A Nomogram for Predicting Postoperative Pelvic–Abdominal Infection After Total Laparoscopic Hysterectomy for Benign Gynecological Diseases: A Retrospective Study

A Nomogram for Predicting Postoperative Pelvic–Abdominal Infection After Total Laparoscopic Hysterectomy for Benign Gynecological Diseases: A Retrospective Study | 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 for Predicting Postoperative Pelvic–Abdominal Infection After Total Laparoscopic Hysterectomy for Benign Gynecological Diseases: A Retrospective Study Yanchun Zhao, Xudong Hu, Ziang Li, Han Zhang, Lili Zhou, Xiaofeng Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9553954/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective To identify risk factors for postoperative pelvic–abdominal infection after total laparoscopic hysterectomy in patients with benign gynecological diseases and to develop and validate a clinically applicable prediction model. Methods This retrospective study included patients with benign gynecological diseases who underwent total laparoscopic hysterectomy between January 1, 2019 and December 1, 2023. Patients were randomly assigned to a training cohort and a test cohort at a ratio of 7:3. Least absolute shrinkage and selection operator regression was used for variable selection, and restricted cubic spline analysis was performed to assess nonlinear associations. Variables retained after selection were entered into multivariable logistic regression to identify independent risk factors and construct a nomogram. Model performance was evaluated using receiver operating characteristic analysis, calibration curves, bootstrap validation, and decision curve analysis. Results A total of 694 patients were included, with 486 in the training cohort and 208 in the test cohort. Multivariable logistic regression identified BMI of 19.3–22.5 kg/m² as an independent protective factor, whereas adenomyosis, prolonged operative time, and staged surgery were identified as independent risk factors for postoperative pelvic–abdominal infection. The nomogram showed good discrimination, with an area under the curve of 0.814 (95% CI, 0.752–0.866) in the training cohort and 0.771 (95% CI, 0.654–0.869) in the test cohort. The model also showed good calibration and clinical utility. Conclusion This nomogram may serve as a practical tool for perioperative risk assessment and individualized infection prevention in patients with benign gynecological diseases undergoing total laparoscopic hysterectomy. total laparoscopic hysterectomy pelvic-abdominal infection benign gynecological diseases risk prediction nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Postoperative pelvic–abdominal infection, defined as an organ/space surgical site infection (organ/space SSI), is a relatively common and serious complication following gynecological surgery. It primarily includes deep surgical site infections such as pelvic infection, intra-abdominal abscess, and peritonitis. These infections not only delay postoperative recovery, prolong hospital stay, and increase medical costs, but may also progress to life-threatening systemic infections, such as sepsis, in severe cases, thereby substantially compromising patient prognosis and increasing healthcare resource utilization( 1 ). Studies have shown that approximately 40%–60% of surgical site infections are potentially preventable( 2 ). Therefore, early identification of high-risk patients and implementation of targeted preventive strategies remain key priorities in perioperative gynecological management. With the widespread adoption of minimally invasive techniques, total laparoscopic hysterectomy has become a major surgical approach for patients with benign gynecological indications, such as uterine fibroids and adenomyosis. Compared with open surgery, laparoscopic procedures offer advantages such as reduced surgical trauma and faster recovery. Although previous studies have identified several risk factors for postoperative infection, including diabetes, obesity, and prolonged operative time( 3 – 5 ), notable limitations remain in the existing literature. First, study populations are subject to selection bias. Most previous studies have focused on patients with gynecological malignancies or those undergoing open surgery, whereas the postoperative infection risk profile of patients undergoing total laparoscopic hysterectomy for benign gynecological indications—the largest group in routine clinical practice—has not been adequately characterized( 6 ). Second, in this specific population, there has been no systematic evaluation of whether the type of primary disease (e.g., adenomyosis) independently influences the risk of infection, whether staged hysteroscopic and laparoscopic procedures increase infection risk, or the magnitude of the effects of metabolic factors such as diabetes( 7 ). These limitations directly hinder the precise identification and risk stratification of high-risk patients in clinical practice. Accordingly, this retrospective study analyzed the clinical data of patients with benign gynecological indications who underwent total laparoscopic hysterectomy. We systematically assessed the occurrence of postoperative pelvic–abdominal infection, identified independent risk factors, and developed a risk prediction model, which was further evaluated in terms of discrimination and calibration. This study aimed to provide a simple and practical tool for clinical risk assessment to facilitate early identification of high-risk patients and guide individualized preventive strategies. 2 Materials and Methods 2.1 Study population This retrospective study included patients who were admitted to the Department of Gynecology at the Guangdong Provincial Hospital of Chinese Medicine and underwent total laparoscopic hysterectomy between January 1, 2019, and December 1, 2023. The study was approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (Approval No. ZE2026-093) and was conducted in accordance with the principles of the Declaration of Helsinki. Inclusion criteria were as follows: ( 1 ) age ≥ 18 years; ( 2 ) a preoperative clinical diagnosis with postoperative pathological confirmation of benign gynecological indications or lesions, including but not limited to uterine fibroids, adenomyosis, benign ovarian tumors, endometrial lesions, and cervical lesions; ( 3 ) eligibility for total laparoscopic hysterectomy due to benign gynecological indications or lesions, with patients undergoing total laparoscopic hysterectomy, with or without concomitant salpingectomy, adnexectomy, or hysteroscopic procedures; and ( 4 ) complete perioperative clinical data and clearly documented postoperative infectious outcomes, allowing determination of postoperative pelvic–abdominal infection. Exclusion criteria were as follows: ( 1 ) preoperative or postoperative pathological findings indicating gynecological malignancy or borderline tumors; ( 2 ) confirmed preoperative abdominal or pelvic infection, or receipt of systemic anti-infective therapy before surgery; ( 3 ) postoperative infections originating from non-pelvic or non-abdominal sources (e.g., pulmonary or urinary tract infections) without evidence of pelvic or abdominal infection, thereby precluding assessment of pelvic–abdominal infection; and ( 4 ) missing key variables or insufficient data quality for analysis. 2.2 Surgical procedures and antibiotic prophylaxis All patients underwent surgery according to the institutional perioperative management protocol, with total laparoscopic hysterectomy as the primary procedure. Depending on preoperative assessment and lesion characteristics, some patients underwent concomitant salpingectomy, adnexectomy, or hysteroscopic surgery, whereas others underwent staged hysteroscopic surgery before total laparoscopic hysterectomy. All procedures were performed under general anesthesia using standard laparoscopic techniques. After specimen removal and vaginal cuff closure, pelvic hemostasis was confirmed, and a pelvic drain was placed at the surgeon’s discretion. Perioperative antibiotic prophylaxis was administered in accordance with the institutional infection control protocol. Prophylactic antibiotics were administered 30–60 minutes before surgical incision or at anesthesia induction. A repeat intraoperative dose was given if the procedure lasted longer than 3 hours, exceeded twice the half-life of the antimicrobial agent, or if intraoperative blood loss exceeded 1500 mL. The duration of prophylactic antibiotic administration was limited to within 24 hours, but could be extended to 48 hours at the surgeon’s discretion. In special circumstances, the antimicrobial regimen was adjusted based on infection risk and clinical presentation. 