Key result
The HAS model predicts early mortality after emergency laparotomy with an excellent 0.96 AUC.
Why the study?
Currently available predictive tools for mortality after emergency laparotomy show adequate to strong discriminative power, but none has demonstrated excellent discrimination.
Does the HAS model accurately predict 30-day postoperative mortality in adult patients undergoing emergency laparotomy?
Cohort (n=1,043)
Does the HAS model accurately predict 30-day postoperative mortality in adult patients undergoing emergency laparotomy?
Effect estimate: AUC 0.96
p-value: p=<0.0001
The novel HAS model, incorporating Hajibandeh Index, ASA status, and sarcopenia, provides excellent discrimination and calibration for predicting 30-day mortality after emergency laparotomy.
May enhance preoperative risk stratification in emergency laparotomy; leaves open external validation before clinical adoption.
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 .
No takes yet. Share an insight, caveat, or question.
Hajibandeh et al. (2023) conducted a cohort in emergency laparotomy due to non-traumatic acute abdominal pathology (n=1,043). HAS model (Hajibandeh Index, ASA status, sarcopenia) was evaluated on 30-day postoperative mortality (AUC 0.96, p=<0.0001). The HAS model, incorporating Hajibandeh Index, ASA status, and sarcopenia, demonstrated excellent discrimination in predicting 30-day mortality following emergency laparotomy (AUC 0.96, P<0.0001).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: