A Nomogram Model for Early Prediction of Postoperative Intra-Abdominal Infection in Colorectal Cancer patients Based on Perioperative Clinical Variables 

A Nomogram Model for Early Prediction of Postoperative Intra-Abdominal Infection in Colorectal Cancer patients Based on Perioperative Clinical Variables | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Nomogram Model for Early Prediction of Postoperative Intra-Abdominal Infection in Colorectal Cancer patients Based on Perioperative Clinical Variables Yunzhe Yu, Sida Sun, Jiansheng Chen, Liqun Liao, Junfeng Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7179766/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To establish a predictive nomogram model for Postoperative Intra-Abdominal Infection (PIAI) following Colorectal Cancer (CRC) surgery using perioperative clinical variables, thereby facilitating early identification of high-risk patients and enhance postoperative management. Method This retrospective cohort study included colorectal cancer patients undergoing surgery from 2022 to 2024 at a single center. Perioperative clinical and laboratory data, along with blood glucose levels from the day of surgery to postoperative day 3, were collected. PIAI was defined according to the Centers for Disease Control and Prevention (CDC) criteria. Blood glucose trajectories were identified using latent class mixed modeling(LCMM). LASSO and logistic regression analyses were used to select risk factors for PIAI. A predictive nomogram was constructed and internally validated by calibration curve, ROC curve analysis (AUC), decision curve analysis (DCA), and clinical impact curves (CIC). Result A total of 197 patients' data were collected, and 163 patients were finally included in the study. The incidence of PIAI in the cohort was 17.8%. Compared with patients without PIAI, those who developed infection had significantly higher rates of NRS2002 ≥ 3 (62.1% vs. 38.1%, P = 0.018), PGSGA ≥ 4 (72.4% vs. 41.0%, P = 0.002), ASA grade ≥ 3 (17.2% vs 6.0%, P = 0.042) and preoperative antibiotic use (10.3% vs 1.5%, P = 0.012), as well as greater intraoperative blood loss (97.9 ± 87.9 mL vs 49.8 ± 40.8 mL, P = 0.001) and higher Creactive protein levels on postoperative day 1 (42.7 ± 35.2 mg/L vs 25.8 ± 22.4 mg/L, P = 0.007) and day 3 (100.8 ± 65.7 mg/L vs. 50.7 ± 39.8 mg/L, P < 0.001). The LCMM model classified postoperative blood glucose trajectories into high and lowglucose groups, with the highglucose group demonstrating a significantly higher infection rate (38.1% vs. 14.8%, P = 0.009). Following further selection, antibiotic use before surgery (HR = 9.292, 95%CI: 1.062–81.320, P = 0.044), blood loss (HR = 1.011, 95%CI: 1.001–1.021, P = 0.029), and POD3 CRP (HR = 1.014, 95%CI: 1.004–1.025, P = 0.006) were incorporated into the prediction model.The AUROC values of the model was 0.8111. The calibration curve, DCA, and CIC demonstrated the favorable clinical applicability of the models. Conclusion This study established a concise and clinically applicable nomogram for the early prediction of PIAI in CRC patients, incorporating preoperative antibiotic use, intraoperative blood loss, and POD3 CRP as independent predictors. The model demonstrated favorable discrimination and calibration. Furthermore, while not included in the final model, LCMM of postoperative glucose trajectories provided a novel perspective for future research on metabolic patterns and infection risk. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Colorectal cancer(CRC) is one of the most common digestivetract malignancies worldwide. Radical surgical resection remains the primary curative approach for CRC. Radical resection with lymphadenectomy cures roughly half of patients with localized CRC [ 1 ] . To lower recurrence risk, postoperative adjuvant chemotherapy is routinely given, and locally advanced rectal cancer often receives neoadjuvant chemoradiation. Although targeted and immunotherapies have expanded options for metastatic CRC, surgery remains the only curative modality [ 2 ] . Postoperative intraabdominal infection (PIAI) is a significant complication in colorectal surgery, characterized by infections such as intra-abdominal abscesses and secondary peritonitis following CRC resections [ 3 ] . In this context, PIAI arises from contamination of the abdominal cavity (for example, spillage of bowel contents) or from an anastomotic leak [ 4 ] . Studies have demonstrated that the incidence of PIAI after CRC resection is roughly 5–15% of cases (about 10% in one series) [ 5 ] . Once such infections occur, they significantly compromise postoperative recovery, reduce patients' quality of life, and are strongly linked to tumor recurrence and unfavorable prognoses [ 6 ] . Building upon the understanding of PIAI's impact, it is imperative for clinical doctors to identify and analyze the factors contributing to its occurrence in CRC surgeries. Recent studies have highlighted several patient-related and perioperative factors associated with an increased risk of PIAI. These include advanced age, obesity, diabetes, malnutrition, prolonged operative time, significant intraoperative blood loss [ 7 , 8 ] , all of which can impair immune function and wound healing processes. Although a large number of studies have been conducted on the risk factors of abdominal cavity infection after colorectal cancer surgery [ 9 – 11 ] , there are still differences in the research results and no unified conclusion has been reached yet. Although latent class mixed models (LCMM) offers an method for characterizing postoperative glycemic trajectories [ 12 ] , the relationship between postoperative glucose profiles and PIAI in CRC patients remains under-explored. This study aims to develop a predictive nomogram for PIAI in CRC patients by integrating a comprehensive array of preoperative, intraoperative, and postoperative clinical indicators, in conjunction with blood glucose trajectories modeled using LCMM. So that it can assist clinicians in identifying high-risk patients, enabling the implementation of targeted preventive strategies and optimized perioperative management to improve patient outcomes. Method Study design and participants This retrospective study included patients who underwent CRC surgery performed by the same surgical team in the Department of Gastrointestinal Surgery at the First Affiliated Hospital of Fujian Medical University between January 2022 and December 2024. The inclusion criteria were as follows: (1) age 18 years or older; (2) a preoperative diagnosis of colorectal cancer confirmed by colonoscopy, with postoperative pathological confirmation; (3) a postoperative diagnosis of intra-abdominal infection. The following categories of patients were excluded: (1)emergency surgery patients; (2)patients with prior abdominal surgery; (3)patients with incomplete data; (4)patients with preoperative intra-abdominal infection. The protocol for this study was approved by the Institutional Review Board of .First Affiliated Hospital of Fujian Medical University. The study was approved by the Branch for Medical Research and Clinical Technology Application, Ethics Committee of the First Affiliated Hospital of Fujian Medical University (MTCA, ECFAH of FMU [2015]084 − 3). Outcome measures The primary outcome measure was the occurrence of PIAI. According to the criteria established by the U.S. Centers for Disease Control and Prevention(CDC) [ 13 ] , PIAI was defined as the presence of any of the following conditions: (1) the presence of pus or intestinal contents in the drainage tube; (2) postoperative imaging studies (e.g., abdominal CT) revealing fluid collection in the anastomotic region of the abdomen or pelvis; (3) leakage of contrast agent through the anastomosis during enema; (4) evident anastomotic dehiscence observed during reoperation due to peritonitis. Patients were grouped as PIAI or nonPIAI based on postoperative infection status. Data Collection Clinical and pathological data sex, age, body mass index (BMI), Nutritional Risk Screening 2002 (NRS-2002) score [ 14 ] , Patient-Generated Subjective Global Assessment (PG-SGA) score [ 15 ] , Venous Thromboembolism (VTE) Risk Assessment Score [ 16 ] , American Society of Anesthesiologists (ASA) physical status classification [ 17 ] , history of neoadjuvant chemotherapy (NACT), tumor location, history of diabetes, smoking, alcohol consumption, hypertension, hyperlipidemia, antibiotic use before surgery (defined as the use of antibiotics prior to surgery for infections other than intraabdominal infection, with surgery performed after infection control), age-adjusted Charlson Comorbidity Index (aCCI) [ 18 ] score, and tumor-node-metastasis (TNM) classification [ 19 ] . Operative variables surgical approach (laparoscopic, robotic-assisted, and open), operative time, intraoperative blood loss(blood loss), intraoperative blood transfusion, and postoperative length of hospital stay. Laboratory indicators : preoperative fasting blood glucose, albumin(ALB), white blood cell count(WBC), hemoglobin(HB), platelet(PLT) and aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio; on postoperative Days 1(POD1) and 3(POD3), the following parameters were measured: white blood cell (WBC), hemoglobin(HB), albumin(ALB), platelet(PLT), C-reactive protein (CRP), procalcitonin (PCT), neutrophil-to-lymphocyte ratio (NLR) [ 20 ] , platelet-to-lymphocyte ratio (PLR) [ 21 ] , neutrophil-to-monocyte ratio (NMR) [ 22 ] , lymphocyte-to-monocyte ratio (LMR) [ 23 ] , and AST/ALT ratio. Postoperative blood glucose monitoring : To comprehensively assess early postoperative glycemic fluctuations, the first blood glucose measurement upon the patient’s return to the ward was recorded as Day 0. From postoperative Day 1 to Day 3, blood glucose was measured four times daily at six-hour intervals (02:30, 08:30, 14:30, and 20:30). The average of these four readings was calculated to reflect daily glycemic trends during the early postoperative period. Construction of the Latent Class Mixed Model (LCMM) Postoperative blood glucose levels were monitored at multiple time points daily from postoperative day 0 to day 3. The blood glucose value on day 0 was obtained upon the patient's return to the ward, while the values for subsequent days were calculated as the average of four time points per day. To identify distinct trajectories of postoperative blood glucose changes, LCMM [ 24 ] was constructed using the lcmm package in R (version 4.1.0). During model fitting, combinations of latent class numbers (ranging from 1 to 4) and time effect forms (linear, quadratic, and cubic) were systematically evaluated. The optimal model was selected based on the Bayesian Information Criterion (BIC) and the distribution of sample sizes across classes, resulting in the selection of a two-class cubic trajectory model. Statistical Analysis Statistical analyses were conducted using SPSS Statistics version 29.0 and R software version 4.1.0. Significance was accepted for p < 0.05. Continuous variables were expressed as mean ± standard deviation (SD). For intergroup comparisons, the t-test was applied to normally distributed continuous variables, while the Mann–Whitney U test was used for non-normally distributed continuous variables. Categorical variables were presented as frequencies and percentages, and comparisons were made using the chi-square test or Fisher’s exact test, as appropriate. LASSO Regression and Nomogram Development This study aimed to identify risk factors for postoperative intra-abdominal infection (coded as 1 = yes, 0 = no) by analyzing perioperative clinical data to develop a predictive model. A total of 49 variables were included, encompassing preoperative factors (sex, age, BMI, ASA physical status, VTE risk score, NRS-2002, PG-SGA, aCCI, tumor location, history of NACT, preoperative antibiotic use excluding intra-abdominal infection, smoking, drinking, diabetes, hypertension, hyperlipidemia, bowel habits, defecation frequency, fasting blood glucose, ALB, WBC, HB, and AST/ALT ratio), intraoperative factors (operative time, blood loss, blood transfusion), and postoperative factors, including postoperative blood glucose, length of hospital stay, and laboratory parameters measured on POD1 and POD3, including WBC, HB, ALB, PLT, CRP, PCT, NLR, PLR, NMR, LMR, and AST/ALT ratio. Collinearity was assessed using variance inflation factor (VIF), and variables with VIF > 10 were excluded. For data reduction and feature selection, least absolute shrinkage and selection operator (LASSO) regression analysis was performed using the “cv.glmnet” package in R. The maximum number of iterations was set to 1,000 to ensure computational accuracy, and 10-fold cross-validation was employed to minimize the risk of overfitting [ 25 ] . Potential predictors of PIAI were evaluated using both univariate and multivariate logistic regression analyses. Based on the results of the multivariate analysis, a predictive model was developed, and a nomogram was constructed using the “rms [ 26 , 27 ] ” and “regplot” packages to estimate the probability of PIAI occurrence. Internal validation of the model was performed using the Bootstrap method, and the corrected C-index was calculated to assess the stability of the prediction model. Calibration curves were generated to evaluate the calibration performance of the model. The “pROC” package was utilized to plot the receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC). Additionally, decision curve analysis (DCA) was conducted using the `rmda` package to evaluate the net benefits across different threshold probabilities and confirm the clinical utility of the model. Finally, the clinical impact curve (CIC) was used to assess the loss-to-benefit ratio of the prediction model. Results Baseline Characteristics A total of 197 patients were assessed for eligibility, and 163 were included after exclusions (Fig. 1 ). Among the 163 patients, there were 87 men and 76 women, with a mean age of 62 ± 10 years. The mean BMI was 23.41 ± 3.43 kg/m². One hundred and forty-six patients (89.6%) underwent laparoscopic surgery, 13 (8.0%) underwent robot-assisted surgery, and 4 (2.4%) underwent open surgery. PIAI occurred in 29 patients (17.8%), whereas 134 patients (82.2%) did not develop infection. The mean postoperative hospital stay for all patients was 9.19 ± 4.12 days.