Palliative Gastrointestinal Surgery in Patients With Advanced Peritoneal Carcinomatosis: Clinical Experience and Development of a Predictive Model for Surgical Outcomes - Wong, Jolene Si Min; Lek, Sze Min; Lim, Daniel Yan Zheng; Chia, Claramae Shulyn; Tan, Grace Hwei Ching; Ong, Chin-Ann Johnny; Teo, Melissa Ching Ching (Frontiers in Oncology, 2022)
Background Palliative gastro-intestinal (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 for patients who can benefit 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 UC Davis Cancer Centre 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 Area Under Curve (AUC) for discriminatory power and Hosmer-Lemeshow test for calibration. Our simplified model was developed using logistic regression and evaluated using cross validation. Results 254 palliative GI surgeries were performed over a 10-year duration. 30-day morbidity and mortality were 43% (n=110) and 21% (n=53), respectively. Pre-operative albumin, age, and emergency nature of surgery were significant independent predictors for 30-day morbidity. A simplified model using pre-operative 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 outcome after palliative GI surgery. Our simplified model may be use to better select for patients that stand to benefit most from surgery.