Prognostic Value of Surgical Site Infection in Patients After Radical Colorectal Cancer Resection

Development and validation of a preoperative systemic inflammation-based nomogram for predicting surgical site infection in patients with colorectal cancer

BACKGROUND

Surgical site infection (SSI) represents a significant postoperative complication in colorectal cancer (CRC). Identifying associated factors is therefore critical. We evaluated the predictive value of clinicopathological features and inflammation-based prognostic scores (IBPSs) for SSI occurrence in CRC patients.

METHODS

We retrospectively analyzed data from 1445 CRC patients who underwent resection surgery at Wuhan Union Hospital between January 2015 and December 2018. We applied two algorithms, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), to identify key predictors. Participants were randomly divided into training (n = 1043) and validation (n = 402) cohorts. A nomogram was constructed to estimate SSI risk, and its performance was assessed by calibration, discrimination, and clinical utility.

RESULTS

Combining the 30 clinicopathological features identified by LASSO and SVM-RFE, we pinpointed seven variables as optimal predictors for a pathology-based nomogram: obstruction, dNLR, ALB, HGB, ALT, CA199, and CA125. The model demonstrated strong calibration and discrimination, with an area under the curve (AUC) of 0.838 (95% CI 0.799-0.876) in the training cohort and 0.793 (95% CI 0.732-0.865) in the validation cohort. Decision curve analysis (DCA) showed that our models provided greater predictive benefit than individual clinical markers.

CONCLUSION

The model based on simplified clinicopathological features in combination with IBPSs is useful in predicting SSI for CRC patients.

Machine learning model predicting factors for incisional infection following right hemicolectomy for colon cancer

BACKGROUND AND AIM

Colorectal cancer is a prevalent malignancy worldwide, and right hemicolectomy is a common surgical procedure for its treatment. However, postoperative incisional infections remain a significant complication, leading to prolonged hospital stays, increased healthcare costs, and patient discomfort. Therefore, this study aims to utilize machine learning models, including random forest, support vector machine, deep learning models, and traditional logistic regression, to predict factors associated with incisional infection following right hemicolectomy for colon cancer.

METHODS

Clinical data were collected from 322 patients undergoing right hemicolectomy for colon cancer, including demographic information, preoperative chemotherapy status, body mass index (BMI), operative time, and other relevant variables. These data are divided into training and testing sets in a ratio of 7:3. Machine learning models, including random forest, support vector machine, and deep learning, were trained using the training set and evaluated using the testing set.

RESULTS

The deep learning model exhibited the highest performance in predicting incisional infection, followed by random forest and logistic regression models. Specifically, the deep learning model demonstrated higher area under the receiver operating characteristic curve (ROC-AUC) and F1 score compared to other models. These findings suggest the efficacy of machine learning models in predicting risk factors for incisional infection following right hemicolectomy for colon cancer.

CONCLUSIONS

Machine learning models, particularly deep learning models, offer a promising approach for predicting the risk of incisional infection following right hemicolectomy for colon cancer. These models can provide valuable decision support for clinicians, facilitating personalized treatment strategies and improving patient outcomes.

The Association Between Surgical Site Infection and Prognosis of T4 Colorectal Cancer

OBJECTIVES

Patients with T4 colorectal cancer have poor prognosis, wherein no prognostic factors have been established. Surgical site infection (SSI) has been reported to be one of the risk factors for colorectal cancer recurrence. In this study, we evaluated the relationship between SSI occurrence and prognosis of T4 colorectal cancer and the prognostic impact of the site of SSI occurrence.

METHODS

We examined 100 patients with T4 colorectal cancer who underwent radical surgery between April 2002 and December 2017, in a retrospective case-control study, excluding stage IV cases, and classified them into two groups: without SSI (non-SSI) and with SSI (SSI). The five-year relapse-free survival (RFS) and overall survival (OS) were calculated and compared between the two groups. The relationship between prognosis and the SSI site was also assessed according to the SSI site in the incisional/deep and organ/space SSI groups.  Results: The without SSI and with SSI groups included 73 and 27 patients, respectively. The five-year RFS was 55.1% and 22.2% in the without SSI and with SSI groups, respectively (hazard ratio (HR), 2.224; 95% confidence interval (CI), 1.269-3.898; P=0.005). The five-year OS was 67.0% and 38.4% in the without SSI and with SSI groups, respectively (HR, 2.366; 95% CI, 1.223-4.575; P=0.010). The patients in the with SSI group had a significantly poorer prognosis compared with the without SSI group. By SSI site, the prognosis was significantly worse in patients with SSI in the incisional/deep SSI group.

