significantly higher, and BMI was higher (26.3\u0026plusmn;4.2 kg/m\u0026sup2; vs 25.0\u0026plusmn;4.6 kg/m\u0026sup2;, t=2.046, P=0.041).\u003c/p\u003e\n\u003cp\u003eSurgery-related factors: The proportions of emergency surgery (16.4% vs 6.7%, \u0026chi;\u0026sup2;=7.125, P=0.008) and longitudinal incision (83.6% vs 69.0%, \u0026chi;\u0026sup2;=5.274, P=0.022) in the infection group were significantly higher than those in the non-infection group. The infection rate of longitudinal incision (46/596\u0026asymp;7.7%) was significantly higher than that of transverse incision (9/268\u0026asymp;3.4%). The proportion of preoperative prophylactic use of antibiotics was lower (52.7% vs 67.7%, \u0026chi;\u0026sup2;=5.234, P=0.022). The shortest diameter of the tumor measured by preoperative CT in the infection group was larger (4.4\u0026plusmn;1.6 cm vs 3.6\u0026plusmn;1.3 cm, t=4.19, P\u0026lt;0.001), and there was a significant difference in the matching relationship between incision length and the shortest tumor diameter: the proportion of \u0026quot;incision length \u0026le; shortest tumor diameter\u0026quot; in the infection group was higher (41.8% vs 22.8%), while the proportion of \u0026quot;incision length \u0026gt; shortest tumor diameter by more than 2 cm\u0026quot; was lower (23.6% vs 34.7%), with \u0026chi;\u0026sup2;=11.222 and P=0.011 in the distribution of differences between the two groups.\u003c/p\u003e\n\u003cp\u003eOther factors: There were no statistically significant differences in surgical site (colon vs rectum) and absolute value of incision length (5.8\u0026plusmn;2.7 cm vs 5.4\u0026plusmn;1.8 cm, t=1.77, P=0.077) between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Analysis of baseline characteristics of patients and their association with incisional infection\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eI-SSI Group (n=55)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNon-I-SSI Group (n=864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003et / \u0026chi;\u0026sup2;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAge (x̄ \u0026plusmn; s, years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e62.3\u0026plusmn;11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e60.9\u0026plusmn;13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e31\u0026nbsp;(56.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e513 (59.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e24 (43.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e351 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e24(43.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e372 (43.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e20 (36.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e174 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHypoproteinemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e18 (32.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e175 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;, x̄ \u0026plusmn; s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e26.3\u0026plusmn;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e25.0\u0026plusmn;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSurgical Site, n (%) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eColon\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29 (52.7%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (47.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e477 (55.2%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e387 (44.8%) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIncision Orientation, n (%)\u003c/p\u003e\n \u003cp\u003eVertical\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Transverse\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIncision Length (x̄ \u0026plusmn; s, cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46 (83.6%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (16.4%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.8\u0026plusmn;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e596 (69.0%)\u003c/p\u003e\n \u003cp\u003e268 (31.0%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.4\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.274\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSurgery Type, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Emergency\u003c/p\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (16.4%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46 (83.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58 (6.7%)\u003c/p\u003e\n \u003cp\u003e806 (93.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eProphylactic Antibiotics, n (%) \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29 (52.7%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (47.