Advances in predictive modelling of surgical site infections following colorectal cancer surgery_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

MA Wenxiang ¹ , LIU Xusheng ¹ , DU Nan ¹ , XIE Jiajing ¹ , YANG Yang ¹ ,  YU Yongjiang ²

1. The First Clinical Medical College of Lanzhou University, Lanzhou 730000, P. R. China;
2. Department of Gastrointestinal Surgery, Hernia and Abdominal Wall Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, P. R. China;

Corresponding author：

YU Yongjiang, Email: ylongy@163.com

Keywords：

colorectal cancer; surgical site infection; clinical prediction model

DOI：

10.7507/1007-9424.202412077

Video：

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Objective To evaluate existing predictive models for surgical site infection (SSI) following colorectal cancer (CRC) surgery, aiming to provide a scientific basis for refining risk prediction models and developing clinically practical and widely applicable screening tools. Method A comprehensive review of existing literature on predictive models for SSI following CRC surgery, both domestically and internationally, were conducted. Results The determination of SSI following CRC surgery primarily relied on the Centers for Disease Control and Prevention standards of USA, which present issues of consistency and accuracy. Various predictive models had been developed, including traditional statistical models and machine learning models, with 0.991 of an area under the operating characteristic curve of predictive model. However, most studies were based on retrospective and single-center data, which limited their applicability and accuracy. Conclusions Although existing models provide strong support for predicting SSI following CRC surgery, there is a need for multi-center, prospective studies to enhance the generalizability and accuracy of these models. Additionally, future research should focus on improving model interpretability to better apply them in clinical practice, providing personalized risk assessments and intervention strategies for patients.