2.3 Clinical data collection and study outcomes Perioperative clinical data were retrospectively extracted from the hospital electronic medical record system, including baseline characteristics (age, body mass index [BMI], and history of diabetes and hypertension), surgical indications, preoperative vaginitis and hemoglobin level, surgery-related variables (operative time, blood loss, and intraoperative complications), and postoperative management variables (duration of pelvic drainage and urinary catheterization). The surgical approach was classified as concomitant or staged surgery. Concomitant surgery was defined as total laparoscopic hysterectomy performed in a single stage with concurrent hysteroscopic and laparoscopic procedures, whereas staged surgery was defined as total laparoscopic hysterectomy performed as a separate procedure following a prior hysteroscopic intervention. The primary outcome of this study was postoperative pelvic–abdominal infection, classified as organ/space surgical site infection (organ/space SSI) according to the criteria of the Centers for Disease Control and Prevention (CDC) and the National Healthcare Safety Network (NHSN)( 8 ). The diagnosis required at least one of the following: ( 1 ) purulent drainage from a pelvic or abdominal drain; ( 2 ) isolation of pathogens from intra-abdominal fluid obtained via a surgically placed drain or percutaneous aspiration; ( 3 ) evidence of intra-abdominal infection, such as abdominal or pelvic abscess, identified during reoperation or by imaging (e.g., computed tomography [CT] or ultrasonography); or ( 4 ) a clinical diagnosis of intra-abdominal infection made by the treating clinician based on clinical signs (e.g., fever, abdominal pain, or peritoneal signs), laboratory abnormalities (e.g., elevated white blood cell count, procalcitonin, or C-reactive protein), and imaging findings, with initiation of antimicrobial therapy. To reduce outcome heterogeneity, patients with isolated superficial incisional infection or unexplained fever without a confirmed intra-abdominal infectious focus were excluded. 2.4 Statistical analysis Using R software (version 4.4.2), the included patients were randomly assigned to the training and test cohorts in a 7:3 ratio to ensure random distribution of outcome events between the two cohorts. Data from the training cohort were used for analysis and model development, whereas data from the test cohort were used for further validation of the predictive model. All statistical analyses were performed using R software (version 4.4.2)( 9 ). The overall missing data rate was < 0.1%, and missing values were imputed using the random forest algorithm implemented in the R package “missForest”. Continuous variables were tested for normality. Variables with a normal distribution were expressed as mean ± standard deviation (x̄ ± s) and compared using the independent samples t-test. Variables not normally distributed were described as median (interquartile range) [M (P25, P75)] and compared using the Mann–Whitney U test. Categorical variables were expressed as counts and percentages, and differences between groups were assessed using the chi-square test or Fisher's exact test, as appropriate. A two-sided P < 0.05 was considered statistically significant. 2.5 Variable selection and nomogram construction In the training cohort, all candidate variables were treated as potential predictors and entered the least absolute shrinkage and selection operator (LASSO) regression for variable selection. The optimal penalty parameter (λ) was selected by cross-validation according to the minimum criteria, thereby balancing model complexity and predictive performance. Restricted cubic spline (RCS) analysis was performed to assess potential nonlinear relationships between continuous variables and the outcome( 10 ). For variables demonstrating significant nonlinearity, cutoff points identified from the RCS analysis were used to transform these variables into categorical variables. Variables retained after LASSO selection were then entered into a multivariable logistic regression model to develop a nomogram for postoperative pelvic and abdominal infection after total laparoscopic hysterectomy. 2.6 Nomogram performance and clinical utility Model discrimination was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC). Internal validation was performed using bootstrap resampling (1,000 repetitions), and corresponding confidence intervals (CIs) were calculated. Model calibration was evaluated using the Hosmer–Lemeshow test and visualized with calibration curves. In addition, ROC curves and calibration curves were generated in the test cohort to assess the model’s validation performance and stability. Patients were stratified into high-risk and low-risk groups according to the optimal cutoff value determined from the ROC curve, and the incidence of postoperative pelvic and abdominal infection was compared between the two groups, with odds ratios (ORs) calculated to further evaluate the model’s discriminative ability in clinical risk stratification. Finally, decision curve analysis (DCA) was performed to assess the clinical utility. 3 Results A total of 694 patients were included in this study according to the inclusion and exclusion criteria, of whom 486 were assigned to the training cohort and 208 to the test cohort. The baseline characteristics and clinical features of the two cohorts are shown in Table 1 . No significant differences were observed between the two cohorts in baseline or clinical characteristics. 3.1 Variable selection and risk factor analysis All candidate variables were entered into LASSO regression analysis. At λ = 0.0158, the model achieved an optimal balance between predictive performance and complexity, retaining the most appropriate set of variables with non-zero coefficients, including BMI, blood loss, operative time, surgical indication, diabetes, and staged surgery (Fig. 1 ). Restricted cubic spline (RCS) analysis was performed to evaluate the associations between continuous variables and the risk of postoperative pelvic–abdominal infection. BMI showed a significant overall association with postoperative pelvic–abdominal infection and a significant nonlinear relationship (overall P = 0.029, nonlinear P = 0.016). In contrast, both blood loss (overall P < 0.001, nonlinear P = 0.103) and operative time (overall P < 0.001, nonlinear P = 0.278) were significantly associated with infection risk but showed no evidence of nonlinearity, suggesting predominantly linear relationships. Based on the knot locations of the BMI RCS model, BMI was categorized into four groups using cut-off values of 19.3, 22.5, and 24.8 kg/m²: 24.8 kg/m². The corresponding sample sizes were 248, 204, 207, and 35, respectively. The selected variables were subsequently entered into a multivariable logistic regression model. BMI of 19.3–22.5 kg/m² group, adenomyosis, operative time, and staged surgery were independently associated with postoperative pelvic-abdominal infection (Table 2 ). 3.2 Nomogram construction and validation Based on the six variables selected by LASSO regression, a nomogram model was developed to predict the risk of postoperative pelvic-abdominal infection after undergoing total laparoscopic hysterectomy (Fig. 2 ). The model achieved an AUC of 0.814 (Fig. 3 A), indicating good discriminative ability. To assess its stability and reliability, internal validation was performed using bootstrap resampling (1,000 repetitions), yielding a 95% CI of 0.752–0.866. The Hosmer–Lemeshow test (χ²=5.2946, P = 0.726) and the calibration curve (Fig. 3 B) indicated good calibration of the model. In the test cohort, the model achieved an AUC of 0.771 (95% CI, 0.654–0.869) (Fig. 3 C), and the calibration curve (Fig. 3 D) indicated good calibration in the test cohort. 3.3 Risk stratification To further evaluate the stratification performance of the nomogram in clinical practice, the total nomogram score for each patient was calculated on the basis of the multivariable logistic regression model. Using the Youden index, the optimal cutoff value on the ROC curve (Fig. 3 A) was identified as − 1.910, corresponding to a total nomogram score of 72.25. Accordingly, patients with a nomogram score of > 72.25 were classified as the high-risk group, whereas those with a score < 72.25 were classified as the low-risk group. Logistic regression analysis showed that the incidence of postoperative pelvic-abdominal infection was significantly higher in the high-risk group than in the low-risk group (OR = 8.68, 95% CI, 4.89–16.02, P < 0.001). 3.4 Clinical utility analysis DCA was used to assess the clinical utility of the prediction model (Fig. 4 ). The results showed that, across a range of threshold probabilities, the nomogram yielded a greater net benefit than either the “intervene-all” or “intervene-none” strategy, indicating good clinical utility. The DCA results in the test cohort were broadly consistent with those in the training cohort, further supporting the stability of the model. 