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e585 (67.7%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e279 (32.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRadiological Tumor Size (cm, x̄ \u0026plusmn; s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4.4\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.6\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIncision Length - Min Tumor Diameter, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026le;0（cm）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0-1（cm）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1-2（cm）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ＞2（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23 (41.8%)\u003c/p\u003e\n \u003cp\u003e11 (20.0%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;8 (14.5%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;13 (23.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e197 (22.8%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e171 (19.8%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e196 (22.7%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e300 (34.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Hypoproteinemia was defined as preoperative serum albumin \u0026lt;35g/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Analysis of Risk Factors for Postoperative Incisional Infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis (\u003cstrong\u003eFigure 1\u003c/strong\u003e) showed that factors significantly associated with incisional infection included: complicated diabetes (OR=2.27, 95% CI=1.28-4.02, P=0.005), hypoproteinemia (OR=1.92, 95% CI=1.06-3.45, P=0.030), high BMI (OR=1.07, 95% CI=1.01-1.14, P=0.042), and longitudinal incision (OR=2.30, 95% CI=1.11-4.76, P=0.025). Elective surgery (OR=0.37, 95% CI=0.17-0.79, P=0.010), preoperative prophylactic use of antibiotics (OR=0.53, 95% CI=0.31-0.92, P=0.024), and incision length \u0026gt; shortest tumor diameter (especially with a difference of 1-2 cm: OR=0.35, 95% CI=0.15-0.80, P=0.013; difference \u0026gt;2 cm: OR=0.37, 95% CI=0.18-0.75, P=0.006) were protective factors. Gender, age, complicated hypertension, and surgical site were not significantly associated with infection (P\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eMultivariate Logistic regression analysis (\u003cstrong\u003eFigure 2\u003c/strong\u003e) further showed that the following factors were independent risk factors for postoperative incisional infection (P\u0026lt;0.05): complicated diabetes (OR=2.05, 95% CI=1.10-3.79, P=0.023), hypoproteinemia (OR=3.52, 95% CI=1.73-7.17, P\u0026lt;0.001), high BMI (OR=1.10, 95% CI=1.02-1.19, P=0.011), and longitudinal incision (OR=2.32, 95% CI=1.09-4.92, P=0.029).\u003c/p\u003e\n\u003cp\u003eElective surgery (OR=0.35, 95% CI=0.16-0.80, P=0.012), preoperative prophylactic use of antibiotics (OR=0.44, 95% CI=0.24-0.78, P=0.005), and incision length \u0026gt; shortest tumor diameter (difference of 1-2 cm: OR=0.35, 95% CI=0.15-0.81, P=0.015; difference \u0026gt;2 cm: OR=0.36, 95% CI=0.17-0.75, P=0.007) remained independent protective factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Construction of Nomogram Prediction Model for Postoperative Incisional Infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the independent risk factors screened by multivariate regression (diabetes, hypoproteinemia, BMI, surgical type, incision direction, prophylactic antibiotics, difference between incision length and shortest tumor diameter), a nomogram prediction model was constructed using R software (\u003cstrong\u003eFigure 3\u003c/strong\u003e). The model calculates the infection risk through the following steps: each variable corresponds to a score on the \u0026quot;Points\u0026quot; axis (e.g., BMI=26 kg/m\u0026sup2; corresponds to 10 points, diabetes (yes) corresponds to 20 points); the total score of all variables is summed to obtain \u0026quot;Total Points\u0026quot;; the predicted probability of postoperative incisional infection (ranging from 0.1 to 0.5) is obtained from the \u0026quot;Risk\u0026quot; axis corresponding to \u0026quot;Total Points\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Validation of the Nomogram Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis of the model showed that the AUC was 0.76 (95% CI=0.70-0.82), indicating moderate discrimination ability; the sensitivity was 0.70 (i.e., 70% of infected patients could be correctly identified), and the specificity was 0.83 (i.e., 83% of non-infected patients could be correctly excluded), suggesting high predictability of the model (\u003cstrong\u003eFigure 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eCalibration: Bootstrap validation (1000 samplings) showed that the curve of predicted risk and actual infection probability in the calibration chart was close to the ideal diagonal (\u003cstrong\u003eFigure 5\u003c/strong\u003e), with a Harrell C-index of 0.73, indicating good consistency between the predicted values of the model and the actual results.