Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality After Emergency Laparotomy
Shahab Hajibandeh, Shahin Hajibandeh, Ioan Hughes, Kalyan Mitra, Alwin Puthiyakunnel Saji, Amy C. Clayton, Giorgio Alessandri, Trish Duncan, Julie Cornish, C.G. Morris, David O’Reilly, Nagappan Kumar
Abstract
OBJECTIVES: To develop and validate a predictive model to predict the risk of postoperative mortality after emergency laparotomy taking into account the following variables: age, age ≥ 80, ASA status, clinical frailty score, sarcopenia, Hajibandeh Index (HI), bowel resection, and intraperitoneal contamination. SUMMARY BACKGROUND DATA: The discriminative powers of the currently available predictive tools range between adequate and strong; none has demonstrated excellent discrimination yet. METHODS: The TRIPOD and STROCSS statement standards were followed to protocol and conduct a retrospective cohort study of adult patients who underwent emergency laparotomy due to non-traumatic acute abdominal pathology between 2017 and 2022. 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 discrimination (ROC curve analysis), calibration (calibration diagram and Hosmer-Lemeshow test), and classification (classification table). RESULTS: One thousand forty-three 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 postoperative 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. CONCLUSIONS: The HAS is the first model demonstrating excellent discrimination, calibration, and classification in predicting the risk of 30-day mortality following emergency laparotomy. The HAS model seems promising and is worth attention for external validation using the calculator provided. HAS mortality risk calculator https://app.airrange.io/#/element/xr3b_E6yLor9R2c8KXViSAeOSK .