(Table 1 ) Table 1 Baseline Characteristics of Patients Variables Cohort (n = 163) Sex (%) Male 87 (53.3%) Female 76 (46.7%) Age (years) 62.20 ± 10.03 BMI(kg/m 2 ) 23.41 ± 3.43 VTE score 3.60 ± 1.82 NRS2002 score < 3 94 (57.6%) ≥ 3 69 (42.4%) PGSGA score < 4 87 (53.4%) ≥ 4 74 (46.6%) ASA grade(%) < 3 150 (92.0%) ≥ 3 13 (8.0%%) History of NACT Yes 11 (6.7%) No 152 (93.3%) Diabetes Yes 32 (19.6%) No 131 (80.4%) Hypertension Yes 67 (41.2%) No 96 (58.8%) Hyperlipidemia Yes 16 (9.8%) No 147 (90.2%) Antibiotic use before surgery Yes 5 (3.0%) No 158 (97.0%) aCCI Score 4.60 ± 1.85 Tumor location Ascending colon 39(24.0%) Transverse colon 3 (1.8%) Descending colon 22 (13.5%) Sigmoid colon 24 (14.7%) Rectum 75 (46.0%) Operative time (min) 202.56 ± 90.94 Intraoperative blood loss (mL) 58.34 ± 55.16 Surgical approach Laparoscopic 146(89.6%) Robot-assisted 13(8.0%) Open surgery 4(2.4%) PIAI Yes 29(17.79%) No 134(82.21%) Postoperative hospital stay (days) 9.19 ± 4.12 Pathological stage(TNM stage) I 37(22.6%) II 60(36.8%) III 48(29.5%) IV 18(11.1%) BMI, body mass index; VTE, Venous Thromboembolism score; NRS, nutrition risk screening; PGSGA, patient-generated subjective global assessment; ASA, American Society of Anesthesiologists; NACT, neoadjuvant chemotherapy; Antibiotic use before surgery, defined as the use of antibiotics prior to surgery for infections other than intraabdominal infection, with surgery performed after infection control; TNM, Tumor-Node-Metastasis; PIAI, postoperative intra-abdominal infection. Perioperative Patient Characteristics Before the surgery, compared with the non-PIAI group, patients in the PIAI group had a significantly higher proportion of NRS-2002 scores ≥ 3 (62.1% vs. 38.1%, P = 0.018), PGSGA scores ≥ 4 (72.4% vs. 41.0%, P = 0.002), ASA classification ≥ 3 (17.2% vs. 6.0%, P = 0.042), reduced frequency of defecation prior to surgery (6.9% vs 0%, P = 0.008), and preoperative antibiotic use (10.3% vs. 1.5%, P = 0.012). During the surgery, those in the PIAI group had a higher proportion of tumors located in the descending colon (31.3% vs. 9.7%, P = 0.037) and experienced greater intraoperative blood loss (230.52 ± 108.50 vs.196.51 ± 85.95, P = 0.001). In the postoperative days, patients in the PIAI group had significantly lower albumin levels on postoperative day 1 (34.25 ± 3.27 vs 38.23 ± 4.31 g/L, P = 0.011), higher procalcitonin levels on postoperative day 1 (0.58 ± 0.77 vs 0.28 ± 0.97 ng/mL, P < 0.001), higher C-reactive protein levels on postoperative day 3 (100.75 ± 65.67 vs 50.67 ± 78.95 mg/L, P < 0.001), and lower lymphocyte-to-monocyte ratios on postoperative day 3 (2.03 ± 1.10 vs 2.96 ± 1.42, P 0.05).(Table 2 ) Table 2 Perioperative characteristics of patients Variables Non-PIAI group (n = 134) PIAI group (n = 29) P value Sex (%) 0.301 Male 69 (51.5%) 18 (62.1%) Female 65 (48.5%)) 11 (37.9%) Age (years) 62 (52, 72) 63(53, 63) 0.621 BMI(kg/m 2 ) 62.01 ± 10.13 63.03 ± 9.71 0.066 VTE score 23.64 ± 3.50 22.35 ± 2.90 0.929 NRS2002 score 0.018 < 3 83 (61.9%) 11 (37.9%) ≥ 3 51 (38.1%) 18 (62.1%) PGSGA score 0.002 < 4 79 (59.0%) 8 (27.6%) ≥ 4 55 (41.0%) 21 (72.4%) ASA grade (%) 0.042 < 3 126 (94.0%) 24 (82.8%) ≥ 3 8 (6.0%) 5 (17.2%) NACT 0.999 Yes 9 (6.7%) 2 (6.9%) No 125 (93.3%) 27 (93.1%) Stool characteristics 0.134 No change 72 (53.7%) 20 (69.0%) Bloody 62 (46.3%) 9 (31%) Stool frequency 0.008 No change 73 (54.5%) 13 (44.8%) increase 61 (45.5%) 14 (48.3%) decrease 0 (0%) 2 (6.9%) Smoking 0.521 Yes 20 (14.9%) 3 (10.3%) No 114 (85.1%) 26 (89.7%) Drinking 0.346 Yes 4 (3.0%) 0 (0%) No 130 (97.0%) 29 (100%) Diabetes 0.874 Yes 26 (19.4%) 6 (20.7%) No 108 (80.6%) 23 (79.3%) Hypertension 0.387 Yes 53 (39.6%) 14 (48.3%) No 81 (60.4%) 15 (51.7%) Hyperlipidemia 0.204 Yes 15 (11.2%) 1 (3.4%) No 119 (88.8%) 28 (96.6%) Antibiotic use before surgery 0.012 Yes 2 (1.5%) 3 (10.3%) No 132 (98.5%) 26 (89.7%) aCCI score 4.57 (2.73, 6.41) 4.72(2.81, 6.63) 0.656 Preoperative WBC(10 * 9/L) 4.57 ± 1.84 4.72 ± 1.91 0.197 Preoperative HB 6.30 ± 1.63 6.94 ± 2.15 0.883 Preoperative ALB (g/L) 125.01 ± 22.55 124.93 ± 24.08 0.278 Preoperative AST/ALT 41.66 ± 4.24 41.16 ± 4.18 0.833 Preoperative blood glucose 1.50 ± 0.70 1.52 ± 0.68 0.346 Tumor location 0.031 Ascending colon 33 (24.6%) 6 (20.7%) Transverse colon 2 (1.5%) 1 (3.4%) Descending colon 13 (9.7%) 9 (31.0%) Sigmoid colon 22 (16.4%) 2 (6.9%) Rectum 64(47.8%) 11 (37.9%) Surgical approach 0.816 Laparoscopic 121 (90.3%) 25 (86.2%) Robot-assisted 10 (7.5%) 3 (10.3%) Open surgery 3 (2.2%) 1 (3.5%) Intraoperative blood transfusion 0.297 Yes 2 (1.5%) 2 (6.9%) No 132 (98.5%) 27 (93.1%) Operative time (min) 196.51 ± 85.95 230.52 ± 108.50 0.062 blood loss (ml) 49.78 ± 40.80 97.93 ± 87.89 0.001 POD1WBC (10 * 9/L) 10.70 ± 2.77 11.36 ± 3.10 0.339 POD1HB (g/L) 114.10 ± 18.88 117.76 ± 21.23 0.401 POD1PLT (10 * 9/L) 230.04 ± 71.29 244.93 ± 75.77 0.364 POD1ALB (g/L) 34.25 ± 3.27 32.55 ± 3.15 0.011 POD1CRP (mg/L) 25.82 ± 22.41 42.70 ± 35.24 0.007 POD1PCT (ng/mL) 0.28 ± 0.63 0.65 ± 0.97 0.007 POD1NLR 10.19 ± 6.29 13.70 ± 10.46 0.043 POD1PLR 245.58 ± 117.27 310.03 ± 149.36 0.021 POD1LMR 2.05 ± 1.00 1.80 ± 1.12 0.057 POD1NMR 19.29 ± 22.58 18.23 ± 7.03 0.276 POD1AST/ALT 1.60 ± 0.72 1.40 ± 0.52 0.198 POD3WBC (10 * 9/L) 7.94 ± 2.44 10.08 ± 4.16 0.003 POD3HB (g/L) 113.69 ± 16.71 114.90 ± 22.10 0.649 POD3PLT (10 * 9/L) 212.12 ± 74.76 237.83 ± 74.41 0.058 POD3ALB (g/L) 34.57 ± 3.01 32.80 ± 3.69 0.007 POD3CRP (mg/L) 44.82 ± 39.82 100.75 ± 65.67 < 0.001 POD3PCT (ng/mL) 0.28 ± 0.97 0.58 ± 0.77 < 0.001 POD3NLR 5.97 ± 3.91 10.66 ± 5.88 < 0.001 POD3PLR 202.54 ± 101.24 290.12 ± 119.70 < 0.001 POD3LMR 2.96 ± 1.42 2.03 ± 1.10 < 0.001 POD3NMR 14.86 ± 7.34 17.36 ± 8.79 0.045 POD3AST/ALT 1.64 ± 0.82 1.38 ± 0.74 0.018 Postoperative length of hospital stay (days) 8.60 ± 3.84 11.93 ± 4.33 < 0.001 TNM Stage 0.077 I 35 (26.1%) 2 (6.9%) II 49 (36.6%) 11 (37.9%) III 36 (26.9%) 12 (41.4%) BMI, body mass index; VTE, Venous Thromboembolism score; NRS, nutrition risk screening; PGSGA, patient-generated subjective global assessment; ASA, American Society of Anesthesiologists; NACT, neoadjuvant chemotherapy; Antibiotic use before surgery, defined as the use of antibiotics prior to surgery for infections other than intraabdominal infection, with surgery performed after infection control; aCCI, age-adjusted Charlson Comorbidity Index; ALB, albumin; HB, hemoglobin; WBC, white blood cell; AST/ALT, aspartate aminotransferase/alanine aminotransferase ratio; POD, postoperative day; PLT, platelet; CRP, C-reactive protein; PCT, procalcitonin; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; NMR, neutrophil-to-monocyte ratio; LMR, lymphocyte-to-monocyte ratio; AST/ALT, aspartate aminotransferase/alanine aminotransferase; TNM, Tumor-Node-Metastasis. LCMM of Postoperative Blood Glucose Trajectories Postoperative blood glucose values were analyzed using a latent class mixed model (LCMM), which identified two distinct glycemic trajectories (Fig. 2 ). The high blood glucose group, comprising 21 patients (12.9%), exhibited persistently elevated glucose levels. In contrast, the low blood glucose group, including 142 patients (87.1%), demonstrated a relatively stable and lower glucose trajectory. In the overall cohort, the high-glucose group showed a significantly higher postoperative infection rate compared to the low-glucose group (38.1% vs. 14.8%, P = 0.009). Among patients without diabetes, this difference was more pronounced (66.7% vs. 15.2%, P = 0.009 Fisher’s exact test), while no significant difference was observed in patients with diabetes ( P = 0.383, Fisher’s exact test).(Table 3) LASSO regression analysis for risk factor selection Collinearity analysis was first performed, revealing that POD1 PLT, POD1 NLR, POD1 PLR, POD3 WBC, POD3 PLT, POD3 NLR, and POD3 PLR had variance inflation factors (VIF) greater than 10 and were therefore excluded. Figure 3 illustrates the LASSO regression process for selecting optimal parameters and predictive variables. Ultimately, 15 potential predictors for PIAI were selected, including BMI, PGSGA, ASA, stool frequency, diabetes, intraoperative blood loss, postoperative length of hospital stay, glucose, POD1 HB, POD1 ALB, POD1 PCT, POD3 CRP, POD3 LMR, and POD3 AST/ALT. Risk factor selection by logistic regression analysis and nomogram development The 15 variables identified by LASSO regression were included in univariate logistic regression analysis. Nine variables were identified as potential predictors(Table 6). These variables were subsequently included in multivariate logistic regression analysis. The results showed that antibiotic use before surgery (HR = 9.292, 95%CI: 1.062–81.320, P = 0.044), blood loss (HR = 1.011, 95%CI: 1.001–1.021, P = 0.029), and POD3 CRP (HR = 1.014, 95%CI: 1.004–1.025, P = 0.006) were independent risk factors for postoperative intra-abdominal infection(Table 4 ). A predictive nomogram model for PIAI was constructed based on the three independent risk factors (antibiotic use before surgery, POD3 CRP, and intraoperative blood loss) (Fig. 4 ). The nomogram indicates that the risk of postoperative intra-abdominal infection in CRC patients increases with use of preoperative antibiotics, elevated POD3 CRP levels, and greater intraoperative blood loss. Table 4 Logistic Regression Analysis of Predictive Factors for PIAI Variables Univariate Analysis Multivariate Analysis HR (95%CI) P value HR (95%CI) P value BMI 0.888 (0.782–1.009) 0.068 - - PGSGA score 3.770 (1.058–9.127) 0.003 3.040 (0.992–9.134) 0.052 ASA score 3.281 (0.989–10.890) 0.052 - - Stool frequency 1.814 (0.845–3.893) 0.127 - - diabetes 1.084 (0.401–2.932) 0.874 - - Antibiotics use before surgery 7.615 (1.212–47.852) 0.030 9.292 (1.062–81.320) 0.044 Intraoperative blood loss 1.014 (1.006–1.022) < 0.001 1.011 (1.001–1.021) 0.029 postoperative blood glucose 0.282 (0.104–0.763) 0.013 0.597 (0.179–1.992) 0.402 Postoperative hospital stay (days) 1.191 (1.074–1.321) < 0.001 1.056 (0.945–1.179) 0.337 POD1HB 1.011 (0.988–1.033) 0.354 - - POD1ALB 0.851 (0.749–0.967) 0.014 0.905 (0.752–1.088) 0.289 POD1PCT 1.735 (1.014–2.969) 0.044 1.343 (0.789–2.288) 0.278 POD3CRP 1.019 (1.011–1.027) < 0.001 1.014 (1.004–1.025) 0.006 POD3LMR 0.493 (0.321–0.759) 0.001 0.679 (0.410–1.125) 0.133 POD3AST/ALT 0.525 (0.244–1.133) 0.100 - - Table 5 Postoperative Blood Glucose of Patients Variables Non-PIAI group (n = 134) PIAI group (n = 29) P value Overall analysis 0.009 Low blood glucose group 121 (85.2%) 21 (14.8%) High blood glucose group 13 (61.9%) 8 (38.1%) Subgroup analysis No history of diabetes Low blood glucose group 106 (84.8%) 19 (15.2%) 0.009a High blood glucose group 2 (33.3%) 4 (66.7%) No history of diabetes Low blood glucose group 15 (88.2%) 2 (11.8%) 0.383a High blood glucose group 11 (73.3%) 4 (26.7%) a, Fisher’s exact test Validation of the nomogram The performance of the predictive model is shown in Fig. 5 . The calibration curve (Fig. 5 A) closely follows the 45° line, indicating good agreement between predicted and actual outcomes (C-index = 0.8111). The ROC curve (Fig. 5 B) yielded an area under the curve (AUC) of 0.8111, demonstrating good discriminatory power and reliability in predicting PIAI in colorectal cancer patients. The decision curve analysis (DCA) in Fig. 5 C indicates that the nomogram offers greater net clinical benefit across a range of threshold probabilities compared to the “treat-all” or “treat-none” strategies. The clinical impact curve (CIC) in Fig. 5 D further supports its utility: the red line shows the number of patients predicted to be at risk, while the blue line shows the number of true positives. As the threshold increases, the two lines converge, reflecting better alignment between predictions and actual outcomes. These findings confirm the nomogram’s strong predictive performance and clinical applicability. Discussion PIAI remains a frequent and serious complication following CRC surgery [ 28 ] , particularly in patients with compromised immune function due to malignancy and perioperative stress. PIAI is associated with increased morbidity, prolonged hospital stays, and elevated healthcare costs [ 29 ] , underscoring the critical importance of early identification and timely intervention. In this study, we systematically integrated perioperative clinical indicators—including demographic characteristics, intraoperative variables, postoperative laboratory parameters (such as inflammatory and hematological markers), and dynamic blood glucose trajectories during the first three postoperative days—to identify potential predictors of PIAI. Based on LASSO and multivariate logistic regression, we constructed a nomogram with favorable predictive performance, providing a simple and practical tool for early risk assessment and clinical decision-making in CRC patients. PIAI often presents with insidious and nonspecific early clinical manifestations, making timely and accurate diagnosis challenging [ 30 ] . Once diagnosed, patients commonly present with fever, abdominal pain, purulent drainage, or even require reoperation due to clinical deterioration [ 31 ] . Several studies have reported that the time to clinical diagnosis ranges from postoperative day 6 to 12 [ 32 , 33 ] . Accordingly, the selection of laboratory indicators from postoperative days 1 and 3 for predictive modeling facilitates the early identification of subtle biochemical alterations that precede the manifestation of overt clinical symptoms, thereby enabling more timely and potentially more effective clinical intervention. Furthermore, the incidence of PIAI in our cohort was 17.8%, which is consistent with previously reported rates ranging from 5–30% in patients undergoing colorectal surgery [ 34 ] . The consistency with previously reported incidence rates highlights the representativeness of our study population and supports the model's applicability to similar clinical settings. Our study revealed that preoperative antibiotic treatment for non-abdominal infections was independently associated with an increased risk of PIAI in patients undergoing colorectal cancer surgery, despite all patients receiving standardized prophylactic antibiotics prior to the operation. This finding appears to contrast with the well-established role of prophylactic antibiotic use in reducing postoperative infections [ 35 – 37 ] . However, most studies supporting prophylactic antibiotics have focused on short-term, standardized regimens given immediately before surgery, usually with mechanical bowel preparation and aimed solely at infection prevention. In contrast, longer-term preoperative antibiotic exposure, which has not been adequately considered, may have distinct effects on postoperative outcomes. Recent evidence suggests that antibiotics may increase the risk of PIAI in colorectal cancer patients by disrupting the gut microbiota [ 38 ] , thereby compromising intestinal barrier function. The intestinal barrier consists of the secretory barrier (mucins, antimicrobial peptides, cytokines), the physical barrier (epithelial cells and tight junctions), and the immune barrier (gut-associated lymphoid tissue and immune cells) [ 39 ] . Antibiotic-induced changes in gut microbiota can weaken this barrier by altering mucin secretion, cytokine production, and antimicrobial peptide expression, thereby increasing intestinal permeability and promoting bacterial translocation. This disruption may contribute to postoperative infections, especially in colorectal surgery patients [ 40 ] . A nationwide cohort study in Swedish supports this hypothesis, identifying preoperative antibiotic use as a novel risk factor for surgical site infections, including anastomotic leakage, within 30 days following colon cancer surgery [ 41 ] . Similarly, another study, which collected self-reported information on antimicrobial exposure in the 3 months before any elective surgery, reported adverse effects of antibiotics on postoperative complications, including infections [ 42 ] . These findings highlight that the clinical impact of preoperative antibiotic use may vary depending on timing, indication, and antibiotic class [ 43 ] . Further research is needed to clarify these associations and guide optimal perioperative antibiotic strategies. In our study, increased intraoperative blood loss was also found to be independently associated with a higher risk of PIAI.Intraoperative blood loss has been widely reported as a significant risk factor for postoperative intra-abdominal infections in colorectal surgery. Studies have shown that increased blood loss is associated with higher rates of surgical site infections and anastomotic leakage, likely due to impaired tissue perfusion, immune suppression, and longer operative time [ 44 – 46 ] . Therefore, minimizing intraoperative bleeding may play an important role in reducing postoperative infectious complications. In the nomogram, POD3 CRP levels were also identified as a significant predictor of PIAI following CRC surgery. The current evidence indicates that CRP alone has limited diagnostic accuracy, as it is associated with a relatively low positive predictive value and inadequate sensitivity in certain clinical contexts [ 47 , 48 ] . Notably, some researchers caution against overinterpreting inflammatory markers such as leukocyte count, CRP, and procalcitonin (PCT) within the first 24 hours after surgery due to their poor specificity and overlap with normal postoperative inflammatory responses [ 49 ] . In contrast, CRP levels on POD3 tend to better reflect ongoing pathological inflammation rather than physiological postoperative changes, thus offering improved discrimination for early infectious complications ][ 50 ] . This timing allows clinicians to detect developing PIAI with greater reliability, aiding timely intervention and improved outcomes. Notably, this study is use LCMM to identify distinct blood glucose trajectories within the first 72 hours after surgery and to explore their association with PIAI. Chi-square tests and univariate logistic regression revealed a significant association between glucose trajectory groups and PIAI risk, particularly in non-diabetic patients. However, the glucose trajectory variable was not retained in the final multivariate logistic regression model, possibly due to collinearity with diabetes status and limited statistical power. Further subgroup analyses revealed that among non-diabetic patients, those in the “high blood glucose group” had a substantially increased risk of developing PIAI. These findings suggest that acute perioperative hyperglycemia may contribute to the development of postoperative intra-abdominal infection in colorectal cancer patients without diabetes. This result aligns with previous study, which have reported that stress-induced hyperglycemia can impair immune function and increase susceptibility to infection [ 51 ] . Future research can employ LCMM to further validate these findings and explore whether the integration of glucose trajectory patterns into predictive models improves early risk stratification and informs infection prevention strategies. This study has several limitations. It was conducted at a single center with a limited sample size, which may affect the generalizability and reduce the statistical power of the results. Additionally, postoperative inflammatory markers (such as CRP, PCT, and IL-6) are subject to early physiological fluctuations irrespective of complications, suggesting that fixed-time sampling may not fully capture individual inflammatory responses. More individualized sampling strategies may be preferable in clinical practice. Furthermore, the relatively wide intervals between postoperative glucose measurements may have limited the resolution of glucose trajectory modeling and the ability to detect short-term variations. Conclusion This study developed a simple and clinically applicable model for predicting PIAI in CRC patients, based on preoperative antibiotic use, intraoperative blood loss, and POD3 CRP. The model showed good discrimination and calibration, supporting its potential in early risk identification and preventive management. Additionally, LCMM was applied to characterize postoperative glucose trajectories. While not included in the final model, this method offers a new approach for exploring the impact of perioperative glycemic patterns on surgical outcomes. Declarations Acknowledgments The authors would like to express their gratitude to Professor Wu from the Public Health School of Fujian Medical University for his valuable statistical advice contributed to this study. Data Availability Statement The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request. Author Contributions Study conception and design: Y.Y., S.D., Q.L.. Administrative support: Y.Y., Q.L.. Provision of materials and samples: Y.Y., J.S., L.Q. and Z.F.. Acquisition of data: Y.Y., J.S., Analysis and interpretation of data: Y.Y., S.D., and L.Q.. Drafting of manuscript: Y.Y., S.D., J.S.. and Q.L.. Declaration of competing interest Yunzhe Yu, Sida Sun, Jianshen Chen, Liqun Liao, Junfeng Zhou, Qingliang He have no conflicts of interest or financial ties to disclose. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Clinical Trial Number Clinical trial number: not applicable. Ethics, Consent to Participate, and Consent to Publish Declarations The study was approved by the Branch for Medical Research and Clinical Technology Application, Ethics Committee of the First Affiliated Hospital of Fujian Medical University (MTCA, ECFAH of FMU [2015]084-3). The requirement for informed consent was waived by the Ethics Committee due to the retrospective nature of the study and the use of anonymized data. Consent for publication was not required as the manuscript does not contain any individual person’s identifiable data. References Zhao P, Yang X, Yan Y, Yang J, Li S, Du X: Effect of radical lymphadenectomy in colorectal cancer with para-aortic lymph node metastasis: a systematic review and meta-analysis . BMC Surg 2022, 22 (1):181. Ohishi T, Kaneko MK, Yoshida Y, Takashima A, Kato Y, Kawada M: Current Targeted Therapy for Metastatic Colorectal Cancer . Int J Mol Sci 2023, 24 (2). Bassetti M, Eckmann C, Giacobbe DR, Sartelli M, Montravers P: Post-operative abdominal infections: epidemiology, operational definitions, and outcomes . Intensive Care Med 2020, 46 (2):163-172. Fry DE: The prevention of surgical site infection in elective colon surgery . Scientifica (Cairo) 2013, 2013 :896297. Sánchez-Velázquez P, Pera M, Jiménez-Toscano M, Mayol X, Rogés X, Lorente L, Iglesias M, Gallén M: Postoperative intra-abdominal infection is an independent prognostic factor of disease-free survival and disease-specific survival in patients with stage II colon cancer . Clin Transl Oncol 2018, 20 (10):1321-1328. Salvans S, Mayol X, Alonso S, Messeguer R, Pascual M, Mojal S, Grande L, Pera M: Postoperative peritoneal infection enhances migration and invasion capacities of tumor cells in vitro: an insight into the association between anastomotic leak and recurrence after surgery for colorectal cancer . Ann Surg 2014, 260 (5):939-943; discussion 943-934. Jia L, Zhao H, Liu J: Meta-analysis of postoperative incision infection risk factors in colorectal cancer surgery . Front Surg 2024, 11 :1415357. Parnasa SY, Lev-Cohain N, Bader R, Shweiki A, Mizrahi I, Abu-Gazala M, Pikarsky AJ, Shussman N: Predictors of perioperative morbidity in elderly patients undergoing colorectal cancer resection . Tech Coloproctol 2024, 29 (1):4. Pei G, Zhen S, Zhang B: A novel nomogram based on nutritional and immune status predicting postoperative intra-abdominal infection in colorectal cancer . Asia Pac J Clin Nutr 2022, 31 (4):626-635. Domínguez-Comesaña E, Estevez-Fernández SM, López-Gómez V, Ballinas-Miranda J, Domínguez-Fernández R: Procalcitonin and C-reactive protein as early markers of postoperative intra-abdominal infection in patients operated on colorectal cancer . Int J Colorectal Dis 2017, 32 (12):1771-1774. Liu CQ, Yu ZB, Gan JX, Mei TM: Preoperative blood markers and intra-abdominal infection after colorectal cancer resection . World J Gastrointest Surg 2024, 16 (2):451-462. Hulman A, Witte DR, Vistisen D, Balkau B, Dekker JM, Herder C, Hatunic M, Konrad T, Færch K, Manco M: Pathophysiological Characteristics Underlying Different Glucose Response Curves: A Latent Class Trajectory Analysis From the Prospective EGIR-RISC Study . Diabetes Care 2018, 41 (8):1740-1748. Horan TC, Andrus M, Dudeck MA: CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting . Am J Infect Control 2008, 36 (5):309-332. Kondrup J, Rasmussen HH, Hamberg O, Stanga Z: Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials . Clin Nutr 2003, 22 (3):321-336. Isenring E, Bauer J, Capra S: The scored Patient-generated Subjective Global Assessment (PG-SGA) and its association with quality of life in ambulatory patients receiving radiotherapy . Eur J Clin Nutr 2003, 57 (2):305-309. Lau BD, Streiff MB, Pronovost PJ, Haut ER: Venous Thromboembolism Quality Measures Fail to Accurately Measure Quality . Circulation 2018, 137 (12):1278-1284. Owens WD, Felts JA, Spitznagel EL, Jr.: ASA physical status classifications: a study of consistency of ratings . Anesthesiology 1978, 49 (4):239-243. Charlson M, Szatrowski TP, Peterson J, Gold J: Validation of a combined comorbidity index . J Clin Epidemiol 1994, 47 (11):1245-1251. Ajani JA, D'Amico TA, Bentrem DJ, Chao J, Cooke D, Corvera C, Das P, Enzinger PC, Enzler T, Fanta P et al : Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology . J Natl Compr Canc Netw 2022, 20 (2):167-192. Qian B, Zheng Y, Jia H, Zheng X, Gao R, Li W: Neutrophil-lymphocyte ratio as a predictive marker for postoperative infectious complications: A systematic review and meta-analysis . Heliyon 2023, 9 (5):e15586. Kriplani A, Pandit S, Chawla A, de la Rosette J, Laguna P, Jayadeva Reddy S, Somani BK: Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and lymphocyte-monocyte ratio (LMR) in predicting systemic inflammatory response syndrome (SIRS) and sepsis after percutaneous nephrolithotomy (PNL) . Urolithiasis 2022, 50 (3):341-348. Xia X, Wang Y, Xie M, Qiu S, Zhou J: Elevated neutrophil - to - monocyte ratio as a prognostic marker for poor outcomes in neonatal sepsis . Heliyon 2022, 8 (10):e11181. Kamonvarapitak T, Matsuda A, Matsumoto S, Jamjittrong S, Sakurazawa N, Kawano Y, Yamada T, Suzuki H, Miyashita M, Yoshida H: Preoperative lymphocyte-to-monocyte ratio predicts postoperative infectious complications after laparoscopic colorectal cancer surgery . Int J Clin Oncol 2020, 25 (4):633-640. Proust-Lima C, Philipps V, Liquet B: Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm . Journal of Statistical Software 2017, 78 (2):1 - 56. Bartholomew TS, Tookes HE, Spencer EC, Feaster DJ: Application of machine learning algorithms for localized syringe services program policy implementation - Florida, 2017 . Ann Med 2022, 54 (1):2137-2150. Park SY: Nomogram: An analogue tool to deliver digital knowledge . J Thorac Cardiovasc Surg 2018, 155 (4):1793. Horton NJ, Kleinman K: Using R and RStudio for data management, statistical analysis, and graphics . 2015. Katoh H, Yamashita K, Wang G, Sato T, Nakamura T, Watanabe M: Anastomotic leakage contributes to the risk for systemic recurrence in stage II colorectal cancer . J Gastrointest Surg 2011, 15 (1):120-129. O'Brien WJ, Gupta K, Itani KMF: Association of Postoperative Infection With Risk of Long-term Infection and Mortality . JAMA Surg 2020, 155 (1):61-68. Zhu R, Hong X, Zhang D, Xiao Y, Xu Q, Wu B, Guo J, Han X, Yang Q, Zhao Y et al : Application of metagenomic sequencing of drainage fluid in rapid and accurate diagnosis of postoperative intra-abdominal infection: a diagnostic study . Int J Surg 2023, 109 (9):2624-2630. Avkan-Oguz V, Baykam N, Sokmen S, Guner R, Agalar F, Alp E, Dogrul A, Turhan O, Agalar C, Kurtaran B et al : Recommendations for intra-abdominal infections consensus report . Ulus Cerrahi Derg 2016, 32 (4):306-321. Hyman N, Manchester TL, Osler T, Burns B, Cataldo PA: Anastomotic leaks after intestinal anastomosis: it's later than you think . Ann Surg 2007, 245 (2):254-258. Pickleman J, Watson W, Cunningham J, Fisher SG, Gamelli R: The failed gastrointestinal anastomosis: an inevitable catastrophe? J Am Coll Surg 1999, 188 (5):473-482. Povsic MK, Beovic B, Ihan A: Perioperative Increase in Neutrophil CD64 Expression is an Indicator for Intra-abdominal Infection after Colorectal Cancer Surgery . Radiol Oncol 2017, 51 (2):211-220. Grewal S, Reuvers JRD, Abis GSA, Otten RHJ, Kazemier G, Stockmann H, van Egmond M, Oosterling SJ: Oral Antibiotic Prophylaxis Reduces Surgical Site Infection and Anastomotic Leakage in Patients Undergoing Colorectal Cancer Surgery . Biomedicines 2021, 9 (9). Sangiorgio G, Vacante M, Basile F, Biondi A: Oral and Parenteral vs. Parenteral Antibiotic Prophylaxis for Patients Undergoing Laparoscopic Colorectal Resection: An Intervention Review with Meta-Analysis . Antibiotics (Basel) 2021, 11 (1). Nelson RL, Hassan M, Grant MD: Antibiotic prophylaxis in colorectal surgery: are oral, intravenous or both best and is mechanical bowel preparation necessary? Tech Coloproctol 2020, 24 (12):1233-1246. Ramirez J, Guarner F, Bustos Fernandez L, Maruy A, Sdepanian VL, Cohen H: Antibiotics as Major Disruptors of Gut Microbiota . Front Cell Infect Microbiol 2020, 10 :572912. Dmytriv TR, Storey KB, Lushchak VI: Intestinal barrier permeability: the influence of gut microbiota, nutrition, and exercise . Front Physiol 2024, 15 :1380713. Duan H, Yu L, Tian F, Zhai Q, Fan L, Chen W: Antibiotic-induced gut dysbiosis and barrier disruption and the potential protective strategies . Crit Rev Food Sci Nutr 2022, 62 (6):1427-1452. Lu SSM, Rutegård M, Häggström C, Gylfe Å, Harlid S, Van Guelpen B: Prior antibiotics exposure is associated with an elevated risk of surgical site infections, including anastomotic leakage, after colon cancer but not rectal cancer surgery: A register-based study of 38,839 patients . Int J Cancer 2025, 156 (9):1703-1715. Guidry CA, Shah PM, Dietch ZC, Elwood NR, Krebs ED, Mehaffey JH, Sawyer RG: Recent Anti-Microbial Exposure Is Associated with More Complications after Elective Surgery . Surg Infect (Larchmt) 2018, 19 (5):473-479. Gao W, Liu X, Zhang S, Wang J, Qiu B, Shao J, Huang W, Huang Y, Yao M, Tang LL: Alterations in gut microbiota and inflammatory cytokines after administration of antibiotics in mice . Microbiol Spectr 2024, 12 (8):e0309523. Cai W, Wang L, Wang W, Zhou T: Systematic review and meta-analysis of the risk factors of surgical site infection in patients with colorectal cancer . Transl Cancer Res 2022, 11 (4):857-871. Post IL, Verheijen PM, Pronk A, Siccama I, Houweling PL: Intraoperative blood pressure changes as a risk factor for anastomotic leakage in colorectal surgery . Int J Colorectal Dis 2012, 27 (6):765-772. Simillis C, Charalambides M, Mavrou A, Afxentiou T, Powar MP, Wheeler J, Davies RJ, Fearnhead NS: Operative blood loss adversely affects short and long-term outcomes after colorectal cancer surgery: results of a systematic review and meta-analysis . Tech Coloproctol 2023, 27 (3):189-208. Ortega-Deballon P, Radais F, Facy O, d'Athis P, Masson D, Charles PE, Cheynel N, Favre JP, Rat P: C-reactive protein is an early predictor of septic complications after elective colorectal surgery . World J Surg 2010, 34 (4):808-814. Giaccaglia V, Salvi PF, Cunsolo GV, Sparagna A, Antonelli MS, Nigri G, Balducci G, Ziparo V: Procalcitonin, as an early biomarker of colorectal anastomotic leak, facilitates enhanced recovery after surgery . J Crit Care 2014, 29 (4):528-532. Xiao H, Xiao Y, Quan H, Liu W, Pan S, Ouyang Y: Intra-abdominal infection after radical gastrectomy for gastric cancer: Incidence, pathogens, risk factors and outcomes . Int J Surg 2017, 48 :195-200. Zhou J, Lin L, He C, Wang Z, Zhan Y, Sun S, He Q: Integrating serological and drainage fluid indicators: developing two predictive models for early detection of postoperative intra-abdominal infections in gastrointestinal tumor patients . Front Oncol 2025, 15 :1566954. Dungan KM, Braithwaite SS, Preiser JC: Stress hyperglycaemia . Lancet 2009, 373 (9677):1798-1807. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7179766","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508420760,"identity":"54e72166-7f39-4ecd-a50e-517f8c0cd7d7","order_by":0,"name":"Yunzhe Yu","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunzhe","middleName":"","lastName":"Yu","suffix":""},{"id":508420761,"identity":"161e0c27-d15d-412a-87a5-9b8f1e15c247","order_by":1,"name":"Sida Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sida","middleName":"","lastName":"Sun","suffix":""},{"id":508420762,"identity":"0fe3f25e-25f2-4ad0-ae0f-7183f247cf8d","order_by":2,"name":"Jiansheng Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiansheng","middleName":"","lastName":"Chen","suffix":""},{"id":508420763,"identity":"d9239285-7d75-463e-b32e-98b0b2b1ec38","order_by":3,"name":"Liqun Liao","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liqun","middleName":"","lastName":"Liao","suffix":""},{"id":508420764,"identity":"d2bba82a-32a2-4be8-bf4c-b0494ba40b09","order_by":4,"name":"Junfeng Zhou","email":"","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Zhou","suffix":""},{"id":508420765,"identity":"63b3bafe-a118-4a97-b457-178e61d39901","order_by":5,"name":"Qingliang He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACgwMMDEBkwQNkMz74UMHAQ6wWCZBKZsMZZ4jUAgQSIIJNmreNGIcdb954uOCXhAz/7PbLxrzz6mTM2Q8wfviYg1uL/ZljBYdn9knwSNw5U/hw7rbDPJY9CcySM7fhseVGjsFh3h6gX27kJBu83XaAx+BAAhszLz4t999AtMjfyEmT4J1Tx2Nw/gEBLTd4DA7z/JDgMbiRfkySt4EZyCBky5m0gsO8DRI8hjdygIF87DBQy8Nm/H45fnjzZ54/NvZyN9IfPvhQU2dvcD754IePeLSAdDEwgqODxwAqwNiAVz1YC8MfEM3+gJDKUTAKRsEoGKEAAG2rWX060rCVAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qingliang","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-07-21 17:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7179766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7179766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90544420,"identity":"9e652c63-4edd-4c26-9bde-b1b096331b9c","added_by":"auto","created_at":"2025-09-04 00:18:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":343069,"visible":true,"origin":"","legend":"\u003cp\u003eThe inclusion and exclusion process\u003c/p\u003e","description":"","filename":"Fig.1flowchart.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7179766/v1/9af444585bde6b9752d40a10.jpg"},{"id":90543360,"identity":"f34c7d2f-41e0-499c-a44f-3038d97272ad","added_by":"auto","created_at":"2025-09-04 00:10:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1004000,"visible":true,"origin":"","legend":"\u003cp\u003ePostoperative Blood Glucose Trajectories\u003c/p\u003e","description":"","filename":"Fig.2Glucosetrajectory.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7179766/v1/704710a735f3ac137bd2fba2.jpg"},{"id":90542380,"identity":"4c71107f-edf0-44c9-8e63-6717025562f0","added_by":"auto","created_at":"2025-09-04 00:02:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":190181,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO analysis of perioperative variables: (A) shows the coefficient trajectories of the variables included in the initial model, demonstrating a progressive shrinkage of coefficients, with some reduced to zero to prevent overfitting. (B) presents the results of 10-fold cross-validation used to select the optimal lambda (λ) value corresponding to the minimum mean cross-validated error, which guided the exclusion of variables with coefficients equal to zero.\u003c/p\u003e","description":"","filename":"Fig.3Lasso.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7179766/v1/3e37650e92cd054a06cbaf32.jpg"},{"id":90542385,"identity":"3b67039e-f653-46e0-b013-662c879de282","added_by":"auto","created_at":"2025-09-04 00:02:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":331573,"visible":true,"origin":"","legend":"\u003cp\u003eFor each independent risk factor, a vertical line is drawn upward to the top scoring axis to obtain the corresponding score. The scores for all variables are then summed to generate a total score, which is projected downward onto the bottom risk scale to estimate the probability of postoperative intra-abdominal infection in colorectal cancer patients. Blood_loss: Intraoperative blood loss; antibiotics_use_before_surgery: Antibiotics use before surgery\u003c/p\u003e","description":"","filename":"Fig.4nomogram.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7179766/v1/dc9faa217e4a4cd4158678cd.jpg"},{"id":90542389,"identity":"6c527abc-02ea-423d-827b-1ffd7845d2ea","added_by":"auto","created_at":"2025-09-04 00:02:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":548937,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance evaluation of the nomogram. (A)Calibration curves of the nomogram.(B)Receiver operating characteristic curves of the nomogram. (C)Decision curve analysis(DCA) curves of the nomogram. (D)Clinical impact curves(CIC) of the nomogram.\u003c/p\u003e","description":"","filename":"Fig.5Validationofthenomogram.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7179766/v1/9dcf5a6090acc68f199df664.jpg"},{"id":106959423,"identity":"a05cb18a-bc74-4d76-9d7d-4644626daa4c","added_by":"auto","created_at":"2026-04-15 09:08:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6102153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7179766/v1/155d204b-de1c-4e94-96e3-e5d1ba03760d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Nomogram Model for Early Prediction of Postoperative Intra-Abdominal Infection in Colorectal Cancer patients Based on Perioperative Clinical Variables ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer(CRC) is one of the most common digestivetract malignancies worldwide. Radical surgical resection remains the primary curative approach for CRC. Radical resection with lymphadenectomy cures roughly half of patients with localized CRC\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. To lower recurrence risk, postoperative adjuvant chemotherapy is routinely given, and locally advanced rectal cancer often receives neoadjuvant chemoradiation. Although targeted and immunotherapies have expanded options for metastatic CRC, surgery remains the only curative modality\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePostoperative intraabdominal infection (PIAI) is a significant complication in colorectal surgery, characterized by infections such as intra-abdominal abscesses and secondary peritonitis following CRC resections\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In this context, PIAI arises from contamination of the abdominal cavity (for example, spillage of bowel contents) or from an anastomotic leak\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Studies have demonstrated that the incidence of PIAI after CRC resection is roughly 5–15% of cases (about 10% in one series)\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Once such infections occur, they significantly compromise postoperative recovery, reduce patients' quality of life, and are strongly linked to tumor recurrence and unfavorable prognoses\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Building upon the understanding of PIAI's impact, it is imperative for clinical doctors to identify and analyze the factors contributing to its occurrence in CRC surgeries. Recent studies have highlighted several patient-related and perioperative factors associated with an increased risk of PIAI. These include advanced age, obesity, diabetes, malnutrition, prolonged operative time, significant intraoperative blood loss\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, all of which can impair immune function and wound healing processes.\u003c/p\u003e\u003cp\u003eAlthough a large number of studies have been conducted on the risk factors of abdominal cavity infection after colorectal cancer surgery\u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, there are still differences in the research results and no unified conclusion has been reached yet. Although latent class mixed models (LCMM) offers an method for characterizing postoperative glycemic trajectories\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, the relationship between postoperative glucose profiles and PIAI in CRC patients remains under-explored. This study aims to develop a predictive nomogram for PIAI in CRC patients by integrating a comprehensive array of preoperative, intraoperative, and postoperative clinical indicators, in conjunction with blood glucose trajectories modeled using LCMM. So that it can assist clinicians in identifying high-risk patients, enabling the implementation of targeted preventive strategies and optimized perioperative management to improve patient outcomes.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cb\u003eStudy design and participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective study included patients who underwent CRC surgery performed by the same surgical team in the Department of Gastrointestinal Surgery at the First Affiliated Hospital of Fujian Medical University between January 2022 and December 2024. The inclusion criteria were as follows: (1) age 18 years or older; (2) a preoperative diagnosis of colorectal cancer confirmed by colonoscopy, with postoperative pathological confirmation; (3) a postoperative diagnosis of intra-abdominal infection. The following categories of patients were excluded: (1)emergency surgery patients; (2)patients with prior abdominal surgery; (3)patients with incomplete data; (4)patients with preoperative intra-abdominal infection. The protocol for this study was approved by the Institutional Review Board of .First Affiliated Hospital of Fujian Medical University. The study was approved by the Branch for Medical Research and Clinical Technology Application, Ethics Committee of the First Affiliated Hospital of Fujian Medical University (MTCA, ECFAH of FMU [2015]084 − 3).\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary outcome measure was the occurrence of PIAI. According to the criteria established by the U.S. Centers for Disease Control and Prevention(CDC)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, PIAI was defined as the presence of any of the following conditions: (1) the presence of pus or intestinal contents in the drainage tube; (2) postoperative imaging studies (e.g., abdominal CT) revealing fluid collection in the anastomotic region of the abdomen or pelvis; (3) leakage of contrast agent through the anastomosis during enema; (4) evident anastomotic dehiscence observed during reoperation due to peritonitis. Patients were grouped as PIAI or nonPIAI based on postoperative infection status.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical and pathological data\u003c/strong\u003e\u003c/p\u003e\u003cp\u003esex, age, body mass index (BMI), Nutritional Risk Screening 2002 (NRS-2002) score\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, Patient-Generated Subjective Global Assessment (PG-SGA) score\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, Venous Thromboembolism (VTE) Risk Assessment Score\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, American Society of Anesthesiologists (ASA) physical status classification\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, history of neoadjuvant chemotherapy (NACT), tumor location, history of diabetes, smoking, alcohol consumption, hypertension, hyperlipidemia, antibiotic use before surgery (defined as the use of antibiotics prior to surgery for infections other than intraabdominal infection, with surgery performed after infection control), age-adjusted Charlson Comorbidity Index (aCCI)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e score, and tumor-node-metastasis (TNM) classification\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOperative variables\u003c/strong\u003e\u003c/p\u003e\u003cp\u003esurgical approach (laparoscopic, robotic-assisted, and open), operative time, intraoperative blood loss(blood loss), intraoperative blood transfusion, and postoperative length of hospital stay.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLaboratory indicators\u003c/b\u003e: preoperative fasting blood glucose, albumin(ALB), white blood cell count(WBC), hemoglobin(HB), platelet(PLT) and aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio; on postoperative Days 1(POD1) and 3(POD3), the following parameters were measured: white blood cell (WBC), hemoglobin(HB), albumin(ALB), platelet(PLT), C-reactive protein (CRP), procalcitonin (PCT), neutrophil-to-lymphocyte ratio (NLR)\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, platelet-to-lymphocyte ratio (PLR)\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, neutrophil-to-monocyte ratio (NMR)\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, lymphocyte-to-monocyte ratio (LMR)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, and AST/ALT ratio.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePostoperative blood glucose monitoring\u003c/b\u003e: To comprehensively assess early postoperative glycemic fluctuations, the first blood glucose measurement upon the patient’s return to the ward was recorded as Day 0. From postoperative Day 1 to Day 3, blood glucose was measured four times daily at six-hour intervals (02:30, 08:30, 14:30, and 20:30). The average of these four readings was calculated to reflect daily glycemic trends during the early postoperative period.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of the Latent Class Mixed Model (LCMM)\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePostoperative blood glucose levels were monitored at multiple time points daily from postoperative day 0 to day 3. The blood glucose value on day 0 was obtained upon the patient's return to the ward, while the values for subsequent days were calculated as the average of four time points per day. To identify distinct trajectories of postoperative blood glucose changes, LCMM\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e was constructed using the lcmm package in R (version 4.1.0). During model fitting, combinations of latent class numbers (ranging from 1 to 4) and time effect forms (linear, quadratic, and cubic) were systematically evaluated. The optimal model was selected based on the Bayesian Information Criterion (BIC) and the distribution of sample sizes across classes, resulting in the selection of a two-class cubic trajectory model.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using SPSS Statistics version 29.0 and R software version 4.1.0. Significance was accepted for p \u0026lt; 0.05. Continuous variables were expressed as mean ± standard deviation (SD). For intergroup comparisons, the t-test was applied to normally distributed continuous variables, while the Mann–Whitney U test was used for non-normally distributed continuous variables. Categorical variables were presented as frequencies and percentages, and comparisons were made using the chi-square test or Fisher’s exact test, as appropriate.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLASSO Regression and Nomogram Development\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study aimed to identify risk factors for postoperative intra-abdominal infection (coded as 1 = yes, 0 = no) by analyzing perioperative clinical data to develop a predictive model. A total of 49 variables were included, encompassing preoperative factors (sex, age, BMI, ASA physical status, VTE risk score, NRS-2002, PG-SGA, aCCI, tumor location, history of NACT, preoperative antibiotic use excluding intra-abdominal infection, smoking, drinking, diabetes, hypertension, hyperlipidemia, bowel habits, defecation frequency, fasting blood glucose, ALB, WBC, HB, and AST/ALT ratio), intraoperative factors (operative time, blood loss, blood transfusion), and postoperative factors, including postoperative blood glucose, length of hospital stay, and laboratory parameters measured on POD1 and POD3, including WBC, HB, ALB, PLT, CRP, PCT, NLR, PLR, NMR, LMR, and AST/ALT ratio.\u003c/p\u003e\u003cp\u003eCollinearity was assessed using variance inflation factor (VIF), and variables with VIF \u0026gt; 10 were excluded. For data reduction and feature selection, least absolute shrinkage and selection operator (LASSO) regression analysis was performed using the “cv.glmnet” package in R. The maximum number of iterations was set to 1,000 to ensure computational accuracy, and 10-fold cross-validation was employed to minimize the risk of overfitting\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePotential predictors of PIAI were evaluated using both univariate and multivariate logistic regression analyses. Based on the results of the multivariate analysis, a predictive model was developed, and a nomogram was constructed using the “rms\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e” and “regplot” packages to estimate the probability of PIAI occurrence. Internal validation of the model was performed using the Bootstrap method, and the corrected C-index was calculated to assess the stability of the prediction model. Calibration curves were generated to evaluate the calibration performance of the model. The “pROC” package was utilized to plot the receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC). Additionally, decision curve analysis (DCA) was conducted using the `rmda` package to evaluate the net benefits across different threshold probabilities and confirm the clinical utility of the model. Finally, the clinical impact curve (CIC) was used to assess the loss-to-benefit ratio of the prediction model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 197 patients were assessed for eligibility, and 163 were included after exclusions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 163 patients, there were 87 men and 76 women, with a mean age of 62\u0026thinsp;\u0026plusmn;\u0026thinsp;10 years. The mean BMI was 23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43 kg/m\u0026sup2;. One hundred and forty-six patients (89.6%) underwent laparoscopic surgery, 13 (8.0%) underwent robot-assisted surgery, and 4 (2.4%) underwent open surgery. PIAI occurred in 29 patients (17.8%), whereas 134 patients (82.2%) did not develop infection. The mean postoperative hospital stay for all patients was 9.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12 days.(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohort (n\u0026thinsp;=\u0026thinsp;163)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.20\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVTE score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRS2002 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94 (57.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69 (42.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePGSGA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87 (53.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74 (46.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASA grade(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150 (92.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (8.0%%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of NACT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e152 (93.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131 (80.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96 (58.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147 (90.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibiotic use before surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e158 (97.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaCCI Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAscending colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39(24.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransverse colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescending colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSigmoid colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (14.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75 (46.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperative time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e202.56\u0026thinsp;\u0026plusmn;\u0026thinsp;90.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative blood loss (mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.34\u0026thinsp;\u0026plusmn;\u0026thinsp;55.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical approach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaparoscopic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146(89.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRobot-assisted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13(8.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpen surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4(2.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29(17.79%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e134(82.21%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative hospital stay (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological stage(TNM stage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37(22.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60(36.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48(29.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18(11.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eBMI, body mass index; VTE, Venous Thromboembolism score; NRS, nutrition risk screening; PGSGA, patient-generated subjective global assessment; ASA, American Society of Anesthesiologists; NACT, neoadjuvant chemotherapy; Antibiotic use before surgery, defined as the use of antibiotics prior to surgery for infections other than intraabdominal infection, with surgery performed after infection control; TNM, Tumor-Node-Metastasis; PIAI, postoperative intra-abdominal infection.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePerioperative Patient Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBefore the surgery, compared with the non-PIAI group, patients in the PIAI group had a significantly higher proportion of NRS-2002 scores\u0026thinsp;\u0026ge;\u0026thinsp;3 (62.1% vs. 38.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), PGSGA scores\u0026thinsp;\u0026ge;\u0026thinsp;4 (72.4% vs. 41.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), ASA classification\u0026thinsp;\u0026ge;\u0026thinsp;3 (17.2% vs. 6.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), reduced frequency of defecation prior to surgery (6.9% vs 0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), and preoperative antibiotic use (10.3% vs. 1.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). During the surgery, those in the PIAI group had a higher proportion of tumors located in the descending colon (31.3% vs. 9.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) and experienced greater intraoperative blood loss (230.52\u0026thinsp;\u0026plusmn;\u0026thinsp;108.50 vs.196.51\u0026thinsp;\u0026plusmn;\u0026thinsp;85.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). In the postoperative days, patients in the PIAI group had significantly lower albumin levels on postoperative day 1 (34.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27 vs 38.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31 g/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), higher procalcitonin levels on postoperative day 1 (0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77 vs 0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97 ng/mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher C-reactive protein levels on postoperative day 3 (100.75\u0026thinsp;\u0026plusmn;\u0026thinsp;65.67 vs 50.67\u0026thinsp;\u0026plusmn;\u0026thinsp;78.95 mg/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lower lymphocyte-to-monocyte ratios on postoperative day 3 (2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10 vs 2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed in other variables between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerioperative characteristics of patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-PIAI group\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePIAI group\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (51.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (62.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (48.5%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (52, 72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63(53, 63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.01\u0026thinsp;\u0026plusmn;\u0026thinsp;10.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.03\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVTE score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRS2002 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (38.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (62.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePGSGA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (59.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (27.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (41.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (72.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASA grade (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (94.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (82.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNACT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (93.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (93.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStool characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (53.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (69.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBloody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (46.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStool frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (54.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (44.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (48.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edecrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (85.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (89.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (97.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (80.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (79.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (39.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (48.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (60.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (51.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (88.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (96.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibiotic use before surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (98.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (89.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaCCI score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.57 (2.73, 6.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.72(2.81, 6.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative WBC(10\u003csup\u003e*\u003c/sup\u003e9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative HB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative ALB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.01\u0026thinsp;\u0026plusmn;\u0026thinsp;22.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124.93\u0026thinsp;\u0026plusmn;\u0026thinsp;24.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative AST/ALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.66\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative blood glucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAscending colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransverse colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescending colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSigmoid colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64(47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical approach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaparoscopic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121 (90.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (86.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRobot-assisted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpen surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative blood transfusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (98.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (93.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperative time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196.51\u0026thinsp;\u0026plusmn;\u0026thinsp;85.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230.52\u0026thinsp;\u0026plusmn;\u0026thinsp;108.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eblood loss (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.78\u0026thinsp;\u0026plusmn;\u0026thinsp;40.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.93\u0026thinsp;\u0026plusmn;\u0026thinsp;87.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1WBC (10\u003csup\u003e*\u003c/sup\u003e9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1HB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114.10\u0026thinsp;\u0026plusmn;\u0026thinsp;18.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117.76\u0026thinsp;\u0026plusmn;\u0026thinsp;21.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1PLT (10\u003csup\u003e*\u003c/sup\u003e9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e230.04\u0026thinsp;\u0026plusmn;\u0026thinsp;71.