1.	Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	黄理宾, 黄秋实, 杨烈. 全球及中国的结直肠癌流行病学特征及防治: 2022《全球癌症统计报告》解读. 中国普外基础与临床杂志, 2024, 31(5): 530-537.
3.	Ladabaum U, Dominitz JA, Kahi C, et al. Strategies for colorectal cancer screening. Gastroenterology, 2020, 158(2): 418-432.
4.	Gillespie BM, Harbeck E, Rattray M, et al. Worldwide incidence of surgical site infections in general surgical patients: A systematic review and meta-analysis of 488 594 patients. Int J Surg, 2021, 95: 106136. doi: 10.1016/j.ijsu.2021.106136.
5.	Leaper DJ, Holy CE, Spencer M, et al. Assessment of the risk and economic burden of surgical site infection following colorectal surgery using a US longitudinal database: Is there a role for innovative antimicrobial wound closure technology to reduce the risk of infection?. Dis Colon Rectum, 2020, 63(12): 1628-1638.
6.	Costa ACD, Santa-Cruz F, Torres AV, et al. Surgical Site Infection In Resections Of Digestive System Tumours. Arq Bras Cir Dig, 2024, 37: e1817. doi: 10.1590/0102-6720202400024e1817.
7.	Miyamoto Y, Hiyoshi Y, Tokunaga R, et al. Postoperative complications are associated with poor survival outcome after curative resection for colorectal cancer: A propensity-score analysis. J Surg Oncol, 2020, 122(2): 344-349.
8.	Koike T, Mukai M, Kishima K, et al. The association between surgical site infection and prognosis of T4 colorectal cancer. Cureus, 2024, 16(8): e66138. doi: 10.7759/cureus.66138.
9.	Hedrick TL, Sawyer RG, Hennessy SA, et al. Can we define surgical site infection accurately in colorectal surgery?. Surg Infect (Larchmt), 2014, 15(4): 372-376.
10.	Hedrick TL, Harrigan AM, Sawyer RG, et al. Defining surgical site infection in colorectal surgery: An objective analysis using serial photographic documentation. Dis Colon Rectum, 2015, 58(11): 1070-1077.
11.	阳怡羽, 张旭飞, 朱建伟, 等. 结直肠术后手术部位感染临床预测模型的建立和验证. 中华胃肠外科杂志, 2023, 26(9): 837-846.
12.	WHO guidelines approved by the Guidelines Review Committee. Global guidelines for the prevention of surgical site infection. Geneva: World Health Organization. © World Health Organization 2018. 2018.
13.	高铭, 戴刚, 蔡卓玮, 等. 结直肠癌术后并发症的风险预测模型. 中国普外基础与临床杂志, 2021, 28(2): 252-255.
14.	Seidelman J, Anderson DJ. Surgical site infections. Infect Dis Clin North Am, 2021, 35(4): 901-929.
15.	McLean KA, Goel T, Lawday S, et al. Prognostic models for surgical-site infection in gastrointestinal surgery: systematic review. Br J Surg, 2023, 110(11): 1441-1450.
16.	Horan TC, Gaynes RP, Martone WJ, et al. CDC definitions of nosocomial surgical site infections, 1992: A modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol, 1992, 13(10): 606-608.
17.	Wilson AP, Treasure T, Sturridge MF, et al. A scoring method (ASEPSIS) for postoperative wound infections for use in clinical trials of antibiotic prophylaxis. Lancet, 1986, 1(8476): 311-313.
18.	Bailey IS, Karran SE, Toyn K, et al. Community surveillance of complications after hernia surgery. BMJ, 1992, 304(6825): 469-471.
19.	Macefield RC, Reeves BC, Milne TK, et al. Development of a single, practical measure of surgical site infection (SSI) for patient report or observer completion. J Infect Prev, 2017, 18(4): 170-179.
20.	Tuuli MG, Liu J, Tita ATN, et al. Effect of prophylactic negative pressure wound therapy vs standard wound dressing on surgical-site infection in obese women after cesarean delivery: A randomized clinical trial. JAMA, 2020, 324(12): 1180-1189.
21.	Campwala I, Unsell K, Gupta S. A comparative analysis of surgical wound infection methods: Predictive values of the CDC, ASEPSIS, and Southampton Scoring Systems in evaluating breast reconstruction surgical site infections. Plast Surg (Oakv), 2019, 27(2): 93-99.
22.	王宝鸿, 黄靓妍, 王彧杰, 等. 手术部位感染判定标准研究进展. 中国消毒学杂志, 2023, 40(6): 460-464.
23.	Siah CJ, Childs C. A systematic review of the ASEPSIS scoring system used in non-cardiac-related surgery. J Wound Care, 2012, 21(3): 124, 126-130.
24.	Claroni C, Marcelli ME, Sofra MC, et al. Preperitoneal continuous infusion of local anesthetics: What is the impact on surgical wound infections in humans? Pain Med, 2016, 17(3): 582-589.
