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Conventional regression analysis and machine learning in prediction of anastomotic leakage and pulmonary complications after esophagogastric cancer surgery

Journal of Surgical Oncology, vol. 126, pp. 490–501

Abstract

BACKGROUND AND OBJECTIVES: With the current advanced data-driven approach to health care, machine learning is gaining more interest. The current study investigates the added value of machine learning to linear regression in predicting anastomotic leakage and pulmonary complications after upper gastrointestinal cancer surgery. METHODS: All patients in the Dutch Upper Gastrointestinal Cancer Audit undergoing curatively intended esophageal or gastric cancer surgeries from 2011 to 2017 were included. Anastomotic leakage was defined as any clinically or radiologically proven anastomotic leakage. Pulmonary complications entailed: pneumonia, pleural effusion, respiratory failure, pneumothorax, and/or acute respiratory distress syndrome. Different machine learning models were tested. Nomograms were constructed using Least Absolute Shrinkage and Selection Operator. RESULTS: Between 2011 and 2017, 4228 patients underwent surgical resection for esophageal cancer, of which 18% developed anastomotic leakage and 30% a pulmonary complication. Of the 2199 patients with surgical resection for gastric cancer, 7% developed anastomotic leakage and 15% a pulmonary complication. In all cases, linear regression had the highest predictive value with the area under the curves varying between 61.9 and 68.0, but the difference with machine learning models did not reach statistical significance. CONCLUSION: Machine learning models can predict postoperative complications in upper gastrointestinal cancer surgery, but they do not outperform the current gold standard, linear regression.

Authors 8

  1. Leiden University Medical Center

    Affiliation as printed

    Department of Surgery Leiden University Medical Center Leiden The Netherlands

    Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

  2. Leiden University Medical Center

    Affiliation as printed

    Department of Surgery Leiden University Medical Center Leiden The Netherlands

    Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

  3. Leiden University Medical Center

    Affiliation as printed

    Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands

    Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

  4. Leiden University Medical Center · The Netherlands Cancer Institute

    Affiliation as printed

    Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands

    Department of Surgery Netherlands Cancer Institute‐Antoni van Leeuwenhoek Amsterdam The Netherlands

    Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

    Department of Surgery, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, The Netherlands

  5. Leiden University Medical Center

    Affiliation as printed

    Department of Surgery Leiden University Medical Center Leiden The Netherlands

    Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

  6. Leiden University Medical Center

    Affiliation as printed

    Department of Surgery Leiden University Medical Center Leiden The Netherlands

    Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

  7. Leiden University Medical Center

    Affiliation as printed

    Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands

    Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

  8. Leiden University Medical Center

    Affiliation as printed

    Department of Surgery Leiden University Medical Center Leiden The Netherlands

    Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

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References 41