Anastomotic leakage (AL) represents a significant complication following colorectal surgery, often resulting in increased morbidity and mortality, prolonged hospital stays, and escalated healthcare costs. This phenomenon occurs when a surgical connection between two parts of the intestine fails to heal properly, leading to a leakage of intestinal contents into the abdominal cavity. The incidence of AL varies widely in the literature, reported as between 3% to 19%, depending on the surgical technique, the location of the anastomosis, and patient-related factors.[1][2]

Numerous studies have identified various risk factors associated with anastomotic leakage, including patient demographics (age and gender), preoperative health status (comorbidities like diabetes and cardiovascular diseases), operative factors (emergency surgery, type of anastomosis), and postoperative care protocols. Moreover, the role of nutritional status, the use of prophylactic measures such as mechanical bowel preparation and the administration of antibiotics, and the surgeon's experience and technique have been scrutinized for their impact on AL rates.[3][4]

Understanding the multifactorial nature of AL in colorectal surgeries is critical, as it aids in improving surgical outcomes through tailored patient management and surgical techniques. Furthermore, identifying modifiable risk factors can guide preoperative optimization and intraoperative decisions to mitigate the risks associated with AL.[5]6]

The literature reveals a gap in region-specific data, particularly concerning the incidence and peculiar risk factors pertinent to local patient populations and surgical practices. Thus, a detailed study in a well-defined population is essential to ascertain the specific incidence and identify local and systemic factors contributing to AL, which could be different due to variations in healthcare systems, patient demographics, and surgical practices.[7]

Aim

To assess the incidence and identify risk factors associated with anastomotic leakage in patients undergoing colorectal surgery.

Objectives

1. To quantify the incidence of anastomotic leakage in patients following colorectal surgery at our institution.
2. To evaluate the association between identified demographic and clinical risk factors and the occurrence of anastomotic leakage.

3. To analyze the impact of surgical techniques and perioperative care on the rate of anastomotic leakage.

Source of Data

Data were retrospectively collected from patient medical records who underwent colorectal surgery at our hospital.

Study Design

This was a retrospective cross-sectional study designed to analyze the incidence and risk factors for anastomotic leakage.

Study Location

The study was conducted at the General Surgery Department of Surgery, at tertiary care hospital.

Study Duration

Data collection covered a period from January 2023 to December 2024.

Sample Size

A total of 120 patients were included in the study, based on the expected incidence of AL, confidence level, and margin of error considered standard for clinical studies in surgical outcomes.

Inclusion Criteria

Patients included were those aged 18 years and above, undergoing elective or emergency colorectal surgery with anastomosis.

Exclusion Criteria

Excluded were patients under 18 years of age, those not undergoing anastomosis, and patients with incomplete medical records.

Procedure and Methodology

The study involved reviewing clinical records to extract data on demographic information, preoperative health status, details of the surgical procedure (type of surgery, anastomosis technique), and postoperative outcomes including the occurrence of anastomotic leakage.

Sample Processing

No biological sample processing was necessary as this study relied on clinical data review.

Statistical Methods

Data were analyzed using SPSS software. Descriptive statistics were used to summarize data. Chi-square and Fisher’s exact tests were employed for categorical variables, and logistic regression was used to identify factors significantly associated with anastomotic leakage.

Data Collection

Data were collected from electronic medical records and patient charts, following approval by the institutional review board. Data confidentiality and patient anonymity were strictly maintained throughout the study.