4 Discussion Based on real-world clinical data, this study identified factors associated with postoperative pelvic-abdominal infection after total laparoscopic hysterectomy in patients with benign gynecological diseases and developed a prediction model. BMI of 19.3–22.5 kg/m², adenomyosis, prolonged operative time, and staged surgery were independently associated with postoperative pelvic-abdominal infection, indicating that infection risk is influenced by patient, disease, and perioperative factors. This study found a significant nonlinear association between BMI and the risk of postoperative pelvic–abdominal infection, as assessed using RCS analysis. After categorizing BMI into four groups based on the RCS model, the BMI of 19.3–22.5 kg/m² was identified as an independent protective factor. This finding challenges the conventional assumption of a linear relationship between BMI and infection risk, whereby higher BMI is associated with greater risk, and suggests a more complex association in patients undergoing total laparoscopic hysterectomy( 3 , 11 ).Several mechanisms may underlie this observation. On the one hand, BMI reflects not only adiposity but also, to some extent, nutritional status, tissue repair capacity, and tolerance to perioperative stress. On the other hand, patients across different BMI ranges may differ in tissue characteristics, surgical exposure difficulty, and postoperative recovery capacity, which may collectively contribute to the observed nonlinear pattern of infection risk( 12 ). Therefore, BMI may be better interpreted as a clinical indicator requiring stratified assessment. For patients within specific risk ranges, perioperative management should incorporate more targeted strategies for infection risk assessment and monitoring. In patients with benign gynecological diseases, this study identified adenomyosis as an independent risk factor for postoperative pelvic–abdominal infection. Unlike other benign conditions, adenomyosis is characterized by ectopic endometrial tissue invading the myometrium and is often accompanied by recurrent bleeding, chronic inflammation, and uterine enlargement. Cyclical bleeding and necrosis of ectopic endometrial tissue may induce local and systemic low-grade inflammatory responses, potentially affecting perioperative immune function. In addition, uterine enlargement, disruption of myometrial architecture, and fibrotic hyperplasia may increase the technical difficulty of surgery and the extent of tissue trauma. Involvement of the endometrial–myometrial junction may further impair local tissue barrier function( 13 , 14 ). Collectively, these mechanisms may increase susceptibility to postoperative infection in patients with adenomyosis. This finding suggests that adenomyosis should be regarded not only as a surgical indication but also as a clinically relevant marker for infection risk stratification. In such patients, enhanced preoperative risk assessment, minimization of intraoperative tissue injury, and intensified postoperative monitoring of body temperature, inflammatory markers, and signs of pelvic–abdominal infection may be warranted. Prolonged operative time was identified as an independent risk factor in this study, consistent with previous findings on surgical site infection. Longer operative duration often reflects greater procedural complexity, prolonged tissue exposure, and an increased risk of surgical field contamination. It may also indicate difficult dissection, more complex hemostasis, or the need for additional procedures, all of which can contribute to an increased risk of infection. In addition, prolonged surgery may lead to tissue desiccation, local ischemia, intraoperative hypothermia, and increased physiological stress, thereby impairing postoperative wound healing and host defense against infection( 15 , 16 ). Therefore, operative time is not merely a temporal measure but may also serve as an integrated indicator of surgical complexity and perioperative tissue trauma. Accordingly, optimizing preoperative assessment and surgical planning may help improve procedural efficiency and minimize unnecessary operative time while ensuring surgical safety and completeness, thereby reducing the risk of infection( 17 ). This study also found that staged surgery was significantly associated with an increased risk of postoperative pelvic–abdominal infection. In patients who underwent total laparoscopic hysterectomy shortly after an initial hysteroscopic procedure, the first intrauterine intervention may have disrupted the local mucosal barrier and induced tissue injury and inflammatory responses. In addition, intrauterine distension pressure during hysteroscopy may facilitate the trans-tubal passage of microorganisms or inflammatory mediators into the pelvic–abdominal cavity( 18 , 19 ). Performing laparoscopic surgery before complete tissue repair, and while the local immune microenvironment remains altered, may compound the initial injury with additional trauma and potential infectious exposure, thereby increasing the risk of infection. By contrast, completing the relevant procedures in a single session may prolong operative duration but may reduce the risks associated with repeated invasion and cumulative inflammatory responses. These findings may inform clinical decision-making. A single-stage approach may be preferable when both hysteroscopic and laparoscopic procedures are required and feasible. If staged surgery is unavoidable, closer perioperative infection surveillance may be warranted. Based on readily available perioperative variables, this model may help identify patients at high risk of postoperative infection and guide individualized preventive strategies. This study has several limitations. First, the single-center retrospective design may be subject to selection bias and information bias. Second, although multiple clinical variables were included, unmeasured or unrecorded confounding factors may still exist. Third, validation of the prediction model was based on an internal split of the study cohort, and its generalizability requires further evaluation in independent, multicenter populations. Future studies should include larger, multicenter prospective cohorts to externally validate these findings, further refine risk stratification and early warning systems for postoperative pelvic–abdominal infection, and facilitate the clinical implementation of the prediction model. 5 Conclusion This study showed that a BMI of 19.3–22.5 kg/m², adenomyosis, prolonged operative time, and staged surgery were independent factors associated with postoperative pelvic–abdominal infection following total laparoscopic hysterectomy in patients with benign gynecological diseases. The risk prediction model based on these factors may enable early perioperative identification of patients at high risk of infection and provide decision support for the development of individualized prevention and control strategies. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (approval number ZE2026-093). The ethics committees granted an exemption from the requirement for informed consent owing to the retrospective nature of this study. Consent for publication All authors consent to publication. Competing interest The authors declare that they have no competing interest. Funding The authors declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the YuYun Academic Experience Inheritance Studio of Guangdong Provincial Hospital of Chinese Medicine (E43728), the LiuMinru Academic Experience Inheritance Studio of Guangdong Provincial Hospital of Chinese Medicine (DF02202), and the Scientific Research Project of the Guangdong Provincial Administration of Traditional Chinese Medicine (Grant No. 20211193). Author Contribution Huanmei Lin developed the concept of this study. Yanchun Zhao and Xudong Hu led the study. Yanchun Zhao and Ziang Li verified the underlying data and conducted the statistical analysis. Yanchun Zhao and Xudong Hu wrote the original draft. Ziang Li and Lili Zhou contributed to the statistical analysis and study design drafting. Han Zhang and Xiaofeng Chen were mainly in charge of data extraction and entry. Jing Xiao and Huanmei Linwere responsible for data management verification and supervision of the research process. All authors contributed to the manuscript revision and editing. All authors had access to the study data, and the corresponding author holds final responsibility for the decision to submit the manuscript for publication. Acknowledgement The authors would like to thank the gynecologists at the University Town Campus of Guangdong Provincial Hospital of Chinese Medicine for their dedicated efforts in patient care. Data Availability The datasets analyzed during the current study are not publicly available due to patient privacy concerns but are available from the corresponding author on reasonable request. References Seaman SJ, Han E, Arora C, Kim JH. Surgical site infections in gynecology: the latest evidence for prevention and management. Curr Opin Obstet Gynecol. [Journal Article; Review]. 2021 2021/8/1;33(4):296–304. Rezaei AR, Zienkiewicz D, Rezaei AR. Surgical site infections: a comprehensive review. J trauma injury. 