Sudharsan Madhavan1, Vishal G Shelat2, Su-Lin Soong2, Winston W L Woon2, Terence Huey2, Yiong H Chan3, Sameer P Junnarkar4. 1. Ministry of Health Holdings, 1 Maritime Square, #11-25 HarbourFront Centre, Singapore, 099253, Republic of Singapore. 2. Hepato-Pancreatico-Biliary Surgery, Department of General Surgery, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Republic of Singapore. 3. Biostatistics Unit, National University Health System, 1E Kent Ridge Road, Singapore, 119228, Republic of Singapore. 4. Hepato-Pancreatico-Biliary Surgery, Department of General Surgery, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Republic of Singapore. sp_junnarkar@ttsh.com.sg.
Abstract
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.
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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