Predicting morbidity of liver resection

Predicting morbidity of liver resection 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 demon- strated better fit of the ACS-NSQIP model than POSSUM in predicting morbidity. Conclusion In patients undergoing LR, the ACS-NSQIP surgical http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Langenbeck's Archives of Surgery Springer Journals

Predicting morbidity of liver resection

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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 demon- strated better fit of the ACS-NSQIP model than POSSUM in predicting morbidity. Conclusion In patients undergoing LR, the ACS-NSQIP surgical

Journal

Langenbeck's Archives of Surgery – Springer Journals

Published: Feb 7, 2018

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