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e244.93\u0026thinsp;\u0026plusmn;\u0026thinsp;75.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1ALB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1CRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.82\u0026thinsp;\u0026plusmn;\u0026thinsp;22.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.70\u0026thinsp;\u0026plusmn;\u0026thinsp;35.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1PCT (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1NLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.19\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.70\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1PLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245.58\u0026thinsp;\u0026plusmn;\u0026thinsp;117.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310.03\u0026thinsp;\u0026plusmn;\u0026thinsp;149.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1LMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1NMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.29\u0026thinsp;\u0026plusmn;\u0026thinsp;22.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.23\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1AST/ALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3WBC (10\u003csup\u003e*\u003c/sup\u003e9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3HB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.69\u0026thinsp;\u0026plusmn;\u0026thinsp;16.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114.90\u0026thinsp;\u0026plusmn;\u0026thinsp;22.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3PLT (10\u003csup\u003e*\u003c/sup\u003e9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212.12\u0026thinsp;\u0026plusmn;\u0026thinsp;74.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e237.83\u0026thinsp;\u0026plusmn;\u0026thinsp;74.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3ALB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3CRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.82\u0026thinsp;\u0026plusmn;\u0026thinsp;39.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.75\u0026thinsp;\u0026plusmn;\u0026thinsp;65.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3PCT (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3NLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3PLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e202.54\u0026thinsp;\u0026plusmn;\u0026thinsp;101.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e290.12\u0026thinsp;\u0026plusmn;\u0026thinsp;119.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3LMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3NMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.86\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.36\u0026thinsp;\u0026plusmn;\u0026thinsp;8.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3AST/ALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative length of hospital stay (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (41.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI, body mass index; VTE, Venous Thromboembolism score; NRS, nutrition risk screening; PGSGA, patient-generated subjective global assessment; ASA, American Society of Anesthesiologists; NACT, neoadjuvant chemotherapy; Antibiotic use before surgery, defined as the use of antibiotics prior to surgery for infections other than intraabdominal infection, with surgery performed after infection control; aCCI, age-adjusted Charlson Comorbidity Index; ALB, albumin; HB, hemoglobin; WBC, white blood cell; AST/ALT, aspartate aminotransferase/alanine aminotransferase ratio; POD, postoperative day; PLT, platelet; CRP, C-reactive protein; PCT, procalcitonin; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; NMR, neutrophil-to-monocyte ratio; LMR, lymphocyte-to-monocyte ratio; AST/ALT, aspartate aminotransferase/alanine aminotransferase; TNM, Tumor-Node-Metastasis.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLCMM of Postoperative Blood Glucose Trajectories\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePostoperative blood glucose values were analyzed using a latent class mixed model (LCMM), which identified two distinct glycemic trajectories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The high blood glucose group, comprising 21 patients (12.9%), exhibited persistently elevated glucose levels. In contrast, the low blood glucose group, including 142 patients (87.1%), demonstrated a relatively stable and lower glucose trajectory.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the overall cohort, the high-glucose group showed a significantly higher postoperative infection rate compared to the low-glucose group (38.1% vs. 14.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Among patients without diabetes, this difference was more pronounced (66.7% vs. 15.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009 Fisher\u0026rsquo;s exact test), while no significant difference was observed in patients with diabetes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.383, Fisher\u0026rsquo;s exact test).(Table\u0026nbsp;3)\u003c/p\u003e\u003cp\u003e\u003cb\u003eLASSO regression analysis for risk factor selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCollinearity analysis was first performed, revealing that POD1 PLT, POD1 NLR, POD1 PLR, POD3 WBC, POD3 PLT, POD3 NLR, and POD3 PLR had variance inflation factors (VIF) greater than 10 and were therefore excluded. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the LASSO regression process for selecting optimal parameters and predictive variables. Ultimately, 15 potential predictors for PIAI were selected, including BMI, PGSGA, ASA, stool frequency, diabetes, intraoperative blood loss, postoperative length of hospital stay, glucose, POD1 HB, POD1 ALB, POD1 PCT, POD3 CRP, POD3 LMR, and POD3 AST/ALT.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk factor selection by logistic regression analysis and nomogram development\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 15 variables identified by LASSO regression were included in univariate logistic regression analysis. Nine variables were identified as potential predictors(Table\u0026nbsp;6). These variables were subsequently included in multivariate logistic regression analysis. The results showed that antibiotic use before surgery (HR\u0026thinsp;=\u0026thinsp;9.292, 95%CI: 1.062\u0026ndash;81.320, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), blood loss (HR\u0026thinsp;=\u0026thinsp;1.011, 95%CI: 1.001\u0026ndash;1.021, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), and POD3 CRP (HR\u0026thinsp;=\u0026thinsp;1.014, 95%CI: 1.004\u0026ndash;1.025, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) were independent risk factors for postoperative intra-abdominal infection(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA predictive nomogram model for PIAI was constructed based on the three independent risk factors (antibiotic use before surgery, POD3 CRP, and intraoperative blood loss) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The nomogram indicates that the risk of postoperative intra-abdominal infection in CRC patients increases with use of preoperative antibiotics, elevated POD3 CRP levels, and greater intraoperative blood loss.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression Analysis of Predictive Factors for PIAI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.888 (0.782\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePGSGA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.770 (1.058\u0026ndash;9.127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.040 (0.992\u0026ndash;9.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.281 (0.989\u0026ndash;10.890)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStool frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.814 (0.845\u0026ndash;3.893)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ediabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.084 (0.401\u0026ndash;2.932)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibiotics use before surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.615 (1.212\u0026ndash;47.852)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.292 (1.062\u0026ndash;81.320)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative blood loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.014 (1.006\u0026ndash;1.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.011 (1.001\u0026ndash;1.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epostoperative blood glucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.282 (0.104\u0026ndash;0.763)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.597 (0.179\u0026ndash;1.992)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative hospital stay (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.191 (1.074\u0026ndash;1.321)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.056 (0.945\u0026ndash;1.179)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1HB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.011 (0.988\u0026ndash;1.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1ALB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.851 (0.749\u0026ndash;0.967)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.905 (0.752\u0026ndash;1.088)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD1PCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.735 (1.014\u0026ndash;2.969)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.343 (0.789\u0026ndash;2.288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3CRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.019 (1.011\u0026ndash;1.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.014 (1.004\u0026ndash;1.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3LMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.493 (0.321\u0026ndash;0.759)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.679 (0.410\u0026ndash;1.125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOD3AST/ALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.525 (0.244\u0026ndash;1.133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePostoperative Blood Glucose of Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-PIAI group\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePIAI group\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow blood glucose group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e121 (85.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh blood glucose group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (38.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubgroup analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo history of diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow blood glucose group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106 (84.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh blood glucose group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo history of diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow blood glucose group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (88.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.383a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh blood glucose group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (73.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (26.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ea, Fisher\u0026rsquo;s exact test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation of the nomogram\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe performance of the predictive model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The calibration curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) closely follows the 45\u0026deg; line, indicating good agreement between predicted and actual outcomes (C-index\u0026thinsp;=\u0026thinsp;0.8111). The ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) yielded an area under the curve (AUC) of 0.8111, demonstrating good discriminatory power and reliability in predicting PIAI in colorectal cancer patients. The decision curve analysis (DCA) in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC indicates that the nomogram offers greater net clinical benefit across a range of threshold probabilities compared to the \u0026ldquo;treat-all\u0026rdquo; or \u0026ldquo;treat-none\u0026rdquo; strategies. The clinical impact curve (CIC) in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD further supports its utility: the red line shows the number of patients predicted to be at risk, while the blue line shows the number of true positives. As the threshold increases, the two lines converge, reflecting better alignment between predictions and actual outcomes. These findings confirm the nomogram\u0026rsquo;s strong predictive performance and clinical applicability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePIAI remains a frequent and serious complication following CRC surgery\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, particularly in patients with compromised immune function due to malignancy and perioperative stress. PIAI is associated with increased morbidity, prolonged hospital stays, and elevated healthcare costs\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, underscoring the critical importance of early identification and timely intervention. In this study, we systematically integrated perioperative clinical indicators\u0026mdash;including demographic characteristics, intraoperative variables, postoperative laboratory parameters (such as inflammatory and hematological markers), and dynamic blood glucose trajectories during the first three postoperative days\u0026mdash;to identify potential predictors of PIAI. Based on LASSO and multivariate logistic regression, we constructed a nomogram with favorable predictive performance, providing a simple and practical tool for early risk assessment and clinical decision-making in CRC patients.\u003c/p\u003e\u003cp\u003ePIAI often presents with insidious and nonspecific early clinical manifestations, making timely and accurate diagnosis challenging\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Once diagnosed, patients commonly present with fever, abdominal pain, purulent drainage, or even require reoperation due to clinical deterioration\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Several studies have reported that the time to clinical diagnosis ranges from postoperative day 6 to 12\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Accordingly, the selection of laboratory indicators from postoperative days 1 and 3 for predictive modeling facilitates the early identification of subtle biochemical alterations that precede the manifestation of overt clinical symptoms, thereby enabling more timely and potentially more effective clinical intervention. Furthermore, the incidence of PIAI in our cohort was 17.8%, which is consistent with previously reported rates ranging from 5\u0026ndash;30% in patients undergoing colorectal surgery\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The consistency with previously reported incidence rates highlights the representativeness of our study population and supports the model's applicability to similar clinical settings.