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLaparoscopic radical resection of colorectal cancer has become the mainstream surgical procedure for the treatment of colorectal cancer; however, postoperative incisional surgical site infection (I-SSI) remains a significant issue affecting prognosis. Through the analysis of 919 patients, this study identified diabetes mellitus, high BMI, hypoproteinemia, emergency surgery, and longitudinal incision as independent risk factors for postoperative incisional infection. In contrast, preoperative prophylactic use of antibiotics and an incision length greater than the shortest tumor diameter (especially with a difference of 1\u0026ndash;2 cm) were identified as protective factors. A nomogram model with good predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.76) was constructed based on these factors.\u003c/p\u003e\u003cp\u003eFirst, we discuss the clinical significance and mechanism analysis of each independent risk factor. Diabetes mellitus, a confirmed high-risk factor in this study, is consistent with the conclusions of previous studies\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The hyperglycemic state in diabetic patients can impair the body's anti-infective capacity by inhibiting leukocyte recruitment, reducing neutrophil phagocytic function, and decreasing antibody activity\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, hyperglycemia-induced microangiopathy reduces local blood supply to the incision\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, delays healing, and increases the risk of bacterial colonization. In this study, the infection rate in diabetic patients (36.4%) was significantly higher than that in non-diabetic patients (20.1%), further verifying the importance of blood glucose control in preventing postoperative infections.\u003c/p\u003e\u003cp\u003eThe mechanism by which high BMI exerts its influence is closely linked to a state of chronic inflammation. Patients with high BMI have increased visceral fat, which releases inflammatory factors such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, inhibiting immune responses. Additionally, mechanical compression from adipose tissue reduces blood perfusion at the incision\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, decreases oxygen supply and nutrient delivery efficiency, and hinders collagen synthesis\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. This study found that for each 1 kg/m\u0026sup2; increase in BMI, the infection risk rises by 11%, consistent with the findings of Heus et al.\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e regarding the impact of body composition on the prognosis of colorectal cancer surgery, suggesting that preoperative weight management may reduce the infection risk.\u003c/p\u003e\u003cp\u003eThe harm of hypoproteinemia is mainly associated with a decline in tissue healing ability. Albumin is a core substance for maintaining plasma colloid osmotic pressure; a reduction in its level leads to incision edema and decreased fibroblast activity\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Moreover, hypoproteinemia is often accompanied by reduced immunoglobulin synthesis, further weakening anti-infective capacity. In this study, the proportion of patients with hypoproteinemia in the infection group (32.7%) was significantly higher than that in the non-infection group (20.3%), consistent with the conclusion proposed by Vincent et al.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e that \"hypoproteinemia is an independent predictor of postoperative complications\".\u003c/p\u003e\u003cp\u003eThe high infection risk of emergency surgery is directly related to insufficient preoperative preparation. Emergency patients often seek medical attention due to tumor obstruction or bleeding, frequently lacking adequate intestinal preparation, and the surgery is performed in a potentially contaminated environment\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Additionally, in the emergency setting, patients experience a strong stress response, which suppresses immune function and further increases susceptibility to infection. In this study, the infection rate of emergency surgery (16.4%) was three times that of elective surgery (5.7%), consistent with the results of a multicenter study by Li et al.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e on the infection risk of emergency abdominal surgery.