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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 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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Acknowledgements

The authors would like to thank Ms. Wang Bei for contributing to the data collection, Dr. Ma Thin Mar-Win for her preliminary organization and analysis of the data, and Dr. Chan Siew-Pang for his aid in the decision tree analysis.

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Authors and Affiliations

  1. Ministry of Health Holdings, 1 Maritime Square, #11-25 HarbourFront Centre, Singapore, 099253, Republic of Singapore

    Sudharsan Madhavan

  2. Hepato-Pancreatico-Biliary Surgery, Department of General Surgery, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Republic of Singapore

    Vishal G. Shelat, Su-Lin Soong, Winston W. L. Woon, Terence Huey & Sameer P. Junnarkar

  3. Biostatistics Unit, National University Health System, 1E Kent Ridge Road, Singapore, 119228, Republic of Singapore

    Yiong H. Chan

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  1. Sudharsan Madhavan
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Contributions

Sameer P. Junnarkar, Sudharsan Madhavan, Vishal G. Shelat, and Winston W.L. Woon participated in study conception and design. Sudharsan Madhavan and Su-Lin Soong participated in acquisition of data. Yiong H. Chan and Sudharsan Madhavan participated in analysis and interpretation of data. Sudharsan Madhavan and Vishal G. Shelat participated in drafting of the manuscript. Terence Huey and Sameer P. Junnarkar participated in critical revision of the manuscript.

Corresponding author

Correspondence to Sameer P. Junnarkar.

Ethics declarations

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors declare that they have no conflict of interest.

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Permissions: All tables and figures are original works and have not been published elsewhere before.

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Madhavan, S., Shelat, V.G., Soong, SL. et al. Predicting morbidity of liver resection. Langenbecks Arch Surg 403, 359–369 (2018). https://doi.org/10.1007/s00423-018-1656-3

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