25.	Macefield R, Brookes S, Blazeby J, et al. Development of a ‘universal-reporter’ outcome measure (UROM) for patient and healthcare professional completion: a mixed methods study demonstrating a novel concept for optimal questionnaire design. BMJ Open, 2019, 9(8): e029741. doi: 10.1136/bmjopen-2019-029741.
26.	Bluebelle Study Group. Validation of the Bluebelle Wound Healing Questionnaire for assessment of surgical-site infection in closed primary wounds after hospital discharge. Br J Surg, 2019, 106(3): 226-235.
27.	Shen Z, Lin Y, Ye Y, et al. The development and validation of a novel model for predicting surgical complications in colorectal cancer of elderly patients: Results from 1008 cases. Eur J Surg Oncol, 2018, 44(4): 490-495.
28.	董健, 谭伟, 吴丰, 等. 老年腹腔镜结直肠手术患者手术部位感染列线图预测模型的构建和评估. 中国现代普通外科进展, 2024, 27(10): 818-820.
29.	Gervaz P, Bandiera-Clerc C, Buchs NC, et al. Scoring system to predict the risk of surgical-site infection after colorectal resection. Br J Surg, 2012, 99(4): 589-595.
30.	Verras GI, Mulita F. Butyrylcholinesterase levels correlate with surgical site infection risk and severity after colorectal surgery: a prospective single-center study. Front Surg, 2024, 11: 1379410. doi: 10.3389/fsurg.2024.1379410.
31.	Kerin Povšič M, Ihan A, Beovič B. Post-operative infection is an independent risk factor for worse long-term survival after colorectal cancer surgery. Surg Infect (Larchmt), 2016, 17(6): 700-712.
32.	符国宏, 赵宇青, 郑杨慈, 等. 结直肠癌根治术后合并切口感染病原学及其决策树预测模型构建. 中华医院感染学杂志, 2023, 33(21): 3270-3274.
33.	Li J, Yan Z. Machine learning model predicting factors for incisional infection following right hemicolectomy for colon cancer. BMC Surg, 2024, 24(1): 279. doi: 10.1186/s12893-024-02543-8.
34.	Boubekki A, Myhre JN, Luppino LT, et al. Clinically relevant features for predicting the severity of surgical site infections. IEEE J Biomed Health Inform, 2022, 26(4): 1794-1801.
35.	Kocbek P, Fijacko N, Soguero-Ruiz C, et al. Maximizing interpretability and cost-effectiveness of Surgical Site Infection (SSI) predictive models using feature-specific regularized logistic regression on preoperative temporal data. Comput Math Methods Med, 2019, 2019: 2059851. doi: 10.1155/2019/2059851.
36.	徐铖斌, 徐平, 葛茂军, 等. 基于机器学习的结直肠手术部位感染预测模型建立. 华西医学, 2020, 35(7): 827-832.
37.	Mao F, Song M, Cao Y, et al. Development and validation of a preoperative systemic inflammation-based nomogram for predicting surgical site infection in patients with colorectal cancer. Int J Colorectal Dis, 2024, 39(1): 208. doi: 10.1007/s00384-024-04772-y.
38.	Chen KA, Joisa CU, Stem JM, et al. Improved prediction of surgical-site infection after colorectal surgery using machine learning. Dis Colon Rectum, 2023, 66(3): 458-466.
39.	Meurer WJ, Tolles J. Logistic regression diagnostics: Understanding how well a model predicts outcomes. JAMA, 2017, 317(10): 1068-1069.
40.	Christodoulou E, Ma J, Collins GS, et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol, 2019, 110: 12-22.
41.	Harrell F E. Cox Proportional Hazards Regression Model [M]//HARRELL J F E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Cham; Springer International Publishing. 2015: 475-519.
42.	Cioci AC, Cioci AL, Mantero AMA, et al. Advanced Statistics: Multiple Logistic Regression, Cox Proportional Hazards, and Propensity Scores. Surg Infect (Larchmt), 2021, 22(6): 604-610.
43.	Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol, 2019, 20(5): e262-e273. doi: 10.1016/S1470-2045(19)30149-4.
44.	Mccarthy R V, Mccarthy M M, Ceccucci W, et al. Predictive models using decision trees // McCarthy RV, Mccarthy MM, Ceccucci W, et al. Applying predictive analytics: Finding value in data. Cham: Springer International Publishing, 2019: 123-144.
45.	Fritz BA, Cui Z, Zhang M, et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth, 2019, 123(5): 688-695.
46.	Ke G, Meng Q, Finley T, et al. Lightgbm: Ahighly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017: 3146-3154.
47.	Ren NS , Cao X , Wei NY , et al. Global refinement of random forest. IEEE Conference on Computer Vision & Pattern Recognition. IEEE Computer Society, 2015. doi:10.1109/CVPR.2015.7298672.
48.	Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273-297.
49.	Ian Goodfellow HJ, Bengio Y, Courville A. Deep learning. Genet Program Evolvable Mach, 2018, 19(1): 305-307.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. 黄理宾, 黄秋实, 杨烈. 全球及中国的结直肠癌流行病学特征及防治: 2022《全球癌症统计报告》解读. 中国普外基础与临床杂志, 2024, 31(5): 530-537.