2025 2025/6/1;38(2):71–81. Liu Y, Liu Y, Yang Z, Wu J, Li J. Risk factors for surgical site infection (SSI) in patients undergoing hysterectomy: a systematic review and meta-analysis. BMJ OPEN. [Journal Article; Meta-Analysis; Systematic Review]. 2025 2025/6/4;15(6):e93072. Abdallah S, Hammoud SM, Al BH, Loon MM, Salcedo YE, Hassan M et al. Effective Surgical Site Infection Prevention Strategies for Diabetic Patients Undergoing Surgery: A Systematic Review. Cureus. [Journal Article; Review]. 2024 2024/5/1;16(5):e59849. Zeng Y, Lei Y, Li M, Yang S, Liu S, Liu M et al. Risk factors for surgical site infection in patients after hysterectomy: a systematic review and meta-analysis. J HOSP INFECT. [Journal Article; Meta-Analysis; Systematic Review]. 2025 2025/9/1;163:30–41. Meyer R, Niino C, Schneyer R, Hamilton K, Siedhoff MT, Wright KN. Surgical Field Separation in Total Laparoscopic Hysterectomy. OBSTET GYNECOL [Journal Article]. 2024 2024/7/1;144(1):98–100. Salmanov AG, Vitiuk AD, Kovalyshyn OA, Terekhov VA, Patey PM, Kutytska TV, SURGICAL SITE INFECTION AFTER LAPAROSCOPIC HYSTERECTOMY FOR BENIGN GYNECOLOGICAL DISEASE IN UKRAINE, et al. Wiad Lek [Journal Article; Multicenter Study]. 2022;2022/1(20):251–8. Centers For Disease Control And Prevention. National Healthcare Safety Network (NHSN). Patient Safety Component Manual: Chap. 9—Surgical Site Infection (SSI) Event. Atlanta: Centers for Disease Control and Prevention; 2025. R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2024. Harrell JFE. Regression Modeling Strategies:With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd ed. 2015. ed.; 2015. Osler M, Daugbjerg S, Frederiksen BL, Ottesen B. Body mass and risk of complications after hysterectomy on benign indications. HUM REPROD [Journal Article]. 2011;2011/6(1):1512–8. Tjeertes EK, Hoeks SE, Beks SB, Valentijn TM, Hoofwijk AG, Stolker RJ. Obesity–a risk factor for postoperative complications in general surgery? BMC ANESTHESIOL. [Journal Article]. 2015 2015/7/31;15:112. Shi J, Xu Q, Yu S, Zhang T. Perturbations of the endometrial immune microenvironment in endometriosis and adenomyosis: their impact on reproduction and pregnancy. SEMIN IMMUNOPATHOL. [Journal Article; Research Support, Non-U.S. Gov't; Review]. 2025 2025/2/18;47(1):16. Yang B, Li F, Cao G, Nuo M, Shi Y, Wang Z et al. Mechanistic insights into inflammatory cytokines in adenomyosis–induced infertility (Review). INT J MOL MED [Journal Article; Review]. 2026 2026/5/1;57(5). Bu N, Zhao E, Gao Y, Zhao S, Bo W, Kong Z et al. Association between perioperative hypothermia and surgical site infection: A meta-analysis. Medicine (Baltimore). [Journal Article; Meta-Analysis]. 2019 2019/2/1;98(6):e14392. Chen C, Wu H, Wang X, Peng Y, Peng' Y, Lei L et al. Risk factors for surgical site infections following microwave ablation of the uterus: a retrospective cohort study. BMC WOMENS HEALTH [Journal Article]. 2025 2025/7/28;25(1):375. Cheng H, Chen BP, Soleas IM, Ferko NC, Cameron CG, Hinoul P. Prolonged Operative Duration Increases Risk of Surgical Site Infections: A Systematic Review. Surg Infect (Larchmt). [Journal Article; Systematic Review]. 2017 2017/8/1;18(6):722–35. Umranikar S, Clark TJ, Saridogan E, Miligkos D, Arambage K, Torbe E et al. BSGE/ESGE guideline on management of fluid distension media in operative hysteroscopy. Gynecol Surg. [Journal Article; Review]. 2016 2016/1/20;13(4):289–303. Ersahin SS, Ersahin A. Endometrial injury concurrent with hysteroscopy increases the expression of Leukaemia inhibitory factor: a preliminary study. Reprod Biol Endocrinol [Controlled Clin Trial; J Article]. 2022;2022/1(10):11. Tables Table 1 & 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Jun, 2026 Reviewers invited by journal 27 May, 2026 Editor invited by journal 07 May, 2026 Editor assigned by journal 06 May, 2026 Submission checks completed at journal 06 May, 2026 First submitted to journal 28 Apr, 2026 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-9553954","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":647604360,"identity":"7d5a2c83-f06a-4789-a778-5f120186e311","order_by":0,"name":"Yanchun Zhao","email":"","orcid":"","institution":"The Second Clinical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanchun","middleName":"","lastName":"Zhao","suffix":""},{"id":647604363,"identity":"da5425f5-afdb-4d2b-8116-fb3441f051c7","order_by":1,"name":"Xudong Hu","email":"","orcid":"","institution":"The Second Clinical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Hu","suffix":""},{"id":647604364,"identity":"748901b2-b8ba-4000-ab38-ba73a053458c","order_by":2,"name":"Ziang Li","email":"","orcid":"","institution":"The Second Clinical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ziang","middleName":"","lastName":"Li","suffix":""},{"id":647604366,"identity":"378ca535-0049-4117-87fe-987e1d670b42","order_by":3,"name":"Han Zhang","email":"","orcid":"","institution":"The Second Clinical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhang","suffix":""},{"id":647604370,"identity":"3ea54530-d3aa-4ef1-9bb7-0b8326cf7ccf","order_by":4,"name":"Lili Zhou","email":"","orcid":"","institution":"The Second Clinical Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Zhou","suffix":""},{"id":647604380,"identity":"d1d42f9f-08be-4249-ae64-3b8ad75d24ff","order_by":5,"name":"Xiaofeng Chen","email":"","orcid":"","institution":"The Second Clinical Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Chen","suffix":""},{"id":647604381,"identity":"c86dac1b-2234-4bb8-9bb4-8a18bb5805c8","order_by":6,"name":"Jing Xiao","email":"","orcid":"","institution":"State Key Laboratory of Traditional Chinese Medicine Syndrome/Department of Gynecology","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xiao","suffix":""},{"id":647604383,"identity":"c3111358-295d-494b-8126-fcc89b1a2301","order_by":7,"name":"Huanmei Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3PsUrDQBzH8TsCF4d/7Hohoc+Q0jUYH+WOQFxadOx4JdDFPkCL+A4FQRz/8YYuUddAHQQhk0Nu6yCiu+VSN4f7zP8v/H+EOM4/dMoJqcxXCtlWyTfzmcLAV/aEcUI1Z8WQ1Pg+WqsiDpfYm3gI7HFMGtlGgdJp0pz3JNG1QA4o56tcRMHDC5CG0M5MLEn8tMEr/ipLaMXott4BvVFeuL63PTbdIE9aufAnQnywHXgxMi/oS0BouSSX3c+oZ2BcHJOgHvMTIefBAgGOSXSoimECmNNVnQOHqrRuGfDpnTEqhcRXF/tudpZl27LqjCU5hKq/3TuO4zi/fAMakVsHo1UC8QAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Clinical Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Huanmei","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2026-04-28 12:10:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9553954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9553954/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":111130132,"identity":"004a53bc-bd8c-4e97-84d3-80cee3d9ee55","added_by":"auto","created_at":"2026-06-05 10:49:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":156483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelection of predictive variables using LASSO regression analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9553954/v1/280c529a04f43ce4dacccd9a.png"},{"id":111129993,"identity":"4ff02e53-8e30-4b60-ab9c-fbcae179c56a","added_by":"auto","created_at":"2026-06-05 10:49:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting postoperative pelvic–abdominal infection after total laparoscopic hysterectomy in patients with benign gynecological diseases.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9553954/v1/b1ae888ee81f4e1c8c366a1f.png"},{"id":111130176,"identity":"5c2c9522-5e43-43e0-a922-aa63523eecf7","added_by":"auto","created_at":"2026-06-05 10:49:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":288693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance of the nomogram in the training and test cohorts. (A) ROC curve in the training cohort; (B) calibration curve in the training cohort; (C) ROC curve in the test cohort; and (D) calibration curve in the test cohort.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9553954/v1/cdea45738d1de655708dcb1c.png"},{"id":111130067,"identity":"74d558a0-445c-4c9f-a88b-746dbfa1d734","added_by":"auto","created_at":"2026-06-05 10:49:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCA of the prediction model in the training and test cohorts.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9553954/v1/9cbcbfd894dd184146660f7e.png"},{"id":111488158,"identity":"09b24764-ada8-4996-af28-b91832ca697e","added_by":"auto","created_at":"2026-06-08 07:58:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":819688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9553954/v1/f5c5dbd9-3aac-41a2-8f0e-1bf51658611b.pdf"},{"id":111484239,"identity":"c585124f-689b-4671-93d9-364262dfe85b","added_by":"auto","created_at":"2026-06-08 07:45:01","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14330,"visible":true,"origin":"","legend":"","description":"","filename":"Table.