Predicting morbidity of liver resection View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-05

AUTHORS

Sudharsan Madhavan, Vishal G. Shelat, Su-Lin Soong, Winston W. L. Woon, Terence Huey, Yiong H. Chan, Sameer P. Junnarkar

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. More... »

PAGES

359-369

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00423-018-1656-3

    DOI

    http://dx.doi.org/10.1007/s00423-018-1656-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1100859155

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29417211


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