O133: Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality after Emergency Laparotomy

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Introduction Current predictive models for mortality following an emergency laparotomy (EL) have a discrimination power of adequate to strong with none showing excellent discrimination. We aim to develop and validate a predictive model to predict the risk of postoperative mortality after EL considering the following variables: age, age ≥ 80, American Society of Anaesthesiologists (ASA) status, clinical frailty score, sarcopenia, Hajibandeh Index (HI), bowel resection, and intraperitoneal contamination. Methods A retrospective cohort study of adult patients who underwent EL due to non-traumatic acute abdominal pathology between 2017 and 2022 was conducted. Multivariable binary logistic regression analysis was used to develop and validate the model via two protocols (Protocol A and B). The model performance was evaluated in terms of ROC curve discrimination, calibration (calibration diagram and Hosmer-Lemeshow test), and classification. Results 1043 patients were included (statistical power = 94%). Multivariable analysis kept HI (Protocol-A: P=0.0004; Protocol-B: P=0.0017), ASA status (Protocol-A: P=0.0068; Protocol-B: P=0.0007), and sarcopenia (Protocol-A: P<0.0001; Protocol-B: P<0.0001) as final predictors of 30-day mortality in both protocols; hence the model was called HAS (HI, ASA status, sarcopenia). The HAS demonstrated excellent discrimination (AUC: 0.96, P<0.0001), excellent calibration (P<0.0001), and excellent classification (95%) via both protocols. The performance of HAS was significantly better than NELA score (AUC: 0.96 vs 0.86, P<0.0001). Conclusions The HAS is the first model demonstrating excellent discrimination, calibration, and classification in predicting the risk of 30-day mortality following EL. The HAS model seems promising and is worth attention for external validation using HAS Emergency Laparotomy calculator.