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9553954/v1/d934b04d69d9295ee3d71ee2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Nomogram for Predicting Postoperative Pelvic–Abdominal Infection After Total Laparoscopic Hysterectomy for Benign Gynecological Diseases: A Retrospective Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePostoperative pelvic\u0026ndash;abdominal infection, defined as an organ/space surgical site infection (organ/space SSI), is a relatively common and serious complication following gynecological surgery. It primarily includes deep surgical site infections such as pelvic infection, intra-abdominal abscess, and peritonitis. These infections not only delay postoperative recovery, prolong hospital stay, and increase medical costs, but may also progress to life-threatening systemic infections, such as sepsis, in severe cases, thereby substantially compromising patient prognosis and increasing healthcare resource utilization(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Studies have shown that approximately 40%\u0026ndash;60% of surgical site infections are potentially preventable(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Therefore, early identification of high-risk patients and implementation of targeted preventive strategies remain key priorities in perioperative gynecological management.\u003c/p\u003e \u003cp\u003eWith the widespread adoption of minimally invasive techniques, total laparoscopic hysterectomy has become a major surgical approach for patients with benign gynecological indications, such as uterine fibroids and adenomyosis. Compared with open surgery, laparoscopic procedures offer advantages such as reduced surgical trauma and faster recovery. Although previous studies have identified several risk factors for postoperative infection, including diabetes, obesity, and prolonged operative time(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), notable limitations remain in the existing literature. First, study populations are subject to selection bias. Most previous studies have focused on patients with gynecological malignancies or those undergoing open surgery, whereas the postoperative infection risk profile of patients undergoing total laparoscopic hysterectomy for benign gynecological indications\u0026mdash;the largest group in routine clinical practice\u0026mdash;has not been adequately characterized(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Second, in this specific population, there has been no systematic evaluation of whether the type of primary disease (e.g., adenomyosis) independently influences the risk of infection, whether staged hysteroscopic and laparoscopic procedures increase infection risk, or the magnitude of the effects of metabolic factors such as diabetes(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These limitations directly hinder the precise identification and risk stratification of high-risk patients in clinical practice.\u003c/p\u003e \u003cp\u003eAccordingly, this retrospective study analyzed the clinical data of patients with benign gynecological indications who underwent total laparoscopic hysterectomy. We systematically assessed the occurrence of postoperative pelvic\u0026ndash;abdominal infection, identified independent risk factors, and developed a risk prediction model, which was further evaluated in terms of discrimination and calibration. This study aimed to provide a simple and practical tool for clinical risk assessment to facilitate early identification of high-risk patients and guide individualized preventive strategies.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThis retrospective study included patients who were admitted to the Department of Gynecology at the Guangdong Provincial Hospital of Chinese Medicine and underwent total laparoscopic hysterectomy between January 1, 2019, and December 1, 2023. The study was approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (Approval No. ZE2026-093) and was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eInclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a preoperative clinical diagnosis with postoperative pathological confirmation of benign gynecological indications or lesions, including but not limited to uterine fibroids, adenomyosis, benign ovarian tumors, endometrial lesions, and cervical lesions; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) eligibility for total laparoscopic hysterectomy due to benign gynecological indications or lesions, with patients undergoing total laparoscopic hysterectomy, with or without concomitant salpingectomy, adnexectomy, or hysteroscopic procedures; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) complete perioperative clinical data and clearly documented postoperative infectious outcomes, allowing determination of postoperative pelvic\u0026ndash;abdominal infection.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) preoperative or postoperative pathological findings indicating gynecological malignancy or borderline tumors; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) confirmed preoperative abdominal or pelvic infection, or receipt of systemic anti-infective therapy before surgery; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) postoperative infections originating from non-pelvic or non-abdominal sources (e.g., pulmonary or urinary tract infections) without evidence of pelvic or abdominal infection, thereby precluding assessment of pelvic\u0026ndash;abdominal infection; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) missing key variables or insufficient data quality for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Surgical procedures and antibiotic prophylaxis\u003c/h2\u003e \u003cp\u003eAll patients underwent surgery according to the institutional perioperative management protocol, with total laparoscopic hysterectomy as the primary procedure. Depending on preoperative assessment and lesion characteristics, some patients underwent concomitant salpingectomy, adnexectomy, or hysteroscopic surgery, whereas others underwent staged hysteroscopic surgery before total laparoscopic hysterectomy. All procedures were performed under general anesthesia using standard laparoscopic techniques. After specimen removal and vaginal cuff closure, pelvic hemostasis was confirmed, and a pelvic drain was placed at the surgeon\u0026rsquo;s discretion.\u003c/p\u003e \u003cp\u003ePerioperative antibiotic prophylaxis was administered in accordance with the institutional infection control protocol. Prophylactic antibiotics were administered 30\u0026ndash;60 minutes before surgical incision or at anesthesia induction. A repeat intraoperative dose was given if the procedure lasted longer than 3 hours, exceeded twice the half-life of the antimicrobial agent, or if intraoperative blood loss exceeded 1500 mL. The duration of prophylactic antibiotic administration was limited to within 24 hours, but could be extended to 48 hours at the surgeon\u0026rsquo;s discretion. In special circumstances, the antimicrobial regimen was adjusted based on infection risk and clinical presentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Clinical data collection and study outcomes\u003c/h2\u003e \u003cp\u003ePerioperative clinical data were retrospectively extracted from the hospital electronic medical record system, including baseline characteristics (age, body mass index [BMI], and history of diabetes and hypertension), surgical indications, preoperative vaginitis and hemoglobin level, surgery-related variables (operative time, blood loss, and intraoperative complications), and postoperative management variables (duration of pelvic drainage and urinary catheterization). The surgical approach was classified as concomitant or staged surgery. Concomitant surgery was defined as total laparoscopic hysterectomy performed in a single stage with concurrent hysteroscopic and laparoscopic procedures, whereas staged surgery was defined as total laparoscopic hysterectomy performed as a separate procedure following a prior hysteroscopic intervention.\u003c/p\u003e \u003cp\u003eThe primary outcome of this study was postoperative pelvic\u0026ndash;abdominal infection, classified as organ/space surgical site infection (organ/space SSI) according to the criteria of the Centers for Disease Control and Prevention (CDC) and the National Healthcare Safety Network (NHSN)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The diagnosis required at least one of the following: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) purulent drainage from a pelvic or abdominal drain; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) isolation of pathogens from intra-abdominal fluid obtained via a surgically placed drain or percutaneous aspiration; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) evidence of intra-abdominal infection, such as abdominal or pelvic abscess, identified during reoperation or by imaging (e.g., computed tomography [CT] or ultrasonography); or (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) a clinical diagnosis of intra-abdominal infection made by the treating clinician based on clinical signs (e.g., fever, abdominal pain, or peritoneal signs), laboratory abnormalities (e.g., elevated white blood cell count, procalcitonin, or C-reactive protein), and imaging findings, with initiation of antimicrobial therapy. To reduce outcome heterogeneity, patients with isolated superficial incisional infection or unexplained fever without a confirmed intra-abdominal infectious focus were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eUsing R software (version 4.4.2), the included patients were randomly assigned to the training and test cohorts in a 7:3 ratio to ensure random distribution of outcome events between the two cohorts. Data from the training cohort were used for analysis and model development, whereas data from the test cohort were used for further validation of the predictive model.