\u003c/p\u003e\u003cp\u003eOur study revealed that preoperative antibiotic treatment for non-abdominal infections was independently associated with an increased risk of PIAI in patients undergoing colorectal cancer surgery, despite all patients receiving standardized prophylactic antibiotics prior to the operation. This finding appears to contrast with the well-established role of prophylactic antibiotic use in reducing postoperative infections\u003csup\u003e[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. However, most studies supporting prophylactic antibiotics have focused on short-term, standardized regimens given immediately before surgery, usually with mechanical bowel preparation and aimed solely at infection prevention. In contrast, longer-term preoperative antibiotic exposure, which has not been adequately considered, may have distinct effects on postoperative outcomes. Recent evidence suggests that antibiotics may increase the risk of PIAI in colorectal cancer patients by disrupting the gut microbiota\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, thereby compromising intestinal barrier function. The intestinal barrier consists of the secretory barrier (mucins, antimicrobial peptides, cytokines), the physical barrier (epithelial cells and tight junctions), and the immune barrier (gut-associated lymphoid tissue and immune cells)\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Antibiotic-induced changes in gut microbiota can weaken this barrier by altering mucin secretion, cytokine production, and antimicrobial peptide expression, thereby increasing intestinal permeability and promoting bacterial translocation. This disruption may contribute to postoperative infections, especially in colorectal surgery patients\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. A nationwide cohort study in Swedish supports this hypothesis, identifying preoperative antibiotic use as a novel risk factor for surgical site infections, including anastomotic leakage, within 30 days following colon cancer surgery\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Similarly, another study, which collected self-reported information on antimicrobial exposure in the 3 months before any elective surgery, reported adverse effects of antibiotics on postoperative complications, including infections\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. These findings highlight that the clinical impact of preoperative antibiotic use may vary depending on timing, indication, and antibiotic class\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Further research is needed to clarify these associations and guide optimal perioperative antibiotic strategies.\u003c/p\u003e\u003cp\u003eIn our study, increased intraoperative blood loss was also found to be independently associated with a higher risk of PIAI.Intraoperative blood loss has been widely reported as a significant risk factor for postoperative intra-abdominal infections in colorectal surgery. Studies have shown that increased blood loss is associated with higher rates of surgical site infections and anastomotic leakage, likely due to impaired tissue perfusion, immune suppression, and longer operative time\u003csup\u003e[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Therefore, minimizing intraoperative bleeding may play an important role in reducing postoperative infectious complications.\u003c/p\u003e\u003cp\u003eIn the nomogram, POD3 CRP levels were also identified as a significant predictor of PIAI following CRC surgery. The current evidence indicates that CRP alone has limited diagnostic accuracy, as it is associated with a relatively low positive predictive value and inadequate sensitivity in certain clinical contexts\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Notably, some researchers caution against overinterpreting inflammatory markers such as leukocyte count, CRP, and procalcitonin (PCT) within the first 24 hours after surgery due to their poor specificity and overlap with normal postoperative inflammatory responses\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. In contrast, CRP levels on POD3 tend to better reflect ongoing pathological inflammation rather than physiological postoperative changes, thus offering improved discrimination for early infectious complications\u003csup\u003e][\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. This timing allows clinicians to detect developing PIAI with greater reliability, aiding timely intervention and improved outcomes.\u003c/p\u003e\u003cp\u003eNotably, this study is use LCMM to identify distinct blood glucose trajectories within the first 72 hours after surgery and to explore their association with PIAI. Chi-square tests and univariate logistic regression revealed a significant association between glucose trajectory groups and PIAI risk, particularly in non-diabetic patients. However, the glucose trajectory variable was not retained in the final multivariate logistic regression model, possibly due to collinearity with diabetes status and limited statistical power. Further subgroup analyses revealed that among non-diabetic patients, those in the \u0026ldquo;high blood glucose group\u0026rdquo; had a substantially increased risk of developing PIAI. These findings suggest that acute perioperative hyperglycemia may contribute to the development of postoperative intra-abdominal infection in colorectal cancer patients without diabetes. This result aligns with previous study, which have reported that stress-induced hyperglycemia can impair immune function and increase susceptibility to infection\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Future research can employ LCMM to further validate these findings and explore whether the integration of glucose trajectory patterns into predictive models improves early risk stratification and informs infection prevention strategies.\u003c/p\u003e\u003cp\u003eThis study has several limitations. It was conducted at a single center with a limited sample size, which may affect the generalizability and reduce the statistical power of the results. Additionally, postoperative inflammatory markers (such as CRP, PCT, and IL-6) are subject to early physiological fluctuations irrespective of complications, suggesting that fixed-time sampling may not fully capture individual inflammatory responses. More individualized sampling strategies may be preferable in clinical practice. Furthermore, the relatively wide intervals between postoperative glucose measurements may have limited the resolution of glucose trajectory modeling and the ability to detect short-term variations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed a simple and clinically applicable model for predicting PIAI in CRC patients, based on preoperative antibiotic use, intraoperative blood loss, and POD3 CRP. The model showed good discrimination and calibration, supporting its potential in early risk identification and preventive management. Additionally, LCMM was applied to characterize postoperative glucose trajectories. While not included in the final model, this method offers a new approach for exploring the impact of perioperative glycemic patterns on surgical outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to Professor Wu from the Public Health School of Fujian Medical University for his valuable statistical advice contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception and design: Y.Y., S.D., Q.L.. Administrative support: Y.Y., Q.L.. Provision of materials and samples: Y.Y., J.S., L.Q. and Z.F.. Acquisition of data: Y.Y., J.S., Analysis and interpretation of data: Y.Y., S.D., and L.Q.. Drafting of manuscript: Y.Y., S.D., J.S.. and Q.L..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunzhe Yu, Sida Sun, Jianshen Chen, Liqun Liao, Junfeng Zhou, Qingliang He have no conflicts of interest or financial ties to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003cbr\u003e\u003c/strong\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Branch for Medical Research and Clinical Technology Application, Ethics Committee of the First Affiliated Hospital of Fujian Medical University (MTCA, ECFAH of FMU [2015]084-3). The requirement for informed consent was waived by the Ethics Committee due to the retrospective nature of the study and the use of anonymized data. Consent for publication was not required as the manuscript does not contain any individual person’s identifiable data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhao P, Yang X, Yan Y, Yang J, Li S, Du X: \u003cstrong\u003eEffect of radical lymphadenectomy in colorectal cancer with para-aortic lymph node metastasis: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eBMC Surg \u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):181.\u003c/li\u003e\n\u003cli\u003eOhishi T, Kaneko MK, Yoshida Y, Takashima A, Kato Y, Kawada M: \u003cstrong\u003eCurrent Targeted Therapy for Metastatic Colorectal Cancer\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2023, \u003cstrong\u003e24\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eBassetti M, Eckmann C, Giacobbe DR, Sartelli M, Montravers P: \u003cstrong\u003ePost-operative abdominal infections: epidemiology, operational definitions, and outcomes\u003c/strong\u003e. \u003cem\u003eIntensive Care Med \u003c/em\u003e2020, \u003cstrong\u003e46\u003c/strong\u003e(2):163-172.\u003c/li\u003e\n\u003cli\u003eFry DE: \u003cstrong\u003eThe prevention of surgical site infection in elective colon surgery\u003c/strong\u003e. \u003cem\u003eScientifica (Cairo) \u003c/em\u003e2013, \u003cstrong\u003e2013\u003c/strong\u003e:896297.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Vel\u0026aacute;zquez P, Pera M, Jim\u0026eacute;nez-Toscano M, Mayol X, Rog\u0026eacute;s X, Lorente L, Iglesias M, Gall\u0026eacute;n M: \u003cstrong\u003ePostoperative intra-abdominal infection is an independent prognostic factor of disease-free survival and disease-specific survival in patients with stage II colon cancer\u003c/strong\u003e. \u003cem\u003eClin Transl Oncol \u003c/em\u003e2018, \u003cstrong\u003e20\u003c/strong\u003e(10):1321-1328.\u003c/li\u003e\n\u003cli\u003eSalvans S, Mayol X, Alonso S, Messeguer R, Pascual M, Mojal S, Grande L, Pera M: \u003cstrong\u003ePostoperative peritoneal infection enhances migration and invasion capacities of tumor cells in vitro: an insight into the association between anastomotic leak and recurrence after surgery for colorectal cancer\u003c/strong\u003e. \u003cem\u003eAnn Surg \u003c/em\u003e2014, \u003cstrong\u003e260\u003c/strong\u003e(5):939-943; discussion 943-934.\u003c/li\u003e\n\u003cli\u003eJia L, Zhao H, Liu J: \u003cstrong\u003eMeta-analysis of postoperative incision infection risk factors in colorectal cancer surgery\u003c/strong\u003e. \u003cem\u003eFront Surg \u003c/em\u003e2024, \u003cstrong\u003e11\u003c/strong\u003e:1415357.\u003c/li\u003e\n\u003cli\u003eParnasa SY, Lev-Cohain N, Bader R, Shweiki A, Mizrahi I, Abu-Gazala M, Pikarsky AJ, Shussman N: \u003cstrong\u003ePredictors of perioperative morbidity in elderly patients undergoing colorectal cancer resection\u003c/strong\u003e. \u003cem\u003eTech Coloproctol \u003c/em\u003e2024, \u003cstrong\u003e29\u003c/strong\u003e(1):4.\u003c/li\u003e\n\u003cli\u003ePei G, Zhen S, Zhang B: \u003cstrong\u003eA novel nomogram based on nutritional and immune status predicting postoperative intra-abdominal infection in colorectal cancer\u003c/strong\u003e. \u003cem\u003eAsia Pac J Clin Nutr \u003c/em\u003e2022, \u003cstrong\u003e31\u003c/strong\u003e(4):626-635.\u003c/li\u003e\n\u003cli\u003eDom\u0026iacute;nguez-Comesa\u0026ntilde;a E, Estevez-Fern\u0026aacute;ndez SM, L\u0026oacute;pez-G\u0026oacute;mez V, Ballinas-Miranda J, Dom\u0026iacute;nguez-Fern\u0026aacute;ndez R: \u003cstrong\u003eProcalcitonin and C-reactive protein as early markers of postoperative intra-abdominal infection in patients operated on colorectal cancer\u003c/strong\u003e. \u003cem\u003eInt J Colorectal Dis \u003c/em\u003e2017, \u003cstrong\u003e32\u003c/strong\u003e(12):1771-1774.\u003c/li\u003e\n\u003cli\u003eLiu CQ, Yu ZB, Gan JX, Mei TM: \u003cstrong\u003ePreoperative blood markers and intra-abdominal infection after colorectal cancer resection\u003c/strong\u003e. \u003cem\u003eWorld J Gastrointest Surg \u003c/em\u003e2024, \u003cstrong\u003e16\u003c/strong\u003e(2):451-462.\u003c/li\u003e\n\u003cli\u003eHulman A, Witte DR, Vistisen D, Balkau B, Dekker JM, Herder C, Hatunic M, Konrad T, F\u0026aelig;rch K, Manco M: \u003cstrong\u003ePathophysiological Characteristics Underlying Different Glucose Response Curves: A Latent Class Trajectory Analysis From the Prospective EGIR-RISC Study\u003c/strong\u003e. \u003cem\u003eDiabetes Care \u003c/em\u003e2018, \u003cstrong\u003e41\u003c/strong\u003e(8):1740-1748.\u003c/li\u003e\n\u003cli\u003eHoran TC, Andrus M, Dudeck MA: \u003cstrong\u003eCDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting\u003c/strong\u003e. \u003cem\u003eAm J Infect Control \u003c/em\u003e2008, \u003cstrong\u003e36\u003c/strong\u003e(5):309-332.\u003c/li\u003e\n\u003cli\u003eKondrup J, Rasmussen HH, Hamberg O, Stanga Z: \u003cstrong\u003eNutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials\u003c/strong\u003e. \u003cem\u003eClin Nutr \u003c/em\u003e2003, \u003cstrong\u003e22\u003c/strong\u003e(3):321-336.\u003c/li\u003e\n\u003cli\u003eIsenring E, Bauer J, Capra S: \u003cstrong\u003eThe scored Patient-generated Subjective Global Assessment (PG-SGA) and its association with quality of life in ambulatory patients receiving radiotherapy\u003c/strong\u003e. \u003cem\u003eEur J Clin Nutr \u003c/em\u003e2003, \u003cstrong\u003e57\u003c/strong\u003e(2):305-309.\u003c/li\u003e\n\u003cli\u003eLau BD, Streiff MB, Pronovost PJ, Haut ER: \u003cstrong\u003eVenous Thromboembolism Quality Measures Fail to Accurately Measure Quality\u003c/strong\u003e. \u003cem\u003eCirculation \u003c/em\u003e2018, \u003cstrong\u003e137\u003c/strong\u003e(12):1278-1284.\u003c/li\u003e\n\u003cli\u003eOwens WD, Felts JA, Spitznagel EL, Jr.: \u003cstrong\u003eASA physical status classifications: a study of consistency of ratings\u003c/strong\u003e. \u003cem\u003eAnesthesiology \u003c/em\u003e1978, \u003cstrong\u003e49\u003c/strong\u003e(4):239-243.\u003c/li\u003e\n\u003cli\u003eCharlson M, Szatrowski TP, Peterson J, Gold J: \u003cstrong\u003eValidation of a combined comorbidity index\u003c/strong\u003e. \u003cem\u003eJ Clin Epidemiol \u003c/em\u003e1994, \u003cstrong\u003e47\u003c/strong\u003e(11):1245-1251.\u003c/li\u003e\n\u003cli\u003eAjani JA, D\u0026apos;Amico TA, Bentrem DJ, Chao J, Cooke D, Corvera C, Das P, Enzinger PC, Enzler T, Fanta P\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology\u003c/strong\u003e. \u003cem\u003eJ Natl Compr Canc Netw \u003c/em\u003e2022, \u003cstrong\u003e20\u003c/strong\u003e(2):167-192.\u003c/li\u003e\n\u003cli\u003eQian B, Zheng Y, Jia H, Zheng X, Gao R, Li W: \u003cstrong\u003eNeutrophil-lymphocyte ratio as a predictive marker for postoperative infectious complications: A systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eHeliyon \u003c/em\u003e2023, \u003cstrong\u003e9\u003c/strong\u003e(5):e15586.