\u003c/p\u003e\u003cp\u003eThe influence mechanism of incision direction is related to abdominal wall blood supply. Longitudinal incisions require cutting more longitudinally oriented abdominal wall blood vessels\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, resulting in reduced local blood supply. In contrast, transverse incisions are more aligned with the anatomical course of abdominal wall blood vessels, providing richer blood supply, which is conducive to immune cell aggregation and incision healing. In this study, the infection rate of longitudinal incisions (8.3%) was significantly higher than that of transverse incisions (2.8%), consistent with the research conclusion of Al Dhaheri et al.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e on the impact of incision site on wound complications in laparoscopic colorectal cancer surgery.\u003c/p\u003e\u003cp\u003eNext, we analyze the innovative findings regarding the matching relationship between incision length and tumor size. The distinctive feature of this study is that it is the first to clarify the impact of the \"difference between incision length and the shortest tumor diameter\" on infection: when the incision length is \u0026le;\u0026thinsp;the shortest tumor diameter, the infection rate reaches 9.1%; when the incision length exceeds the shortest tumor diameter by 1\u0026ndash;2 cm, the infection rate drops to 4.3% (OR\u0026thinsp;=\u0026thinsp;0.32). The mechanism behind this result may be related to two aspects: ① An excessively short incision may increase the risk of contamination by intestinal contents due to mechanical compression during tumor extraction\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e; ② Moderately extending the incision (by 1\u0026ndash;2 cm beyond the shortest tumor diameter) can reduce incision tension and avoid obstruction of local blood supply\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. This provides a quantitative basis for clinical incision design\u0026mdash;it is recommended that the incision length be set as \"shortest tumor diameter\u0026thinsp;+\u0026thinsp;1\u0026ndash;2 cm\" based on the tumor's shortest diameter measured by preoperative CT, balancing the convenience of surgical operation and the infection risk.\u003c/p\u003e\u003cp\u003eFinally, we discuss the protective effect of prophylactic antibiotics and the clinical value of the prediction model. Preoperative prophylactic use of antibiotics can reduce the infection risk by 55% (OR\u0026thinsp;=\u0026thinsp;0.45), consistent with the guideline recommendations by Bratzler et al.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Antibiotics such as cefazolin and cefuroxime can effectively inhibit intestinal-derived gram-negative bacteria and anaerobes during surgery\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and administration 0.5-1 hour before surgery ensures that the local drug concentration at the incision reaches its peak during the operation\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In this study, the infection rate of patients who used antibiotics (4.7%) was significantly lower than that of non-users (8.5%), further verifying the importance of standardized use of prophylactic antibiotics.\u003c/p\u003e\u003cp\u003eThe nomogram model constructed based on multiple factors has an AUC of 0.76, a sensitivity of 0.70, and a specificity of 0.83, indicating that it can effectively identify high-risk patients. Compared with the SSI prediction model after colorectal cancer surgery constructed by Yang et al.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e (AUC\u0026thinsp;=\u0026thinsp;0.72), this model adds the characteristic variable of \"difference between incision length and tumor size\", which is more in line with the operational characteristics of laparoscopic surgery. Its visual risk scoring system (e.g., 20 points assigned for diabetes mellitus, 30 points for emergency surgery) facilitates rapid clinical assessment, can guide preoperative interventions (such as blood glucose control and optimized incision design) and postoperative monitoring, and reduce the incidence of infections.\u003c/p\u003e\u003cp\u003eThis study still has certain scientific limitations: ① The single-center retrospective design may have selection bias. Although the sample size (919 cases) can meet basic analysis needs, multicenter studies can further verify the universality of the model; ② Intraoperative factors (such as operation time and blood loss) and postoperative management (such as the frequency of incision dressing changes) were not included, which may have missed potential influencing factors\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e; ③ The matching relationship between incision length and tumor size is based solely on preoperative CT measurements; in the future, it can be combined with intraoperative real-time evaluation to optimize incision design.