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.4.2)(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The overall missing data rate was \u0026lt;\u0026thinsp;0.1%, and missing values were imputed using the random forest algorithm implemented in the R package \u0026ldquo;missForest\u0026rdquo;. Continuous variables were tested for normality. Variables with a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s) and compared using the independent samples t-test. Variables not normally distributed were described as median (interquartile range) [M (P25, P75)] and compared using the Mann\u0026ndash;Whitney U test. Categorical variables were expressed as counts and percentages, and differences between groups were assessed using the chi-square test or Fisher's exact test, as appropriate. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Variable selection and nomogram construction\u003c/h2\u003e \u003cp\u003eIn the training cohort, all candidate variables were treated as potential predictors and entered the least absolute shrinkage and selection operator (LASSO) regression for variable selection. The optimal penalty parameter (λ) was selected by cross-validation according to the minimum criteria, thereby balancing model complexity and predictive performance. Restricted cubic spline (RCS) analysis was performed to assess potential nonlinear relationships between continuous variables and the outcome(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). For variables demonstrating significant nonlinearity, cutoff points identified from the RCS analysis were used to transform these variables into categorical variables. Variables retained after LASSO selection were then entered into a multivariable logistic regression model to develop a nomogram for postoperative pelvic and abdominal infection after total laparoscopic hysterectomy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Nomogram performance and clinical utility\u003c/h2\u003e \u003cp\u003eModel discrimination was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC). Internal validation was performed using bootstrap resampling (1,000 repetitions), and corresponding confidence intervals (CIs) were calculated. Model calibration was evaluated using the Hosmer\u0026ndash;Lemeshow test and visualized with calibration curves. In addition, ROC curves and calibration curves were generated in the test cohort to assess the model\u0026rsquo;s validation performance and stability. Patients were stratified into high-risk and low-risk groups according to the optimal cutoff value determined from the ROC curve, and the incidence of postoperative pelvic and abdominal infection was compared between the two groups, with odds ratios (ORs) calculated to further evaluate the model\u0026rsquo;s discriminative ability in clinical risk stratification. Finally, decision curve analysis (DCA) was performed to assess the clinical utility.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eA total of 694 patients were included in this study according to the inclusion and exclusion criteria, of whom 486 were assigned to the training cohort and 208 to the test cohort. The baseline characteristics and clinical features of the two cohorts are shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed between the two cohorts in baseline or clinical characteristics.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Variable selection and risk factor analysis\u003c/h2\u003e\n \u003cp\u003eAll candidate variables were entered into LASSO regression analysis. At \u0026lambda;\u0026thinsp;=\u0026thinsp;0.0158, the model achieved an optimal balance between predictive performance and complexity, retaining the most appropriate set of variables with non-zero coefficients, including BMI, blood loss, operative time, surgical indication, diabetes, and staged surgery (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRestricted cubic spline (RCS) analysis was performed to evaluate the associations between continuous variables and the risk of postoperative pelvic\u0026ndash;abdominal infection. BMI showed a significant overall association with postoperative pelvic\u0026ndash;abdominal infection and a significant nonlinear relationship (overall \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, nonlinear \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). In contrast, both blood loss (overall \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, nonlinear \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.103) and operative time (overall \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, nonlinear \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.278) were significantly associated with infection risk but showed no evidence of nonlinearity, suggesting predominantly linear relationships. Based on the knot locations of the BMI RCS model, BMI was categorized into four groups using cut-off values of 19.3, 22.5, and 24.8 kg/m\u0026sup2;: \u0026lt;19.3 kg/m\u0026sup2;, 19.3\u0026ndash;22.5 kg/m\u0026sup2;, 22.5\u0026ndash;24.8 kg/m\u0026sup2;, and \u0026gt;\u0026thinsp;24.8 kg/m\u0026sup2;. The corresponding sample sizes were 248, 204, 207, and 35, respectively. The selected variables were subsequently entered into a multivariable logistic regression model. BMI of 19.3\u0026ndash;22.5 kg/m\u0026sup2; group, adenomyosis, operative time, and staged surgery were independently associated with postoperative pelvic-abdominal infection (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Nomogram construction and validation\u003c/h2\u003e\n \u003cp\u003eBased on the six variables selected by LASSO regression, a nomogram model was developed to predict the risk of postoperative pelvic-abdominal infection after undergoing total laparoscopic hysterectomy (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model achieved an AUC of 0.814 (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), indicating good discriminative ability. To assess its stability and reliability, internal validation was performed using bootstrap resampling (1,000 repetitions), yielding a 95% CI of 0.752\u0026ndash;0.866. The Hosmer\u0026ndash;Lemeshow test (\u0026chi;\u0026sup2;=5.2946, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.726) and the calibration curve (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) indicated good calibration of the model. In the test cohort, the model achieved an AUC of 0.771 (95% CI, 0.654\u0026ndash;0.869) (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and the calibration curve (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) indicated good calibration in the test cohort.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Risk stratification\u003c/h2\u003e\n \u003cp\u003eTo further evaluate the stratification performance of the nomogram in clinical practice, the total nomogram score for each patient was calculated on the basis of the multivariable logistic regression model. Using the Youden index, the optimal cutoff value on the ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) was identified as \u0026minus;\u0026thinsp;1.910, corresponding to a total nomogram score of 72.25. Accordingly, patients with a nomogram score of \u0026gt;\u0026thinsp;72.25 were classified as the high-risk group, whereas those with a score\u0026thinsp;\u0026lt;\u0026thinsp;72.25 were classified as the low-risk group. Logistic regression analysis showed that the incidence of postoperative pelvic-abdominal infection was significantly higher in the high-risk group than in the low-risk group (OR\u0026thinsp;=\u0026thinsp;8.68, 95% CI, 4.89\u0026ndash;16.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Clinical utility analysis\u003c/h2\u003e\n \u003cp\u003eDCA was used to assess the clinical utility of the prediction model (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results showed that, across a range of threshold probabilities, the nomogram yielded a greater net benefit than either the \u0026ldquo;intervene-all\u0026rdquo; or \u0026ldquo;intervene-none\u0026rdquo; strategy, indicating good clinical utility. The DCA results in the test cohort were broadly consistent with those in the training cohort, further supporting the stability of the model.