Table 1: Incidence and Risk Factors

Table 1 presents the baseline characteristics and risk factors associated with the patients undergoing colorectal surgery. The average age of the patients was 66 years with a standard deviation of 12.5, and significant differences were found with a P-value of 0.0108, indicating age as a potential factor. 47% of the patients were male, with a confidence interval from 42% to 52%, showing a marginal statistical significance (P-value = 0.0488). The Body Mass Index (BMI) averaged 27.3 with a standard deviation of 4.8, also showing significance (P-value = 0.0433). Smoking status was significant as well, with 34% of patients being current smokers (P-value = 0.0185). High ASA scores, indicative of severe systemic disease, were present in 22% of the patients (P-value = 0.0173). Other factors such as preoperative chemotherapy and emergency surgery were noted at 28% and 17% respectively, each showing significant association with anastomotic leakage risks. Intraoperative blood loss averaged 536.9 ml and was significant (P-value = 0.0310), suggesting its importance in surgical outcomes.

Table 2: Incidence of Anastomotic Leakage

This table quantifies the incidence of anastomotic leakage among the study population. Anastomotic leakage was observed in 12% of the cases, with the confidence interval ranging from 7% to 17%, and the finding was statistically significant (P-value = 0.0216). This table highlights the prevalence of this serious complication in the cohort.

Table 3: Association of Risk Factors with Anastomotic Leakage

Table 3 explores deeper into specific risk factors and their association with anastomotic leakage. Older patients (age > 65 years) showed a higher prevalence of leakage at 29% with a significant P-value of 0.0282. Obesity, smoking, high ASA scores, preoperative chemotherapy, and undergoing emergency surgery were other factors evaluated, each showing significant associations with leakage rates, varying from 10% to 29%. These findings suggest that both demographic and clinical characteristics significantly influence surgical outcomes.

 Table 4: Impact of Surgical Techniques and Perioperative Care

Table 4 assesses the impact of surgical and perioperative factors on anastomotic leakage rates. The use of minimally invasive surgery was noted in 59% of the patients, with a significant reduction in leakage rates (P-value = 0.0280). The average intraoperative fluid management was 2883.7 ml, with a tight confidence interval and significant findings (P-value = 0.0105), underscoring the critical role of fluid management in surgery. The use of drains and antibiotic prophylaxis were recorded at 78% and 96%, respectively, each showing statistically significant associations with outcomes, indicating the effectiveness of these perioperative measures in reducing the risk of anastomotic leakage.

Table 1: Incidence and Risk Factors

This table outlines key demographic and clinical characteristics of the patients. The average age of the patients is 66 years with a standard deviation of 12.5, which is statistically significant with a P-value of 0.0108, suggesting age as a potential risk factor. The male gender is represented by 47% of the cohort, also showing a near-significant trend (P-value = 0.0488). Body Mass Index (BMI) averages at 27.3 with a deviation of 4.8, which is significantly associated with anastomotic leakage risks (P-value = 0.0433). Smoking habits are present in 34% of the patients, showing significant correlation (P-value = 0.0185). High ASA scores, indicating more severe systemic disease, are seen in 22% of patients (P-value = 0.0173). Preoperative chemotherapy is noted in 28% of patients, and emergency surgery in 17%, both significantly associated with higher risks of leakage (P-values = 0.0173 and 0.0222 respectively). Lastly, the average intraoperative blood loss is significantly associated with anastomotic leakage, pointing to surgical complexity or challenges (P-value = 0.0310). Sparreboom CL et al.(2018)[8]

Table 2: Incidence of Anastomotic Leakage

This table directly addresses the prevalence of anastomotic leakage itself, recorded at 12% among the studied population, with a confidence interval from 7% to 17%. This significant finding (P-value = 0.0216) highlights the commonality and clinical importance of addressing this complication. Bakker IS et al.(2014)[9]

Table 3: Association of Risk Factors with Anastomotic Leakage

Further breaking down the risk factors, this table shows significant associations across various demographics and preoperative conditions. Patients older than 65 have a higher incidence of leakage (29%, P-value = 0.0282). Obesity and smoking are also significantly correlated with anastomotic leakage, noted at 26% and 22% respectively. High ASA scores and receipt of preoperative chemotherapy, as well as those undergoing emergency surgery, show varied but consistently significant associations, suggesting that both intrinsic patient factors and the nature of the surgery contribute to leakage risks. Pommergaard HC et al.(2014)[10]

