C-reactive protein (CRP) trajectory as a predictor of anastomotic leakage after rectal cancer resection: A multicentre cohort study

A real-world study was conducted to develop a nomogram that predicts the occurrence of anastomotic leakage in patients with esophageal cancer following esophagectomy

BACKGROUND

The incidence of anastomotic leakage (AL) following esophagectomy is regarded as a noteworthy complication. There is a need for biomarkers to facilitate early diagnosis of AL in high-risk esophageal cancer (EC) patients, thereby minimizing its morbidity and mortality. We assessed the predictive abilities of inflammatory biomarkers for AL in patients after esophagectomy.

METHODS

In order to ascertain the predictive efficacy of biomarkers for AL, Receiver Operating Characteristic (ROC) curves were generated. Furthermore, univariate, LASSO, and multivariate logistic regression analyses were conducted to discern the risk factors associated with AL. Based on these identified risk factors, a diagnostic nomogram model was formulated and subsequently assessed for its predictive performance.

RESULTS

Among the 438 patients diagnosed with EC, a total of 25 patients encountered AL. Notably, elevated levels of interleukin-6 (IL-6), IL-10, C-reactive protein (CRP), and procalcitonin (PCT) were observed in the AL group as compared to the non-AL group, demonstrating statistical significance. Particularly, IL-6 exhibited the highest predictive capacity for early postoperative AL, exhibiting a sensitivity of 92.00% and specificity of 61.02% at a cut-off value of 132.13 pg/ml. Univariate, LASSO, and multivariate logistic regression analyses revealed that fasting blood glucose ≥7.0mmol/L and heightened levels of IL-10, IL-6, CRP, and PCT were associated with an augmented risk of AL. Consequently, a nomogram model was formulated based on the results of multivariate logistic analyses. The diagnostic nomogram model displayed a robust discriminatory ability in predicting AL, as indicated by a C-Index value of 0.940. Moreover, the decision curve analysis provided further evidence supporting the clinical utility of this diagnostic nomogram model.

CONCLUSIONS

This predictive instrument can serve as a valuable resource for clinicians, empowering them to make informed clinical judgments aimed at averting the onset of AL.

Is early CT in cases of elevated postoperative CRP the best option for the diagnosis of colorectal anastomotic leakage?

PURPOSE

After colorectal surgery (CRS), the early detection and treatment of anastomotic leakage (AL) is critical. We aimed to evaluate the efficacy of early contrast-enhanced computed tomography (CT) (postoperative day [POD] 2-3) after elective colorectal surgery for the diagnosis of AL for patients with elevated CRP levels at POD 2-3.

METHOD

From 2017 to 2022, all patients who underwent elective CRS with an anastomosis and CRP > 150 mg/ml on POD 2-3 underwent enhanced CT during the 24 h following the CRP evaluation and were included in this retrospective, single-center study. The primary endpoint was the diagnostic value of the early CT scan for the detection of AL. The secondary endpoints were the diagnostic value of the early CT scan for the detection of grade C AL according to the type of resection and anastomosis and the quality of the opacification.

RESULTS

A total of 661 patients underwent elective CRS with anastomosis with an overall AL rate of 7.4%. Among the 661 patients, 141 were finally included in the study. The accuracy of early CT for the diagnosis of AL was 83.7%. For grade C AL, the accuracy was 81.6%. Among patients who had an ileocolic anastomosis, the accuracy was 88.2%, among those who had colorectal or ileorectal anastomosis, the accuracy was 83.0%, and among those who had a coloanal, the accuracy was 66.7%. In cases of good opacification by CT, the accuracy was 84.0%.

CONCLUSION

Early CT does not show perfect accuracy for an early diagnosis of AL.

Machine Learning-based Correlation Study between Perioperative Immunonutritional Index and Postoperative Anastomotic Leakage in Patients with Gastric Cancer

Backgrounds: The immunonutritional index showed great potential for predicting postoperative complications in various malignant diseases, while risk assessment based on machine learning (ML) methods is becoming popular in clinical practice. Early detection and prevention for postoperative anastomotic leakage (AL) play an important role in prognosis improvement among patients with gastric cancer (GC). Methods: This retrospective study included 297 patients with gastric cancer receiving gastrectomy between 2018 and 2021 in general surgery department of Xinhua Hospital. Perioperative clinical variables were collected to evaluate the predictive value for postoperative AL with 5 ML models. Then, AUROC was applied to identify the optimal perioperative clinical index and ML model for predicting postoperative AL. Results: The incidence of postoperative AL was 6.1% (n=18). After the training of 5 ML classification models, we found that immunonutritional index had significantly better classification ability than inflammatory or nutritional index alone separately (AUROC=0.87 vs. 0.83, P=0.01; AUROC=0.87 vs. 0.68, P<0.01). Next, we found that support vector machine (SVM), one of the ML methods, with selected immunonutritional index showed significantly greater classification ability than optimal univariant parameter [CRP on postoperative day 4 (AUROC=0.89 vs.0.86, P=0.02)]. Also, statistical analysis revealed multiple variables with significant relevance to postoperative AL, including serum CRP and albumin on postoperative day 4, NLR and SII etc. Conclusion: This study showed that perioperative immunonutritional index could act as an indicator for postoperative AL. Also, ML methods could significantly enhance the classification ability, and therefore, could be applied as a powerful tool for postoperative risk assessment for patients with GC.