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Updated: Feb 14, 2026

The Influence of Liver Resection on Intrahepatic Tumor Growth
Published on: April 9, 2016
Predicting morbidity of liver resection
Sudharsan Madhavan1, Vishal G Shelat2, Su-Lin Soong2
1Ministry of Health Holdings, 1 Maritime Square, #11-25 HarbourFront Centre, Singapore, 099253, Republic of Singapore.
Purpose:
Multiple models have attempted to predict morbidity of liver resection (LR). This study aims to determine the efficacy of American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator and the Physiological and Operative Severity Score in the enUmeration of Mortality and Morbidity (POSSUM) in predicting post-operative morbidity in patients who underwent LR.
Methods:
A retrospective analysis was conducted on patients who underwent elective LR. Morbidity risk was calculated with the ACS-NSQIP surgical risk calculator and POSSUM equation. Two models were then constructed for both ACS-NSQIP and POSSUM-(1) the original risk probabilities from each scoring system and (2) a model derived from logistic regression of variables. Discrimination, calibration, and overall performance for ACS-NSQIP and POSSUM were compared. Sub-group analysis was performed for both primary and secondary liver malignancies.
Results:
Two hundred forty-five patients underwent LR. Two hundred twenty-three (91%) had malignant liver pathologies. The post-operative morbidity, 90-day mortality, and 30-day mortality rate were 38.3%, 3.7%, and 2.4% respectively. ACS-NSQIP showed superior discriminative ability, calibration, and performance to POSSUM (p = 0.03). Hosmer-Lemeshow plot demonstrated better fit of the ACS-NSQIP model than POSSUM in predicting morbidity.
Conclusion:
In patients undergoing LR, the ACS-NSQIP surgical risk calculator was superior to POSSUM in predicting morbidity risk.
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