ArticleWorld journal of gastroenterology2025
Prediction and stratification for the surgical adverse events after minimally invasive esophagectomy: A two-center retrospective study.
Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Nomographic predictive models for complications after minimally invasive esophagectomy: Current status and future perspectives.World journal of gastrointestinal surgery · 2025Review
- Towards personalized care in minimally invasive esophageal surgery: An adverse events prediction model.World journal of gastroenterology · 2025Article
- Risk and management of adverse events in minimally invasive esophagectomy.World journal of gastrointestinal surgery · 2025Article
- Perioperative nursing care strategies in esophageal cancer surgery: a mini-review.Frontiers in oncology · 2025Review
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10 authors.
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Abstract
backgroundMinimally 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.
aimTo establish and validate a risk prediction and stratification model for the risk of SAEs in patients with MIE.
methodsThis retrospective study included 747 patients who underwent MIE at two centers from January 2019 to February 2024. Patients were separated into a train set (
resultsSAEs 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.
conclusionThe developed risk stratification model effectively predicts the risk of SAEs in patients undergoing MIE, facilitating targeted preoperative interventions and improving perioperative management.
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