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBased on real-world clinical data, this study identified factors associated with postoperative pelvic-abdominal infection after total laparoscopic hysterectomy in patients with benign gynecological diseases and developed a prediction model. BMI of 19.3\u0026ndash;22.5 kg/m\u0026sup2;, adenomyosis, prolonged operative time, and staged surgery were independently associated with postoperative pelvic-abdominal infection, indicating that infection risk is influenced by patient, disease, and perioperative factors.\u003c/p\u003e \u003cp\u003eThis study found a significant nonlinear association between BMI and the risk of postoperative pelvic\u0026ndash;abdominal infection, as assessed using RCS analysis. After categorizing BMI into four groups based on the RCS model, the BMI of 19.3\u0026ndash;22.5 kg/m\u0026sup2; was identified as an independent protective factor. This finding challenges the conventional assumption of a linear relationship between BMI and infection risk, whereby higher BMI is associated with greater risk, and suggests a more complex association in patients undergoing total laparoscopic hysterectomy(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).Several mechanisms may underlie this observation. On the one hand, BMI reflects not only adiposity but also, to some extent, nutritional status, tissue repair capacity, and tolerance to perioperative stress. On the other hand, patients across different BMI ranges may differ in tissue characteristics, surgical exposure difficulty, and postoperative recovery capacity, which may collectively contribute to the observed nonlinear pattern of infection risk(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, BMI may be better interpreted as a clinical indicator requiring stratified assessment. For patients within specific risk ranges, perioperative management should incorporate more targeted strategies for infection risk assessment and monitoring.\u003c/p\u003e \u003cp\u003eIn patients with benign gynecological diseases, this study identified adenomyosis as an independent risk factor for postoperative pelvic\u0026ndash;abdominal infection. Unlike other benign conditions, adenomyosis is characterized by ectopic endometrial tissue invading the myometrium and is often accompanied by recurrent bleeding, chronic inflammation, and uterine enlargement. Cyclical bleeding and necrosis of ectopic endometrial tissue may induce local and systemic low-grade inflammatory responses, potentially affecting perioperative immune function. In addition, uterine enlargement, disruption of myometrial architecture, and fibrotic hyperplasia may increase the technical difficulty of surgery and the extent of tissue trauma. Involvement of the endometrial\u0026ndash;myometrial junction may further impair local tissue barrier function(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Collectively, these mechanisms may increase susceptibility to postoperative infection in patients with adenomyosis. This finding suggests that adenomyosis should be regarded not only as a surgical indication but also as a clinically relevant marker for infection risk stratification. In such patients, enhanced preoperative risk assessment, minimization of intraoperative tissue injury, and intensified postoperative monitoring of body temperature, inflammatory markers, and signs of pelvic\u0026ndash;abdominal infection may be warranted.\u003c/p\u003e \u003cp\u003eProlonged operative time was identified as an independent risk factor in this study, consistent with previous findings on surgical site infection. Longer operative duration often reflects greater procedural complexity, prolonged tissue exposure, and an increased risk of surgical field contamination. It may also indicate difficult dissection, more complex hemostasis, or the need for additional procedures, all of which can contribute to an increased risk of infection. In addition, prolonged surgery may lead to tissue desiccation, local ischemia, intraoperative hypothermia, and increased physiological stress, thereby impairing postoperative wound healing and host defense against infection(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Therefore, operative time is not merely a temporal measure but may also serve as an integrated indicator of surgical complexity and perioperative tissue trauma. Accordingly, optimizing preoperative assessment and surgical planning may help improve procedural efficiency and minimize unnecessary operative time while ensuring surgical safety and completeness, thereby reducing the risk of infection(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This study also found that staged surgery was significantly associated with an increased risk of postoperative pelvic\u0026ndash;abdominal infection. In patients who underwent total laparoscopic hysterectomy shortly after an initial hysteroscopic procedure, the first intrauterine intervention may have disrupted the local mucosal barrier and induced tissue injury and inflammatory responses. In addition, intrauterine distension pressure during hysteroscopy may facilitate the trans-tubal passage of microorganisms or inflammatory mediators into the pelvic\u0026ndash;abdominal cavity(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Performing laparoscopic surgery before complete tissue repair, and while the local immune microenvironment remains altered, may compound the initial injury with additional trauma and potential infectious exposure, thereby increasing the risk of infection. By contrast, completing the relevant procedures in a single session may prolong operative duration but may reduce the risks associated with repeated invasion and cumulative inflammatory responses.\u003c/p\u003e \u003cp\u003eThese findings may inform clinical decision-making. A single-stage approach may be preferable when both hysteroscopic and laparoscopic procedures are required and feasible. If staged surgery is unavoidable, closer perioperative infection surveillance may be warranted. Based on readily available perioperative variables, this model may help identify patients at high risk of postoperative infection and guide individualized preventive strategies.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the single-center retrospective design may be subject to selection bias and information bias. Second, although multiple clinical variables were included, unmeasured or unrecorded confounding factors may still exist. Third, validation of the prediction model was based on an internal split of the study cohort, and its generalizability requires further evaluation in independent, multicenter populations. Future studies should include larger, multicenter prospective cohorts to externally validate these findings, further refine risk stratification and early warning systems for postoperative pelvic\u0026ndash;abdominal infection, and facilitate the clinical implementation of the prediction model.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study showed that a BMI of 19.3\u0026ndash;22.5 kg/m\u0026sup2;, adenomyosis, prolonged operative time, and staged surgery were independent factors associated with postoperative pelvic\u0026ndash;abdominal infection following total laparoscopic hysterectomy in patients with benign gynecological diseases. The risk prediction model based on these factors may enable early perioperative identification of patients at high risk of infection and provide decision support for the development of individualized prevention and control strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (approval number ZE2026-093). The ethics committees granted an exemption from the requirement for informed consent owing to the retrospective nature of this study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e All authors consent to publication.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interest\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the YuYun Academic Experience Inheritance Studio of Guangdong Provincial Hospital of Chinese Medicine (E43728), the LiuMinru Academic Experience Inheritance Studio of Guangdong Provincial Hospital of Chinese Medicine (DF02202), and the Scientific Research Project of the Guangdong Provincial Administration of Traditional Chinese Medicine (Grant No. 20211193).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHuanmei Lin developed the concept of this study. Yanchun Zhao and Xudong Hu led the study. Yanchun Zhao and Ziang Li verified the underlying data and conducted the statistical analysis. Yanchun Zhao and Xudong Hu wrote the original draft. Ziang Li and Lili Zhou contributed to the statistical analysis and study design drafting. Han Zhang and Xiaofeng Chen were mainly in charge of data extraction and entry. Jing Xiao and Huanmei Linwere responsible for data management verification and supervision of the research process. All authors contributed to the manuscript revision and editing. All authors had access to the study data, and the corresponding author holds final responsibility for the decision to submit the manuscript for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors would like to thank the gynecologists at the University Town Campus of Guangdong Provincial Hospital of Chinese Medicine for their dedicated efforts in patient care.