\u003c/p\u003e\u003cp\u003eIn summary, this study identifies key risk factors for incisional infection after laparoscopic colorectal cancer surgery, and the constructed prediction model can provide a basis for personalized clinical prevention strategies. In the future, it is necessary to improve the model through multicenter prospective studies, explore the interaction of different factors, further enhance the safety of minimally invasive treatment for colorectal cancer, ultimately reduce the postoperative infection rate, and improve patient prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBL and JCZ designed the study. WW participated in data analysis. JR conducted experiments and acquired data. LHW performed statistical analysis of experimental data. DRW supervised the research and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (82373014); Key Laboratory of Digestive and Metabolic Diseases Basic and Clinical Transformation (YZ2020159); Yangzhou \"Lv yang Jin feng Plan\" Talent Project (LYJF00050); Northern Jiangsu People's Hospital Doctoral Research Fund (BSQDJ0197).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were approved by the Ethics Committee of Northern Jiangsu People's Hospital Affiliated with Yangzhou University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMa L, Yu H J, Zhu Y B, et al. Laparoscopy is non-inferior to open surgery for rectal cancer: A systematic review and meta-analysis[J]. Cancer Med, 2024, 13(13):e7363.\u003c/li\u003e\n\u003cli\u003eZhang J, Huang F, Niu R, et al. Short-term and long-term outcomes of laparoscopic surgery for locally recurrent rectal cancer: a propensity score-matched cohort study[J]. Tech Coloproctol, 2024, 28(1):100.\u003c/li\u003e\n\u003cli\u003eChang J, Karlsdottir B R, Phillips H L, et al. Modern Trends in Surgical Site Infection Rates for Colorectal Surgery: A National Surgical Quality Improvement Project Study 2013-2020[J]. Dis Colon Rectum, 2024, 67(9):1201-1209.\u003c/li\u003e\n\u003cli\u003eHan C, Chen W, Ye X L, et al. Risk factors analysis of surgical site infections in postoperative colorectal cancer: a nine-year retrospective study[J]. BMC Surg, 2023, 23(1):320.\u003c/li\u003e\n\u003cli\u003eNi L T, Zhao R, Ye Y R, et al. Incidence of surgical site infection in minimally invasive colorectal surgery[J]. World J Gastrointest Surg, 2024, 16(4):1121-1129.\u003c/li\u003e\n\u003cli\u003eElsayed N A, Aleppo G, Aroda V R, et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023. Diabetes Care 2023;46(Suppl. 1):S19-S40[J]. Diabetes Care, 2023, 46(9):1715.\u003c/li\u003e\n\u003cli\u003eBratzler D W, Dellinger E P, Olsen K M, et al. Clinical practice guidelines for antimicrobial prophylaxis in surgery[J]. Am J Health Syst Pharm, 2013, 70(3):195-283.\u003c/li\u003e\n\u003cli\u003eHeus C, Bakker N, Verduin W M, et al. Impact of Body Composition on Surgical Outcome in Rectal Cancer Patients, a Retrospective Cohort Study[J]. World J Surg, 2019, 43(5):1370-1376.\u003c/li\u003e\n\u003cli\u003eAl Dhaheri M, Ibrahim M, Al-Yahri O, et al. Choice of specimen\u0026apos;s extraction site affects wound morbidity in laparoscopic colorectal cancer surgery[J]. Langenbecks Arch Surg, 2022, 407(8):3561-3565.\u003c/li\u003e\n\u003cli\u003eYang Y Y, Zhang X F, Zhu J W, et al. [Establishment and validation of a predictive clinical model for postoperative surgical site infection in patients with colorectal surgery][J]. Zhonghua Wei Chang Wai Ke Za Zhi, 2023, 26(9):837-846.\u003c/li\u003e\n\u003cli\u003eMangram A J, Horan T C, Pearson M L, et al. Guideline for Prevention of Surgical Site Infection, 1999. Centers for Disease Control and Prevention (CDC) Hospital Infection Control Practices Advisory Committee[J]. Am J Infect Control, 1999, 27(2):97-132; quiz 133-4; discussion 96.\u003c/li\u003e\n\u003cli\u003eHoran T C, Andrus M, Dudeck M A. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting[J]. Am J Infect Control, 2008, 36(5):309-32.\u003c/li\u003e\n\u003cli\u003eBerbudi A, Rahmadika N, Tjahjadi A I, et al. Type 2 Diabetes and its Impact on the Immune System[J]. Curr Diabetes Rev, 2020, 16(5):442-449.\u003c/li\u003e\n\u003cli\u003eRohm T V, Meier D T, Olefsky J M, et al. Inflammation in obesity, diabetes, and related disorders[J]. Immunity, 2022, 55(1):31-55.\u003c/li\u003e\n\u003cli\u003eWu Y W, Chen J W, Tsai H Y, et al. Inhibition of Adipocyte-Derived FABP4 Reduces Adipocyte Inflammation, Improves Angiogenesis, and Facilitates Wound Healing in Metabolic Dysfunctions[J]. J Invest Dermatol, 2025, 145(4):939-953.