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Performance of risk prediction models for post-operative mortality in patients undergoing liver resection

  • Author Footnotes
    1 Co-first authors.
    Nadim Mahmud
    Correspondence
    Corresponding author. 3400 Civic Center Boulevard 4th Floor, South Pavilion, Philadelphia, PA, 19104, USA.
    Footnotes
    1 Co-first authors.
    Affiliations
    Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA

    Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Leonard David Institute of Health Economics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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  • Author Footnotes
    1 Co-first authors.
    Sarjukumar Panchal
    Footnotes
    1 Co-first authors.
    Affiliations
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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  • Florence E. Turrentine
    Affiliations
    Department of Surgery, Surgical Outcomes Research Center, University of Virginia, Charlottesville, VA, USA
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  • Author Footnotes
    2 Co-senior authors.
    David E. Kaplan
    Footnotes
    2 Co-senior authors.
    Affiliations
    Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
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  • Author Footnotes
    2 Co-senior authors.
    Victor M. Zaydfudim
    Footnotes
    2 Co-senior authors.
    Affiliations
    Department of Surgery, Surgical Outcomes Research Center, University of Virginia, Charlottesville, VA, USA
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  • Author Footnotes
    1 Co-first authors.
    2 Co-senior authors.

      Highlights

      • Preoperative risk stratification for liver resection remains a clinical challenge.
      • In a dataset of patients undergoing liver resection, prediction scores were compared for 30-day postoperative mortality.
      • The VOCAL-Penn score had superior discrimination and adequate calibration versus MELD, MELD-Na, ALBI, and Mayo risk scores.

      Abstract

      Background

      Liver resection is commonly performed for hepatic tumors, however preoperative risk stratification remains challenging. We evaluated the performance of contemporary prediction models for short-term mortality after liver resection in patients with and without cirrhosis.

      Methods

      This retrospective cohort study examined National Surgical Quality Improvement Program data. We included patients who underwent liver resections from 2014 to 2019. VOCAL-Penn, MELD, MELD-Na, ALBI, and Mayo risk scores were evaluated in terms of model discrimination and calibration for 30-day post-operative mortality.

      Results

      A total 15,198 patients underwent liver resection, of whom 249 (1.6%) experienced 30-day post-operative mortality. The VOCAL-Penn score had the highest discrimination (area under the ROC curve [AUC] 0.74) compared to all other models. The VOCAL-Penn score similarly outperformed other models in patients with (AUC 0.70) and without (AUC 0.74) cirrhosis.

      Conclusion

      The VOCAL-Penn score demonstrated superior predictive performance for 30-day post-operative mortality after liver resection as compared to existing clinical standards.

      Keywords

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