CONCLUSIONS

In T4 colorectal cancer, SSI occurrence was a high-risk factor for recurrence and may be a prognostic factor. This result suggested that patients with SSI occurrence may require close postoperative follow-up and appropriate adjuvant chemotherapy.

Surgical Site Infections in Colorectal Cancer Surgeries: A Systematic Review and Meta-Analysis of the Impact of Surgical Approach and Associated Risk Factors

BACKGROUND

Surgical site infections (SSIs) represent a noteworthy contributor to both morbidity and mortality in the context of patients who undergo colorectal surgery. Several risk factors have been identified; however, their relative significance remains uncertain.

METHODS

We conducted a meta-analysis of observational studies from their inception up until 2023 that investigated risk factors for SSIs in colorectal surgery. A random-effects model was used to pool the data and calculate the odds ratio (OR) and 95% confidence interval (CI) for each risk factor.

RESULTS

Our analysis included 26 studies with a total of 61,426 patients. The pooled results showed that male sex (OR = 1.45), body mass index (BMI) ≥ 25 kg/m2 (OR = 1.09), American Society of Anesthesiologists (ASA) score ≥ 3 (OR = 1.69), were all independent risk factors for SSIs in colorectal surgery. Conversely, laparoscopic surgery (OR = 0.70) was found to be a protective factor.

CONCLUSIONS

The meta-analysis conducted revealed various risk factors, both modifiable and non-modifiable, associated with surgical site infections (SSIs) in colorectal surgery. These findings emphasize the significance of targeted interventions, including optimizing glycemic control, minimizing blood loss, and using laparoscopic techniques whenever feasible in order to decrease the occurrence of surgical site infections in this particular group of patients.

The incidence of surgical site infections in China

Surgical site infections (SSIs) are a common type of healthcare-associated infection. We performed a literature review to demonstrate the incidence of SSIs in mainland China based on studies since 2010. We included 231 eligible studies with ≥30 postoperative patients, comprising 14 providing overall SSI data regardless of surgical sites and 217 reporting SSIs for a specific site. We found that the overall SSI incidence was 2.91% (median; interquartile range: 1.05%, 4.57%) or 3.18% (pooled; 95% confidence interval: 1.85%, 4.51%) and the SSI incidence varied remarkably according to the surgical site between the lowest (median, 1.00%; pooled, 1.69%) in thyroid surgeries and the highest (median, 14.89%; pooled, 12.54%) in colorectal procedures. We uncovered that Enterobacterales and staphylococci were the most common types of micro-organisms associated with SSIs after various abdominal surgeries and cardiac or neurological procedures, respectively. We identified two, nine, and five studies addressing the impact of SSIs on mortality, the length of stay (LOS) in hospital, and additional healthcare-related economic burden, respectively, all of which demonstrated increased mortality, prolonged LOS, and elevated medical costs associated with SSIs among affected patients. Our findings illustrate that SSIs remain a relatively common, serious threat to patient safety in China, requiring more action. To tackle SSIs, we propose to establish a nationwide network for SSI surveillance using unified criteria with the aid of informatic techniques and to tailor and implement countermeasures based on local data and observation. We highlight that the impact of SSIs in China warrants further study.

The association between surgical site infection and postoperative colorectal cancer recurrence and the effect of laparoscopic surgery on prognosis

PURPOSE

Studies have shown that surgical site infection (SSI) incidence is lower in patients undergoing laparoscopic surgery. Therefore, we reported the SSI countermeasures adopted by our institution and aimed to evaluate the association between SSI occurrence and postoperative colorectal cancer recurrence and the usefulness of laparoscopic surgery for prognosis.

METHODS

Among the patients with colorectal cancer who underwent radical surgery at our hospital between January 2015 and December 2017, 197 with stage I-III cancer without distant metastases were included. We retrospectively analyzed patients' electronic medical records and classified them into the non-SSI (without SSI, n = 159) and SSI (with SSI, n = 38) groups. We calculated and compared the 5-year relapse-free survival (RFS) and overall survival (OS) rates. Additionally, we assessed the relationship between prognosis in the non-SSI, incisional SSI, and organ/space SSI groups and the usefulness of laparoscopic surgery.