3. Ladabaum U, Dominitz JA, Kahi C, et al. Strategies for colorectal cancer screening. Gastroenterology, 2020, 158(2): 418-432.
4. Gillespie BM, Harbeck E, Rattray M, et al. Worldwide incidence of surgical site infections in general surgical patients: A systematic review and meta-analysis of 488 594 patients. Int J Surg, 2021, 95: 106136. doi: 10.1016/j.ijsu.2021.106136.
5. Leaper DJ, Holy CE, Spencer M, et al. Assessment of the risk and economic burden of surgical site infection following colorectal surgery using a US longitudinal database: Is there a role for innovative antimicrobial wound closure technology to reduce the risk of infection?. Dis Colon Rectum, 2020, 63(12): 1628-1638.
6. Costa ACD, Santa-Cruz F, Torres AV, et al. Surgical Site Infection In Resections Of Digestive System Tumours. Arq Bras Cir Dig, 2024, 37: e1817. doi: 10.1590/0102-6720202400024e1817.
7. Miyamoto Y, Hiyoshi Y, Tokunaga R, et al. Postoperative complications are associated with poor survival outcome after curative resection for colorectal cancer: A propensity-score analysis. J Surg Oncol, 2020, 122(2): 344-349.
8. Koike T, Mukai M, Kishima K, et al. The association between surgical site infection and prognosis of T4 colorectal cancer. Cureus, 2024, 16(8): e66138. doi: 10.7759/cureus.66138.
9. Hedrick TL, Sawyer RG, Hennessy SA, et al. Can we define surgical site infection accurately in colorectal surgery?. Surg Infect (Larchmt), 2014, 15(4): 372-376.
10. Hedrick TL, Harrigan AM, Sawyer RG, et al. Defining surgical site infection in colorectal surgery: An objective analysis using serial photographic documentation. Dis Colon Rectum, 2015, 58(11): 1070-1077.
11. 阳怡羽, 张旭飞, 朱建伟, 等. 结直肠术后手术部位感染临床预测模型的建立和验证. 中华胃肠外科杂志, 2023, 26(9): 837-846.
12. WHO guidelines approved by the Guidelines Review Committee. Global guidelines for the prevention of surgical site infection. Geneva: World Health Organization. © World Health Organization 2018. 2018.
13. 高铭, 戴刚, 蔡卓玮, 等. 结直肠癌术后并发症的风险预测模型. 中国普外基础与临床杂志, 2021, 28(2): 252-255.
14. Seidelman J, Anderson DJ. Surgical site infections. Infect Dis Clin North Am, 2021, 35(4): 901-929.
15. McLean KA, Goel T, Lawday S, et al. Prognostic models for surgical-site infection in gastrointestinal surgery: systematic review. Br J Surg, 2023, 110(11): 1441-1450.
16. Horan TC, Gaynes RP, Martone WJ, et al. CDC definitions of nosocomial surgical site infections, 1992: A modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol, 1992, 13(10): 606-608.
17. Wilson AP, Treasure T, Sturridge MF, et al. A scoring method (ASEPSIS) for postoperative wound infections for use in clinical trials of antibiotic prophylaxis. Lancet, 1986, 1(8476): 311-313.
18. Bailey IS, Karran SE, Toyn K, et al. Community surveillance of complications after hernia surgery. BMJ, 1992, 304(6825): 469-471.
19. Macefield RC, Reeves BC, Milne TK, et al. Development of a single, practical measure of surgical site infection (SSI) for patient report or observer completion. J Infect Prev, 2017, 18(4): 170-179.
20. Tuuli MG, Liu J, Tita ATN, et al. Effect of prophylactic negative pressure wound therapy vs standard wound dressing on surgical-site infection in obese women after cesarean delivery: A randomized clinical trial. JAMA, 2020, 324(12): 1180-1189.
21. Campwala I, Unsell K, Gupta S. A comparative analysis of surgical wound infection methods: Predictive values of the CDC, ASEPSIS, and Southampton Scoring Systems in evaluating breast reconstruction surgical site infections. Plast Surg (Oakv), 2019, 27(2): 93-99.
22. 王宝鸿, 黄靓妍, 王彧杰, 等. 手术部位感染判定标准研究进展. 中国消毒学杂志, 2023, 40(6): 460-464.
23. Siah CJ, Childs C. A systematic review of the ASEPSIS scoring system used in non-cardiac-related surgery. J Wound Care, 2012, 21(3): 124, 126-130.
24. Claroni C, Marcelli ME, Sofra MC, et al. Preperitoneal continuous infusion of local anesthetics: What is the impact on surgical wound infections in humans? Pain Med, 2016, 17(3): 582-589.
25. Macefield R, Brookes S, Blazeby J, et al. Development of a ‘universal-reporter’ outcome measure (UROM) for patient and healthcare professional completion: a mixed methods study demonstrating a novel concept for optimal questionnaire design. BMJ Open, 2019, 9(8): e029741. doi: 10.1136/bmjopen-2019-029741.