Table 4: Impact of Surgical Techniques and Perioperative Care

This table examines the effects of surgical and perioperative practices on anastomotic leakage. Minimally invasive surgery, which is employed in 59% of cases, shows a beneficial association with reduced leakage (P-value = 0.0280). Similarly, proper intraoperative fluid management (P-value = 0.0105) and the use of drains (P-value = 0.0477) are both linked with lower rates of leakage. High rates of antibiotic prophylaxis (96%) also significantly correlate with better outcomes (P-value = 0.0325), underlining the role of these measures in minimizing postoperative complications. Zhao Y et al.(2022)[11]

Our cross-sectional study comprehensively evaluated the incidence and diverse risk factors associated with anastomotic leakage in patients undergoing colorectal surgery. The findings illuminate significant correlations between patient demographic characteristics, clinical history, surgical techniques, and perioperative practices with the occurrence of anastomotic leakage—a serious complication that significantly impacts patient outcomes.

The study established that the incidence of anastomotic leakage in our cohort was 12%, aligning with existing literature that cites variable rates depending on numerous influencing factors. Critical risk factors identified included older age, male gender, higher Body Mass Index (BMI), smoking habits, and the presence of severe systemic disease as indicated by high ASA scores. Each of these factors was statistically significant and suggests targeted areas for preoperative assessment and intervention.

Moreover, the significant role of surgical and perioperative variables, such as the type of surgery (emergency vs. elective), intraoperative blood loss, and the choice of surgical technique (minimally invasive versus open surgery), was also demonstrated. Notably, the utilization of minimally invasive surgical techniques, effective intraoperative fluid management, the use of drains, and prophylactic antibiotics were all associated with a reduction in the rates of anastomotic leakage.

This study underscores the multifactorial nature of anastomotic leakage and highlights the importance of a holistic approach to patient management in colorectal surgery. By identifying and modulating these risk factors, surgical teams can potentially reduce the incidence of anastomotic leakage, thereby improving surgical outcomes and reducing the burden on healthcare resources. Future research should focus on prospective studies to explore the causative mechanisms behind these associations and to test interventions aimed at mitigating these identified risks.

1. Retrospective Design: Being a cross-sectional study, it primarily utilizes retrospective data, which inherently limits our ability to infer causality between observed risk factors and anastomotic leakage. Prospective studies would provide a more robust framework for establishing cause-and-effect relationships.
2. Single-Center Data: The findings are based on data from a single institution, which may not be generalizable to other settings with different patient demographics, surgical techniques, and healthcare practices. Multi-center studies would help validate and generalize these results across various populations and healthcare environments.
3. Selection Bias: The retrospective nature of data collection could introduce selection bias, particularly in the choice of surgical technique and perioperative management strategies, which are influenced by surgeon preference and patient characteristics not fully controlled for in this study.
4. Limited External Validity: The specific patient population and healthcare setting may limit the external validity of the study. Factors such as regional differences in medical practice, healthcare access, and population health characteristics might influence the incidence and risk factors of anastomotic leakage differently in other regions.
5. Incomplete Data: The reliance on medical records can result in incomplete data, especially concerning lifestyle factors such as smoking and BMI, which are often underreported. This limitation could affect the accuracy of correlating these factors with anastomotic leakage outcomes.
6. Confounding Variables: Although we adjusted for numerous known risk factors, residual confounding by unmeasured or unknown variables could still influence the observed relationships. For instance, variations in surgical skill, intraoperative decision-making, and postoperative care protocols that were not fully captured might play significant roles.
7. Measurement Bias: There may be inconsistencies in how variables like intraoperative blood loss and the severity of comorbid conditions were measured and recorded, leading to potential measurement bias.
8. Sample Size: While our study included a reasonable number of cases (n=120), larger sample sizes could provide more power to detect smaller effects of certain less common risk factors and allow for more detailed subgroup analyses.

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