RESULTS

The 5-year RFS and OS were 80.5% versus 63.2% (P = 0.024; hazard ratio [HR], 2.065; 95% confidence interval [CI], 1.099-3.883) and 88.7% versus 84.2% (P = 0.443; HR, 1.436; 95% CI, 0.570-3.617), respectively. The SSI group had a significantly worse 5-year RFS prognosis. Regarding the relationship with laparoscopic surgery, the SSI incidence was 45.0% (9/20 cases) and 16.4% (29/177 cases) with laparotomy and laparoscopic surgery, respectively, indicating a significantly reduced SSI occurrence with laparoscopic surgery (P = 0.005).

CONCLUSION

Patients with SSI were at high risk for colorectal cancer recurrence, and laparoscopic surgery may be useful for reducing SSI.

Improved Prediction of Surgical-Site Infection After Colorectal Surgery Using Machine Learning

BACKGROUND

Surgical-site infection is a source of significant morbidity after colorectal surgery. Previous efforts to develop models that predict surgical-site infection have had limited accuracy. Machine learning has shown promise in predicting postoperative outcomes by identifying nonlinear patterns within large data sets.

OBJECTIVE

This study aimed to seek usage of machine learning to develop a more accurate predictive model for colorectal surgical-site infections.

DESIGN

Patients who underwent colorectal surgery were identified in the American College of Surgeons National Quality Improvement Program database from years 2012 to 2019 and were split into training, validation, and test sets. Machine-learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using area under the receiver operating characteristic curve.

SETTINGS

A national, multicenter data set.

PATIENTS

Patients who underwent colorectal surgery.

MAIN OUTCOME MEASURES

The primary outcome (surgical-site infection) included patients who experienced superficial, deep, or organ-space surgical-site infections.

RESULTS

The data set included 275,152 patients after the application of exclusion criteria. Of all patients, 10.7% experienced a surgical-site infection. Artificial neural network showed the best performance with area under the receiver operating characteristic curve of 0.769 (95% CI, 0.762-0.777), compared with 0.766 (95% CI, 0.759-0.774) for gradient boosting, 0.764 (95% CI, 0.756-0.772) for random forest, and 0.677 (95% CI, 0.669-0.685) for logistic regression. For the artificial neural network model, the strongest predictors of surgical-site infection were organ-space surgical-site infection present at time of surgery, operative time, oral antibiotic bowel preparation, and surgical approach.

LIMITATIONS

Local institutional validation was not performed.

CONCLUSIONS

Machine-learning techniques predict colorectal surgical-site infections with higher accuracy than logistic regression. These techniques may be used to identify patients at increased risk and to target preventive interventions for surgical-site infection. See Video Abstract at http://links.lww.com/DCR/C88 .

PREDICCIN MEJORADA DE LA INFECCIN DEL SITIO QUIRRGICO DESPUS DE LA CIRUGA COLORRECTAL MEDIANTE EL APRENDIZAJE AUTOMTICO

ANTECEDENTES:La infección del sitio quirúrgico es una fuente de morbilidad significativa después de la cirugía colorrectal. Los esfuerzos anteriores para desarrollar modelos que predijeran la infección del sitio quirúrgico han tenido una precisión limitada. El aprendizaje automático se ha mostrado prometedor en la predicción de los resultados posoperatorios mediante la identificación de patrones no lineales dentro de grandes conjuntos de datos.OBJETIVO:Intentamos utilizar el aprendizaje automático para desarrollar un modelo predictivo más preciso para las infecciones del sitio quirúrgico colorrectal.DISEÑO:Los pacientes que se sometieron a cirugía colorrectal se identificaron en la base de datos del Programa Nacional de Mejoramiento de la Calidad del Colegio Estadounidense de Cirujanos de los años 2012 a 2019 y se dividieron en conjuntos de capacitación, validación y prueba. Las técnicas de aprendizaje automático incluyeron conjunto aleatorio, aumento de gradiente y red neuronal artificial. También se creó un modelo de regresión logística. El rendimiento del modelo se evaluó utilizando el área bajo la curva característica operativa del receptor.CONFIGURACIÓN:Un conjunto de datos multicéntrico nacional.PACIENTES:Pacientes intervenidos de cirugía colorrectal.PRINCIPALES MEDIDAS DE RESULTADO:El resultado primario (infección del sitio quirúrgico) incluyó pacientes que experimentaron infecciones superficiales, profundas o del espacio de órganos del sitio quirúrgico.RESULTADOS:El conjunto de datos incluyó 275.152 pacientes después de la aplicación de los criterios de exclusión. El 10,7% de los pacientes presentó infección del sitio quirúrgico. La red neuronal artificial mostró el mejor rendimiento con el área bajo la curva característica operativa del receptor de 0,769 (IC del 95 %: 0,762 - 0,777), en comparación con 0,766 (IC del 95 %: 0,759 - 0,774) para el aumento de gradiente, 0,764 (IC del 95 %: 0,756 - 0,772) para conjunto aleatorio y 0,677 (IC 95% 0,669 - 0,685) para regresión logística. Para el modelo de red neuronal artificial, los predictores más fuertes de infección del sitio quirúrgico fueron la infección del sitio quirúrgico del espacio del órgano presente en el momento de la cirugía, el tiempo operatorio, la preparación intestinal con antibióticos orales y el abordaje quirúrgico.LIMITACIONES:No se realizó validación institucional local.CONCLUSIONES:Las técnicas de aprendizaje automático predicen infecciones del sitio quirúrgico colorrectal con mayor precisión que la regresión logística. Estas técnicas se pueden usar para identificar a los pacientes con mayor riesgo y para orientar las intervenciones preventivas para la infección del sitio quirúrgico. Consulte Video Resumen en http://links.lww.com/DCR/C88 . (Traducción-Dr Yolanda Colorado ).