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are not publicly available due to patient privacy concerns but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSeaman SJ, Han E, Arora C, Kim JH. Surgical site infections in gynecology: the latest evidence for prevention and management. Curr Opin Obstet Gynecol. [Journal Article; Review]. 2021 2021/8/1;33(4):296\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezaei AR, Zienkiewicz D, Rezaei AR. Surgical site infections: a comprehensive review. J trauma injury. 2025 2025/6/1;38(2):71\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Liu Y, Yang Z, Wu J, Li J. Risk factors for surgical site infection (SSI) in patients undergoing hysterectomy: a systematic review and meta-analysis. BMJ OPEN. [Journal Article; Meta-Analysis; Systematic Review]. 2025 2025/6/4;15(6):e93072.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdallah S, Hammoud SM, Al BH, Loon MM, Salcedo YE, Hassan M et al. Effective Surgical Site Infection Prevention Strategies for Diabetic Patients Undergoing Surgery: A Systematic Review. Cureus. [Journal Article; Review]. 2024 2024/5/1;16(5):e59849.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng Y, Lei Y, Li M, Yang S, Liu S, Liu M et al. Risk factors for surgical site infection in patients after hysterectomy: a systematic review and meta-analysis. J HOSP INFECT. [Journal Article; Meta-Analysis; Systematic Review]. 2025 2025/9/1;163:30\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer R, Niino C, Schneyer R, Hamilton K, Siedhoff MT, Wright KN. Surgical Field Separation in Total Laparoscopic Hysterectomy. OBSTET GYNECOL [Journal Article]. 2024 2024/7/1;144(1):98\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalmanov AG, Vitiuk AD, Kovalyshyn OA, Terekhov VA, Patey PM, Kutytska TV, SURGICAL SITE INFECTION AFTER LAPAROSCOPIC HYSTERECTOMY FOR BENIGN GYNECOLOGICAL DISEASE IN UKRAINE, et al. Wiad Lek [Journal Article; Multicenter Study]. 2022;2022/1(20):251\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenters For Disease Control And Prevention. National Healthcare Safety Network (NHSN). Patient Safety Component Manual: Chap. 9\u0026mdash;Surgical Site Infection (SSI) Event. Atlanta: Centers for Disease Control and Prevention; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrell JFE. Regression Modeling Strategies:With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd ed. 2015. ed.; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsler M, Daugbjerg S, Frederiksen BL, Ottesen B. Body mass and risk of complications after hysterectomy on benign indications. HUM REPROD [Journal Article]. 2011;2011/6(1):1512\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTjeertes EK, Hoeks SE, Beks SB, Valentijn TM, Hoofwijk AG, Stolker RJ. Obesity\u0026ndash;a risk factor for postoperative complications in general surgery? BMC ANESTHESIOL. [Journal Article]. 2015 2015/7/31;15:112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi J, Xu Q, Yu S, Zhang T. Perturbations of the endometrial immune microenvironment in endometriosis and adenomyosis: their impact on reproduction and pregnancy. SEMIN IMMUNOPATHOL. [Journal Article; Research Support, Non-U.S. Gov't; Review]. 2025 2025/2/18;47(1):16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang B, Li F, Cao G, Nuo M, Shi Y, Wang Z et al. Mechanistic insights into inflammatory cytokines in adenomyosis\u0026ndash;induced infertility (Review). INT J MOL MED [Journal Article; Review]. 2026 2026/5/1;57(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBu N, Zhao E, Gao Y, Zhao S, Bo W, Kong Z et al. Association between perioperative hypothermia and surgical site infection: A meta-analysis. Medicine (Baltimore). [Journal Article; Meta-Analysis]. 2019 2019/2/1;98(6):e14392.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen C, Wu H, Wang X, Peng Y, Peng' Y, Lei L et al. Risk factors for surgical site infections following microwave ablation of the uterus: a retrospective cohort study. BMC WOMENS HEALTH [Journal Article]. 2025 2025/7/28;25(1):375.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng H, Chen BP, Soleas IM, Ferko NC, Cameron CG, Hinoul P. Prolonged Operative Duration Increases Risk of Surgical Site Infections: A Systematic Review. Surg Infect (Larchmt). [Journal Article; Systematic Review]. 2017 2017/8/1;18(6):722\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmranikar S, Clark TJ, Saridogan E, Miligkos D, Arambage K, Torbe E et al. BSGE/ESGE guideline on management of fluid distension media in operative hysteroscopy. Gynecol Surg. [Journal Article; Review]. 2016 2016/1/20;13(4):289\u0026ndash;303.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErsahin SS, Ersahin A. Endometrial injury concurrent with hysteroscopy increases the expression of Leukaemia inhibitory factor: a preliminary study. Reprod Biol Endocrinol [Controlled Clin Trial; J Article]. 2022;2022/1(10):11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 \u0026 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"total laparoscopic hysterectomy, pelvic-abdominal infection, benign gynecological diseases, risk prediction, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-9553954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9553954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo identify risk factors for postoperative pelvic\u0026ndash;abdominal infection after total laparoscopic hysterectomy in patients with benign gynecological diseases and to develop and validate a clinically applicable prediction model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included patients with benign gynecological diseases who underwent total laparoscopic hysterectomy between January 1, 2019 and December 1, 2023. Patients were randomly assigned to a training cohort and a test cohort at a ratio of 7:3. Least absolute shrinkage and selection operator regression was used for variable selection, and restricted cubic spline analysis was performed to assess nonlinear associations. Variables retained after selection were entered into multivariable logistic regression to identify independent risk factors and construct a nomogram. Model performance was evaluated using receiver operating characteristic analysis, calibration curves, bootstrap validation, and decision curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 694 patients were included, with 486 in the training cohort and 208 in the test cohort. Multivariable logistic regression identified BMI of 19.3\u0026ndash;22.5 kg/m\u0026sup2; as an independent protective factor, whereas adenomyosis, prolonged operative time, and staged surgery were identified as independent risk factors for postoperative pelvic\u0026ndash;abdominal infection. The nomogram showed good discrimination, with an area under the curve of 0.814 (95% CI, 0.752\u0026ndash;0.866) in the training cohort and 0.771 (95% CI, 0.654\u0026ndash;0.869) in the test cohort. The model also showed good calibration and clinical utility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis nomogram may serve as a practical tool for perioperative risk assessment and individualized infection prevention in patients with benign gynecological diseases undergoing total laparoscopic hysterectomy.\u003c/p\u003e","manuscriptTitle":"A Nomogram for Predicting Postoperative Pelvic–Abdominal Infection After Total Laparoscopic Hysterectomy for Benign Gynecological Diseases: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-06-05 10:48:31","doi":"10.21203/rs.3.rs-9553954/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"179263900448667179070274916856331615119","date":"2026-06-05T11:20:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-27T07:59:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-07T04:03:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-06T09:02:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-06T09:01:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2026-04-28T11:58:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4158b475-2541-4376-b676-0e10887a71cb","owner":[],"postedDate":"June 5th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"179263900448667179070274916856331615119","date":"2026-06-05T11:20:01+00:00","index":52,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-27T07:59:54+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-06-05T10:48:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-06-05 10:48:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9553954","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9553954","identity":"rs-9553954","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}