\u003c/li\u003e\n\u003cli\u003eKriplani A, Pandit S, Chawla A, de la Rosette J, Laguna P, Jayadeva Reddy S, Somani BK: \u003cstrong\u003eNeutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and lymphocyte-monocyte ratio (LMR) in predicting systemic inflammatory response syndrome (SIRS) and sepsis after percutaneous nephrolithotomy (PNL)\u003c/strong\u003e. \u003cem\u003eUrolithiasis \u003c/em\u003e2022, \u003cstrong\u003e50\u003c/strong\u003e(3):341-348.\u003c/li\u003e\n\u003cli\u003eXia X, Wang Y, Xie M, Qiu S, Zhou J: \u003cstrong\u003eElevated neutrophil - to - monocyte ratio as a prognostic marker for poor outcomes in neonatal sepsis\u003c/strong\u003e. \u003cem\u003eHeliyon \u003c/em\u003e2022, \u003cstrong\u003e8\u003c/strong\u003e(10):e11181.\u003c/li\u003e\n\u003cli\u003eKamonvarapitak T, Matsuda A, Matsumoto S, Jamjittrong S, Sakurazawa N, Kawano Y, Yamada T, Suzuki H, Miyashita M, Yoshida H: \u003cstrong\u003ePreoperative lymphocyte-to-monocyte ratio predicts postoperative infectious complications after laparoscopic colorectal cancer surgery\u003c/strong\u003e. \u003cem\u003eInt J Clin Oncol \u003c/em\u003e2020, \u003cstrong\u003e25\u003c/strong\u003e(4):633-640.\u003c/li\u003e\n\u003cli\u003eProust-Lima C, Philipps V, Liquet B: \u003cstrong\u003eEstimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm\u003c/strong\u003e. \u003cem\u003eJournal of Statistical Software \u003c/em\u003e2017, \u003cstrong\u003e78\u003c/strong\u003e(2):1 - 56.\u003c/li\u003e\n\u003cli\u003eBartholomew TS, Tookes HE, Spencer EC, Feaster DJ: \u003cstrong\u003eApplication of machine learning algorithms for localized syringe services program policy implementation - Florida, 2017\u003c/strong\u003e. \u003cem\u003eAnn Med \u003c/em\u003e2022, \u003cstrong\u003e54\u003c/strong\u003e(1):2137-2150.\u003c/li\u003e\n\u003cli\u003ePark SY: \u003cstrong\u003eNomogram: An analogue tool to deliver digital knowledge\u003c/strong\u003e. \u003cem\u003eJ Thorac Cardiovasc Surg \u003c/em\u003e2018, \u003cstrong\u003e155\u003c/strong\u003e(4):1793.\u003c/li\u003e\n\u003cli\u003eHorton NJ, Kleinman K: \u003cstrong\u003eUsing R and RStudio for data management, statistical analysis, and graphics\u003c/strong\u003e. 2015.\u003c/li\u003e\n\u003cli\u003eKatoh H, Yamashita K, Wang G, Sato T, Nakamura T, Watanabe M: \u003cstrong\u003eAnastomotic leakage contributes to the risk for systemic recurrence in stage II colorectal cancer\u003c/strong\u003e. \u003cem\u003eJ Gastrointest Surg \u003c/em\u003e2011, \u003cstrong\u003e15\u003c/strong\u003e(1):120-129.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Brien WJ, Gupta K, Itani KMF: \u003cstrong\u003eAssociation of Postoperative Infection With Risk of Long-term Infection and Mortality\u003c/strong\u003e. \u003cem\u003eJAMA Surg \u003c/em\u003e2020, \u003cstrong\u003e155\u003c/strong\u003e(1):61-68.\u003c/li\u003e\n\u003cli\u003eZhu R, Hong X, Zhang D, Xiao Y, Xu Q, Wu B, Guo J, Han X, Yang Q, Zhao Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eApplication of metagenomic sequencing of drainage fluid in rapid and accurate diagnosis of postoperative intra-abdominal infection: a diagnostic study\u003c/strong\u003e. \u003cem\u003eInt J Surg \u003c/em\u003e2023, \u003cstrong\u003e109\u003c/strong\u003e(9):2624-2630.\u003c/li\u003e\n\u003cli\u003eAvkan-Oguz V, Baykam N, Sokmen S, Guner R, Agalar F, Alp E, Dogrul A, Turhan O, Agalar C, Kurtaran B\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRecommendations for intra-abdominal infections consensus report\u003c/strong\u003e. \u003cem\u003eUlus Cerrahi Derg \u003c/em\u003e2016, \u003cstrong\u003e32\u003c/strong\u003e(4):306-321.\u003c/li\u003e\n\u003cli\u003eHyman N, Manchester TL, Osler T, Burns B, Cataldo PA: \u003cstrong\u003eAnastomotic leaks after intestinal anastomosis: it\u0026apos;s later than you think\u003c/strong\u003e. \u003cem\u003eAnn Surg \u003c/em\u003e2007, \u003cstrong\u003e245\u003c/strong\u003e(2):254-258.\u003c/li\u003e\n\u003cli\u003ePickleman J, Watson W, Cunningham J, Fisher SG, Gamelli R: \u003cstrong\u003eThe failed gastrointestinal anastomosis: an inevitable catastrophe?\u003c/strong\u003e \u003cem\u003eJ Am Coll Surg \u003c/em\u003e1999, \u003cstrong\u003e188\u003c/strong\u003e(5):473-482.\u003c/li\u003e\n\u003cli\u003ePovsic MK, Beovic B, Ihan A: \u003cstrong\u003ePerioperative Increase in Neutrophil CD64 Expression is an Indicator for Intra-abdominal Infection after Colorectal Cancer Surgery\u003c/strong\u003e. \u003cem\u003eRadiol Oncol \u003c/em\u003e2017, \u003cstrong\u003e51\u003c/strong\u003e(2):211-220.\u003c/li\u003e\n\u003cli\u003eGrewal S, Reuvers JRD, Abis GSA, Otten RHJ, Kazemier G, Stockmann H, van Egmond M, Oosterling SJ: \u003cstrong\u003eOral Antibiotic Prophylaxis Reduces Surgical Site Infection and Anastomotic Leakage in Patients Undergoing Colorectal Cancer Surgery\u003c/strong\u003e. \u003cem\u003eBiomedicines \u003c/em\u003e2021, \u003cstrong\u003e9\u003c/strong\u003e(9).\u003c/li\u003e\n\u003cli\u003eSangiorgio G, Vacante M, Basile F, Biondi A: \u003cstrong\u003eOral and Parenteral vs. Parenteral Antibiotic Prophylaxis for Patients Undergoing Laparoscopic Colorectal Resection: An Intervention Review with Meta-Analysis\u003c/strong\u003e. \u003cem\u003eAntibiotics (Basel) \u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eNelson RL, Hassan M, Grant MD: \u003cstrong\u003eAntibiotic prophylaxis in colorectal surgery: are oral, intravenous or both best and is mechanical bowel preparation necessary?\u003c/strong\u003e \u003cem\u003eTech Coloproctol \u003c/em\u003e2020, \u003cstrong\u003e24\u003c/strong\u003e(12):1233-1246.\u003c/li\u003e\n\u003cli\u003eRamirez J, Guarner F, Bustos Fernandez L, Maruy A, Sdepanian VL, Cohen H: \u003cstrong\u003eAntibiotics as Major Disruptors of Gut Microbiota\u003c/strong\u003e. \u003cem\u003eFront Cell Infect Microbiol \u003c/em\u003e2020, \u003cstrong\u003e10\u003c/strong\u003e:572912.\u003c/li\u003e\n\u003cli\u003eDmytriv TR, Storey KB, Lushchak VI: \u003cstrong\u003eIntestinal barrier permeability: the influence of gut microbiota, nutrition, and exercise\u003c/strong\u003e. \u003cem\u003eFront Physiol \u003c/em\u003e2024, \u003cstrong\u003e15\u003c/strong\u003e:1380713.\u003c/li\u003e\n\u003cli\u003eDuan H, Yu L, Tian F, Zhai Q, Fan L, Chen W: \u003cstrong\u003eAntibiotic-induced gut dysbiosis and barrier disruption and the potential protective strategies\u003c/strong\u003e. \u003cem\u003eCrit Rev Food Sci Nutr \u003c/em\u003e2022, \u003cstrong\u003e62\u003c/strong\u003e(6):1427-1452.\u003c/li\u003e\n\u003cli\u003eLu SSM, Ruteg\u0026aring;rd M, H\u0026auml;ggstr\u0026ouml;m C, Gylfe \u0026Aring;, Harlid S, Van Guelpen B: \u003cstrong\u003ePrior antibiotics exposure is associated with an elevated risk of surgical site infections, including anastomotic leakage, after colon cancer but not rectal cancer surgery: A register-based study of 38,839 patients\u003c/strong\u003e. \u003cem\u003eInt J Cancer \u003c/em\u003e2025, \u003cstrong\u003e156\u003c/strong\u003e(9):1703-1715.\u003c/li\u003e\n\u003cli\u003eGuidry CA, Shah PM, Dietch ZC, Elwood NR, Krebs ED, Mehaffey JH, Sawyer RG: \u003cstrong\u003eRecent Anti-Microbial Exposure Is Associated with More Complications after Elective Surgery\u003c/strong\u003e. \u003cem\u003eSurg Infect (Larchmt) \u003c/em\u003e2018, \u003cstrong\u003e19\u003c/strong\u003e(5):473-479.\u003c/li\u003e\n\u003cli\u003eGao W, Liu X, Zhang S, Wang J, Qiu B, Shao J, Huang W, Huang Y, Yao M, Tang LL: \u003cstrong\u003eAlterations in gut microbiota and inflammatory cytokines after administration of antibiotics in mice\u003c/strong\u003e. \u003cem\u003eMicrobiol Spectr \u003c/em\u003e2024, \u003cstrong\u003e12\u003c/strong\u003e(8):e0309523.\u003c/li\u003e\n\u003cli\u003eCai W, Wang L, Wang W, Zhou T: \u003cstrong\u003eSystematic review and meta-analysis of the risk factors of surgical site infection in patients with colorectal cancer\u003c/strong\u003e. \u003cem\u003eTransl Cancer Res \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e(4):857-871.\u003c/li\u003e\n\u003cli\u003ePost IL, Verheijen PM, Pronk A, Siccama I, Houweling PL: \u003cstrong\u003eIntraoperative blood pressure changes as a risk factor for anastomotic leakage in colorectal surgery\u003c/strong\u003e. \u003cem\u003eInt J Colorectal Dis \u003c/em\u003e2012, \u003cstrong\u003e27\u003c/strong\u003e(6):765-772.\u003c/li\u003e\n\u003cli\u003eSimillis C, Charalambides M, Mavrou A, Afxentiou T, Powar MP, Wheeler J, Davies RJ, Fearnhead NS: \u003cstrong\u003eOperative blood loss adversely affects short and long-term outcomes after colorectal cancer surgery: results of a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eTech Coloproctol \u003c/em\u003e2023, \u003cstrong\u003e27\u003c/strong\u003e(3):189-208.\u003c/li\u003e\n\u003cli\u003eOrtega-Deballon P, Radais F, Facy O, d\u0026apos;Athis P, Masson D, Charles PE, Cheynel N, Favre JP, Rat P: \u003cstrong\u003eC-reactive protein is an early predictor of septic complications after elective colorectal surgery\u003c/strong\u003e. \u003cem\u003eWorld J Surg \u003c/em\u003e2010, \u003cstrong\u003e34\u003c/strong\u003e(4):808-814.\u003c/li\u003e\n\u003cli\u003eGiaccaglia V, Salvi PF, Cunsolo GV, Sparagna A, Antonelli MS, Nigri G, Balducci G, Ziparo V: \u003cstrong\u003eProcalcitonin, as an early biomarker of colorectal anastomotic leak, facilitates enhanced recovery after surgery\u003c/strong\u003e. \u003cem\u003eJ Crit Care \u003c/em\u003e2014, \u003cstrong\u003e29\u003c/strong\u003e(4):528-532.\u003c/li\u003e\n\u003cli\u003eXiao H, Xiao Y, Quan H, Liu W, Pan S, Ouyang Y: \u003cstrong\u003eIntra-abdominal infection after radical gastrectomy for gastric cancer: Incidence, pathogens, risk factors and outcomes\u003c/strong\u003e. \u003cem\u003eInt J Surg \u003c/em\u003e2017, \u003cstrong\u003e48\u003c/strong\u003e:195-200.\u003c/li\u003e\n\u003cli\u003eZhou J, Lin L, He C, Wang Z, Zhan Y, Sun S, He Q: \u003cstrong\u003eIntegrating serological and drainage fluid indicators: developing two predictive models for early detection of postoperative intra-abdominal infections in gastrointestinal tumor patients\u003c/strong\u003e. \u003cem\u003eFront Oncol \u003c/em\u003e2025, \u003cstrong\u003e15\u003c/strong\u003e:1566954.\u003c/li\u003e\n\u003cli\u003eDungan KM, Braithwaite SS, Preiser JC: \u003cstrong\u003eStress hyperglycaemia\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2009, \u003cstrong\u003e373\u003c/strong\u003e(9677):1798-1807.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7179766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7179766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo establish a predictive nomogram model for Postoperative Intra-Abdominal Infection (PIAI) following Colorectal Cancer (CRC) surgery using perioperative clinical variables, thereby facilitating early identification of high-risk patients and enhance postoperative management.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study included colorectal cancer patients undergoing surgery from 2022 to 2024 at a single center. Perioperative clinical and laboratory data, along with blood glucose levels from the day of surgery to postoperative day 3, were collected. PIAI was defined according to the Centers for Disease Control and Prevention (CDC) criteria. Blood glucose trajectories were identified using latent class mixed modeling(LCMM). LASSO and logistic regression analyses were used to select risk factors for PIAI. A predictive nomogram was constructed and internally validated by calibration curve, ROC curve analysis (AUC), decision curve analysis (DCA), and clinical impact curves (CIC).\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eA total of 197 patients' data were collected, and 163 patients were finally included in the study. The incidence of PIAI in the cohort was 17.8%. Compared with patients without PIAI, those who developed infection had significantly higher rates of NRS2002\u0026thinsp;\u0026ge;\u0026thinsp;3 (62.1% vs. 38.1%, P\u0026thinsp;=\u0026thinsp;0.018), PGSGA\u0026thinsp;\u0026ge;\u0026thinsp;4 (72.4% vs. 41.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), ASA grade\u0026thinsp;\u0026ge;\u0026thinsp;3 (17.2% vs 6.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) and preoperative antibiotic use (10.3% vs 1.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), as well as greater intraoperative blood loss (97.9\u0026thinsp;\u0026plusmn;\u0026thinsp;87.9 mL vs 49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;40.8 mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and higher Creactive protein levels on postoperative day 1 (42.7\u0026thinsp;\u0026plusmn;\u0026thinsp;35.2 mg/L vs 25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4 mg/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and day 3 (100.8\u0026thinsp;\u0026plusmn;\u0026thinsp;65.7 mg/L vs. 50.7\u0026thinsp;\u0026plusmn;\u0026thinsp;39.8 mg/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The LCMM model classified postoperative blood glucose trajectories into high and lowglucose groups, with the highglucose group demonstrating a significantly higher infection rate (38.1% vs. 14.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Following further selection, antibiotic use before surgery (HR\u0026thinsp;=\u0026thinsp;9.292, 95%CI: 1.062\u0026ndash;81.320, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), blood loss (HR\u0026thinsp;=\u0026thinsp;1.011, 95%CI: 1.001\u0026ndash;1.021, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), and POD3 CRP (HR\u0026thinsp;=\u0026thinsp;1.014, 95%CI: 1.004\u0026ndash;1.025, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) were incorporated into the prediction model.The AUROC values of the model was 0.8111. The calibration curve, DCA, and CIC demonstrated the favorable clinical applicability of the models.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study established a concise and clinically applicable nomogram for the early prediction of PIAI in CRC patients, incorporating preoperative antibiotic use, intraoperative blood loss, and POD3 CRP as independent predictors. The model demonstrated favorable discrimination and calibration. Furthermore, while not included in the final model, LCMM of postoperative glucose trajectories provided a novel perspective for future research on metabolic patterns and infection risk.\u003c/p\u003e","manuscriptTitle":"A Nomogram Model for Early Prediction of Postoperative Intra-Abdominal Infection in Colorectal Cancer patients Based on Perioperative Clinical Variables ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 00:02:36","doi":"10.21203/rs.3.rs-7179766/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b140c7e-1917-423a-bc27-e016e0e0e941","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T09:48:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 00:02:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7179766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7179766","identity":"rs-7179766","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}