Prediction model using risk factors associated with anastomotic leakage after minimally invasive esophagectomy

Prediction and stratification for the surgical adverse events after minimally invasive esophagectomy: A two-center retrospective study

BACKGROUND

Minimally invasive esophagectomy (MIE) is a widely accepted treatment for esophageal cancer, yet it is associated with a significant risk of surgical adverse events (SAEs), which can compromise patient recovery and long-term survival. Accurate preoperative identification of high-risk patients is critical for improving outcomes.

AIM

To establish and validate a risk prediction and stratification model for the risk of SAEs in patients with MIE.

METHODS

This retrospective study included 747 patients who underwent MIE at two centers from January 2019 to February 2024. Patients were separated into a train set (n = 549) and a validation set (n = 198). After screening by least absolute shrinkage and selection operator regression, multivariate logistic regression analyzed clinical and intraoperative variables to identify independent risk factors for SAEs. A risk stratification model was constructed and validated to predict the probability of SAEs.

RESULTS

SAEs occurred in 10.2% of patients in train set and 13.6% in the validation set. Patients with SAE had significantly higher complication rate and a longer hospital stay after surgery. The key independent risk factors identified included chronic obstructive pulmonary disease, a history of alcohol consumption, low forced expiratory volume in the first second, and low albumin levels. The stratification model has excellent prediction accuracy, with an area under the curve of 0.889 for the training set and an area under the curve of 0.793 for the validation set.

CONCLUSION

The developed risk stratification model effectively predicts the risk of SAEs in patients undergoing MIE, facilitating targeted preoperative interventions and improving perioperative management.

Essential updates 2022/2023: Recent advances in perioperative management of esophagectomy to improve operative outcomes

In the era of minimally invasive surgery, esophagectomy remains a highly invasive procedure with a high rate of postoperative complications. Preoperative risk assessment is essential for planning esophagectomy in patients with esophageal cancer, and it is crucial to implement evidence-based perioperative management to mitigate these risks. Perioperative support from multidisciplinary teams has recently been reported to improve the perioperative nutritional status and long-term survival of patients undergoing esophagectomy. Intraoperative management of anesthesia and fluid therapy also significantly affects short-term outcomes after esophagectomy. In this narrative review, we outline the recent updates in the perioperative management of esophagectomy, focusing on preoperative risk assessment, intraoperative management, and perioperative support by multidisciplinary teams to improve operative outcomes.

Clinical value of prognostic nutritional index combined with C-reactive protein and albumin in early prediction of anastomotic leakage after radical gastric cancer surgery

OBJECTIVE

To evaluate the predictive value of the Prognostic Nutritional Index (PNI) combined with C-reactive protein (CRP) and albumin (ALB) for anastomotic leakage following radical gastric cancer surgery.

METHODS

A retrospective case-control study was conducted with 275 gastric cancer patients at the Second People's Hospital of Lanzhou City from September 2019 to October 2022. Patients were categorized into an anastomotic leakage group (n=31) or a non-leakage group. Clinical, surgical, and pathological data were analyzed using logistic regression to develop two risk models: a combined clinical-laboratory index (RISK1) and a separate laboratory index (RISK2). Model effectiveness was compared using Receiver Operating Characteristic (ROC) curves.

RESULTS

Anastomotic leakage occurred in 11.27% of patients, predominantly in those with advanced TNM stages (P=0.006). Notably, higher operative times (P=0.049) and increased intraoperative bleeding (P=0.027) were associated with the leakage group. Significant differences in ALB, PNI, and CRP levels were observed between the groups. Both RISK1 and RISK2 identified ALB, CRP, PNI, operative time, and intraoperative bleeding as independent predictors of leakage, demonstrating high predictive accuracy (RISK1 AUC=0.937, RISK2 AUC=0.911), with no significant difference in performance between the models (P=0.245).

CONCLUSION

The combination of ALB, CRP, and PNI effectively predicts the risk of anastomotic leakage in patients undergoing gastric cancer surgery. These biomarkers can significantly enhance postoperative management and improve patient outcomes.

Complication Prediction after Esophagectomy with Machine Learning

Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who underwent esophagectomy between 2011 and 2021. The dataset contains multimodal temporal information, specifically, laboratory results, vital signs, thorax images, and preoperative patient characteristics. The best models scored mean test set AUROCs of 0.87 and 0.82 for leakage 1 and 2 days ahead, respectively. For pneumonia, this was 0.74 and 0.61 for 1 and 2 days ahead, respectively. We conclude that machine learning models can effectively predict anastomotic leakage and pneumonia after esophagectomy.