26. Bluebelle Study Group. Validation of the Bluebelle Wound Healing Questionnaire for assessment of surgical-site infection in closed primary wounds after hospital discharge. Br J Surg, 2019, 106(3): 226-235.
27. Shen Z, Lin Y, Ye Y, et al. The development and validation of a novel model for predicting surgical complications in colorectal cancer of elderly patients: Results from 1008 cases. Eur J Surg Oncol, 2018, 44(4): 490-495.
28. 董健, 谭伟, 吴丰, 等. 老年腹腔镜结直肠手术患者手术部位感染列线图预测模型的构建和评估. 中国现代普通外科进展, 2024, 27(10): 818-820.
29. Gervaz P, Bandiera-Clerc C, Buchs NC, et al. Scoring system to predict the risk of surgical-site infection after colorectal resection. Br J Surg, 2012, 99(4): 589-595.
30. Verras GI, Mulita F. Butyrylcholinesterase levels correlate with surgical site infection risk and severity after colorectal surgery: a prospective single-center study. Front Surg, 2024, 11: 1379410. doi: 10.3389/fsurg.2024.1379410.
31. Kerin Povšič M, Ihan A, Beovič B. Post-operative infection is an independent risk factor for worse long-term survival after colorectal cancer surgery. Surg Infect (Larchmt), 2016, 17(6): 700-712.
32. 符国宏, 赵宇青, 郑杨慈, 等. 结直肠癌根治术后合并切口感染病原学及其决策树预测模型构建. 中华医院感染学杂志, 2023, 33(21): 3270-3274.
33. Li J, Yan Z. Machine learning model predicting factors for incisional infection following right hemicolectomy for colon cancer. BMC Surg, 2024, 24(1): 279. doi: 10.1186/s12893-024-02543-8.
34. Boubekki A, Myhre JN, Luppino LT, et al. Clinically relevant features for predicting the severity of surgical site infections. IEEE J Biomed Health Inform, 2022, 26(4): 1794-1801.
35. Kocbek P, Fijacko N, Soguero-Ruiz C, et al. Maximizing interpretability and cost-effectiveness of Surgical Site Infection (SSI) predictive models using feature-specific regularized logistic regression on preoperative temporal data. Comput Math Methods Med, 2019, 2019: 2059851. doi: 10.1155/2019/2059851.
36. 徐铖斌, 徐平, 葛茂军, 等. 基于机器学习的结直肠手术部位感染预测模型建立. 华西医学, 2020, 35(7): 827-832.
37. Mao F, Song M, Cao Y, et al. Development and validation of a preoperative systemic inflammation-based nomogram for predicting surgical site infection in patients with colorectal cancer. Int J Colorectal Dis, 2024, 39(1): 208. doi: 10.1007/s00384-024-04772-y.
38. Chen KA, Joisa CU, Stem JM, et al. Improved prediction of surgical-site infection after colorectal surgery using machine learning. Dis Colon Rectum, 2023, 66(3): 458-466.
39. Meurer WJ, Tolles J. Logistic regression diagnostics: Understanding how well a model predicts outcomes. JAMA, 2017, 317(10): 1068-1069.
40. Christodoulou E, Ma J, Collins GS, et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol, 2019, 110: 12-22.
41. Harrell F E. Cox Proportional Hazards Regression Model [M]//HARRELL J F E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Cham; Springer International Publishing. 2015: 475-519.
42. Cioci AC, Cioci AL, Mantero AMA, et al. Advanced Statistics: Multiple Logistic Regression, Cox Proportional Hazards, and Propensity Scores. Surg Infect (Larchmt), 2021, 22(6): 604-610.
43. Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol, 2019, 20(5): e262-e273. doi: 10.1016/S1470-2045(19)30149-4.
44. Mccarthy R V, Mccarthy M M, Ceccucci W, et al. Predictive models using decision trees // McCarthy RV, Mccarthy MM, Ceccucci W, et al. Applying predictive analytics: Finding value in data. Cham: Springer International Publishing, 2019: 123-144.
45. Fritz BA, Cui Z, Zhang M, et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth, 2019, 123(5): 688-695.
46. Ke G, Meng Q, Finley T, et al. Lightgbm: Ahighly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017: 3146-3154.
47. Ren NS , Cao X , Wei NY , et al. Global refinement of random forest. IEEE Conference on Computer Vision & Pattern Recognition. IEEE Computer Society, 2015. doi:10.1109/CVPR.2015.7298672.
48. Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273-297.
49. Ian Goodfellow HJ, Bengio Y, Courville A. Deep learning. Genet Program Evolvable Mach, 2018, 19(1): 305-307.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

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