• J. Am. Coll. Surg. · Jan 2020

    Can We Improve Prediction of Adverse Surgical Outcomes? Development of a Surgical Complexity Score Using A Novel Machine Learning Technique.

    • J Madison Hyer, Susan White, Jordan Cloyd, Mary Dillhoff, Allan Tsung, Timothy M Pawlik, and Aslam Ejaz.
    • Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH.
    • J. Am. Coll. Surg. 2020 Jan 1; 230 (1): 43-52.e1.

    BackgroundAn optimal method to quantify surgical complexity using patient comorbidities derived from administrative billing data is lacking. We sought to develop a novel, easy-to-use surgical Complexity Score to accurately predict adverse outcomes among patients undergoing elective surgery.Study DesignA novel surgical Complexity Score was developed using 100% Medicare Inpatient and Outpatient Standard Analytic Files (SAFs) from years 2012 to 2016 (n = 1,049,160). Comorbid conditions were entered into a machine learning algorithm to assign weights to maximize the correlation with multiple postoperative outcomes including morbidity, readmission, mortality, and postoperative super-use. Predictive ability was compared against 3 of the most commonly used risk adjustment indices: the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), and the Centers for Medicare and Medicaid Service's Hierarchical Condition Category (CMS-HCC).ResultsPatients underwent colectomy (12.6%), abdominal aortic aneurysm repair (4.4%), coronary artery bypass grafting (13.0%), total hip replacement (22.0%), total knee replacement (43.0%), or lung resection (5.0%). The Complexity Score had a good to very good predictive ability for all adverse outcomes. The Complexity Score had the highest accuracy in predicting perioperative morbidity (area under the curve [AUC]: 0.868, 95% CI 0.866 to 0.869); this performed better than the CCI (AUC: 0.717, 95% CI 0.715 to 0.719), ECI (AUC: 0.799, 95% CI 0.797 to 0.800), and similar to the CMS-HCC (AUC: 0.862, 95% CI 0.861 to 0.863). Similarly, the Complexity Score outperformed each of the 3 other comorbidity indices in predicting 90-day readmission (AUC: 0.707, 95% CI 0.705 to 0.709), 30-day readmission (AUC: 0.717, 95% CI 0.715 to 0.720), and postoperative super-use (AUC: 0.817, 95% CI 0.814 to 0.820).ConclusionsCompared with the most commonly used comorbidity and surgical risk scores, the novel surgical Complexity Score outperformed the CCI, ECI, and CMS-HCC in predicting postoperative morbidity, 30-day readmission, 90-day readmission, and postoperative super-use.Copyright © 2019 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

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