\u003c/li\u003e\n\u003cli\u003eVincent J L, Dubois M J, Navickis R J, et al. Hypoalbuminemia in acute illness: is there a rationale for intervention? A meta-analysis of cohort studies and controlled trials[J]. Ann Surg, 2003, 237(3):319-34.\u003c/li\u003e\n\u003cli\u003eLi Z, Gao J R, Song L, et al. [Risk factors for surgical site infection after emergency abdominal surgery: a multicenter cross-sectional study in China][J]. Zhonghua Wei Chang Wai Ke Za Zhi, 2020, 23(11):1043-1050.\u003c/li\u003e\n\u003cli\u003eSommerstein R, Troillet N, Harbarth S, et al. Timing of Cefuroxime Surgical Antimicrobial Prophylaxis and Its Association With Surgical Site Infections[J]. JAMA Netw Open, 2023, 6(6):e2317370.\u003c/li\u003e\n\u003cli\u003eEckmann C, Aghdassi S J S, Brinkmann A, et al. Perioperative Antibiotic Prophylaxis\u0026mdash;Indications and Modalities for the Prevention of Postoperative Wound Infection[J]. Dtsch Arztebl Int, 2024, 121(7):233-242.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Laparoscopy, Colorectal cancer, Incisional surgical site infection (I-SSI), Risk factors, Prediction model, Incision length","lastPublishedDoi":"10.21203/rs.3.rs-8008648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8008648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) is a common malignant tumor worldwide. Laparoscopic surgery has become the preferred treatment due to its minimally invasive advantages, but postoperative incisional surgical site infection (I-SSI) still significantly affects patient prognosis. This study aimed to analyze the risk factors for incisional infection after laparoscopic colorectal cancer surgery and construct a risk prediction model to provide a basis for precise clinical prevention. Clinical data of 919 patients who underwent laparoscopic radical resection of colorectal cancer at Northern Jiangsu People's Hospital from February 2018 to February 2022 were retrospectively collected. Univariate and multivariate Logistic regression were used to screen infection-related risk factors. A nomogram prediction model was constructed using R software, and the model performance was evaluated by ROC curve and calibration curve. Results showed that the incidence of incisional infection among 919 patients was 6.0%. Multivariate analysis indicated that diabetes mellitus (OR\u0026thinsp;=\u0026thinsp;2.05), hypoproteinemia (OR\u0026thinsp;=\u0026thinsp;3.52), high BMI (OR\u0026thinsp;=\u0026thinsp;1.10), emergency surgery (OR\u0026thinsp;=\u0026thinsp;0.35), and longitudinal incision (OR\u0026thinsp;=\u0026thinsp;2.32) were independent risk factors, while preoperative prophylactic use of antibiotics (OR\u0026thinsp;=\u0026thinsp;0.44) and incision length greater than the shortest tumor diameter (especially with a difference of 1\u0026ndash;2 cm, OR\u0026thinsp;=\u0026thinsp;0.35) were protective factors. The constructed nomogram model had an AUC of 0.76 (95% CI\u0026thinsp;=\u0026thinsp;0.70\u0026ndash;0.82), with a sensitivity of 0.70 and specificity of 0.83, and good calibration effect. This study identifies key influencing factors for incisional infection after laparoscopic colorectal cancer surgery, and the established nomogram model has moderate predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.76), providing a scientific basis for personalized surgical planning and infection prevention.\u003c/p\u003e","manuscriptTitle":"Analysis of Influencing Factors for Incisional Surgical Site Infection after Laparoscopic Colorectal Cancer Surgery and Construction of a Prediction Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 07:03:05","doi":"10.21203/rs.3.rs-8008648/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-03T02:25:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284953398843734646734160420961266453053","date":"2025-12-02T07:21:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-14T07:08:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-06T13:38:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T15:04:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T15:03:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-02T03:31:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"145ddddb-e637-4cbd-b6db-a1cce91768c5","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58484015,"name":"Biological sciences/Cancer"},{"id":58484016,"name":"Health sciences/Diseases"},{"id":58484017,"name":"Health sciences/Gastroenterology"},{"id":58484018,"name":"Health sciences/Medical research"},{"id":58484019,"name":"Health sciences/Oncology"},{"id":58484020,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-26T07:03:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 07:03:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8008648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8008648","identity":"rs-8008648","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}