Key result
Risk stratification model predicts surgical adverse events after minimally invasive esophagectomy with ~0.79 validation AUC.
Why the study?
Minimally invasive esophagectomy carries a significant risk of surgical adverse events that compromise recovery and survival, making accurate preoperative identification of high-risk patients critical.
Does a risk stratification model accurately predict surgical adverse events in patients undergoing minimally invasive esophagectomy?
Observational (n=198)
Yes
Does a risk stratification model accurately predict surgical adverse events in patients undergoing minimally invasive esophagectomy?
Absolute Event Rate: 10.2% vs 13.6%
A newly developed risk stratification model incorporating COPD, alcohol history, FEV1, and albumin levels effectively predicts surgical adverse events after minimally invasive esophagectomy.
May support preoperative counseling before minimally invasive esophagectomy; leaves open need for prospective validation of clinical impact.
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: = 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.
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Zhong et al. (2024) conducted an observational in Esophageal cancer (n=198). Risk stratification model vs. Validation cohort was evaluated on Surgical adverse events (SAEs). A risk stratification model effectively predicted surgical adverse events after minimally invasive esophagectomy, achieving an area under the curve of 0.889 in the training set and 0.793 in validation.
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