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Background  Palliative gastrointestinal (GI) surgery potentially relieves distressing symptoms arising from intestinal obstruction (IO) in patients with advanced peritoneal carcinomatosis (PC). As surgery is associated with significant morbidity risks in advanced cancer patients, it is important for surgeons to select patients who can benefit the most from this approach. Hence, we aim to determine predictors of morbidity and mortality after palliative surgery in patients with PC. In addition, we evaluate the utility of the UC Davis Cancer Care nomogram (UCDCCn) and develop a simplified model to predict short-term surgical mortality in these patients.    Methods  A retrospective review of patients with IO secondary to PC undergoing palliative GI surgery was performed. Logistic regression was used to determine independent predictors of 30-day morbidity and mortality after surgery. UCDCCn was evaluated using the area under the curve (AUC) for discriminatory power and the Hosmer-Lemeshow test for calibration. Our simplified model was developed using logistic regression and evaluated using cross-validation.    Results  A total of 254 palliative GI surgeries were performed over a 10-year duration. The 30-day morbidity and mortality were 43% (n = 110) and 21% (n = 53), respectively. Preoperative albumin, age, and emergency nature of surgery were significant independent predictors for 30-day morbidity. A simplified model using preoperative Eastern Cooperative Oncology Group (ECOG) status and albumin (AUC = 0.71) achieved better predictive power than UCDCCn (AUC = 0.66) for 30-day mortality.    Conclusion  Good ECOG status and high preoperative albumin levels were independently associated with good short-term outcomes after palliative GI surgery. Our simplified model may be used to conveniently and efficiently select patients who stand to benefit the most from surgery.

frontiers in oncology

Palliative Gastrointestinal Surgery in Patients With Advanced Peritoneal Carcinomatosis: Clinical Experience and Development of a Predictive Model for Surgical Outcomes