Dressing to prevent surgical site infection in adult patients with cancer: a systematic review with meta-analysis

PURPOSE

To identify the most effective dressing for application to surgical wounds with primary closure to prevent surgical site infection (SSI) in adult patients with cancer undergoing elective surgeries.

METHODS

This systematic review was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis, with online searches conducted in the CINHAL, Cochrane Central, LILACS, PubMed, Scopus, Embase, Livivo, and Web of Science databases. An additional search was conducted in gray literature using Google Scholar. The risk of bias was assessed using RoB 2.0. The certainty of evidence was evaluated using the Grading of Recommendations Assessment and Development and Evaluation, and the results were synthesized in a descriptive manner and using meta-analysis.

RESULTS

Eleven randomized clinical trials were conducted to compare different types of dressing-silver dressing with absorbent dressing (n = 3), mupirocin dressing with paraffin/no dressing (n = 1), honey-based dressing with absorbent dressing (n = 1), vitamin E and silicone-containing dressing with absorbent dressing (n = 1), and negative pressure wound therapy with absorbent dressing (n = 4)-and compare the usage duration of absorbent dressing (n = 1). Nine trials presented a low risk of bias, and two were classified as having uncertain bias. Compared with absorbent dressing, silver dressing did not reduce the risk of developing any type of SSI in 894 clinical trial participants (risk relative RR: 0.72; 95% confidence interval [CI] [0.44, 1.17] p = 0.18). Compared with absorbent dressing, negative pressure wound therapy did not reduce the risk of developing any type of SSI in the 1041 participants of two clinical trials (RR 0.68; 95% CI [0.31, 1.26] p = 0.22). The certainty of evidence of the three meta-analyses was considered low or very low for the prevention of SSI. We believe that this low certainty of evidence can be improved by conducting new studies in the future.

CONCLUSION

There is no evidence regarding which dressing is the most effective in preventing SSI in adult patients with cancer.

Research on the factors that influence patients with colorectal cancer participating in the prevention and control of surgical site infection: Based on the extended theory of planned behaviour

Background

The most common and severe type of nosocomial infection in patients with colorectal cancer is surgical site infection (SSI). Patient‐related factors are an important components of SSI. So it is necessary to participate in SSI prevention and control. It is important to identify the factors that influence patients' participation behaviour and to explore the mechanism of these effects.

Methods

A total of 580 patients with colorectal cancer completed relevant measures. Based on the extended theory of planned behaviour, a structural equation model was used to analyse the relationship among the influencing factors.

Results

The factors influencing participation of patients with colorectal cancer in SSI prevention and control were participation intention, participation ability, self‐efficacy, participation attitude, perceived medical staff support, trust in physicians and social support. The direct effect coefficients of participation intention, participation ability and physician trust on SSI prevention and control behaviour were 0.67, 0.21 and 0.11, respectively. Self‐efficacy, participation attitude, perceived medical staff support and social support indirectly affect participation behaviour through participation intention, and their effect values are 0.21, 0.11, 0.11 and 0.08, respectively.

Conclusions

Based on the structural equation model developed in this study, targeted intervention measures should be implemented to mobilize the intention and enthusiasm of patients with colorectal cancer to participate in the prevention and control of SSI.

Patient or Public Contribution

Patients or public contribute to spreading research findings, and promote broad participation in the implementation of policies or strategies.