Key result
A novel machine learning-derived surgical Complexity Score accurately predicted perioperative morbidity, outperforming the CCI and ECI.
Why the study?
An optimal method to quantify surgical complexity using patient comorbidities derived from administrative billing data was lacking.
Does a novel machine learning-derived surgical Complexity Score improve the prediction of adverse postoperative outcomes compared to standard comorbidity indices in patients undergoing elective surgery?
Observational (n=1,049,160)
Yes
Does a novel machine learning-derived surgical Complexity Score improve the prediction of adverse postoperative outcomes compared to standard comorbidity indices in patients undergoing elective surgery?
A novel machine learning-derived surgical Complexity Score outperformed traditional comorbidity indices in predicting postoperative morbidity and readmissions among Medicare patients undergoing elective surgery.
Answers are grounded in this paper and cite their sources.
May aid surgical risk stratification; leaves open prospective validation before practice change.
BACKGROUND: An 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 DESIGN: A 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). RESULTS: Patients 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). CONCLUSIONS: Compared 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.
No takes yet. Share an insight, caveat, or question.
Hyer et al. (2019) conducted an observational in Elective surgery (n=1,049,160). Surgical Complexity Score vs. Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), and CMS-HCC was evaluated on Perioperative morbidity (AUC 0.868, 95% CI 0.866-0.869). A novel machine learning-derived surgical Complexity Score accurately predicted perioperative morbidity, outperforming the CCI and ECI.
Synapse has enriched 10 related papers. Consider them for comparative context: