Journal
SURGERY
Volume 170, Issue 1, Pages 298-303Publisher
MOSBY-ELSEVIER
DOI: 10.1016/j.surg.2021.01.030
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Funding
- National Institute of General Medical Sciences [R01 GM110240]
- Sepsis and Critical Illness Research Center Award from the National Institute of General Medical Sciences [P50 GM111152]
- National Center for Advancing Translational Sciences of the National Institutes of Health [UL1TR001427]
- Gatorade Trust, University of Florida [127900]
- National Institute of General Medical Sciences of the National Institutes of Health [K23 GM140268]
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By incorporating intraoperative data, machine learning models showed greater accuracy, discrimination, and precision in predicting acute kidney injury after major vascular surgery, compared to models using only preoperative data or the American Society of Anesthesiologists physical status classification. Machine learning approaches have the potential for real-time identification of high-risk patients who may benefit from personalized risk-reduction strategies.
Background: Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery. Methods: A single-center retrospective cohort of 1,531 adult patients who underwent nonemergency major vascular surgery, including open aortic, endovascular aortic, and lower extremity bypass pro-cedures, was evaluated. The validated, automated MySurgeryRisk analytics platform used electronic health record data to forecast patient-level probabilistic risk scores for postoperative acute kidney injury using random forest models with preoperative data alone and perioperative data (preoperative plus intraoperative). The MySurgeryRisk predictions were compared with each other as well as with the American Society of Anesthesiologists physical status classification. Results: Machine learning models using perioperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification (accuracy: 0.70 vs 0.64 vs 0.62, area under the receiver operating charac-teristics curve: 0.77 vs 0.68 vs 0.61, area under the precision-recall curve: 0.70 vs 0.58 vs 0.48). Conclusion: In predicting acute kidney injury after major vascular surgery, machine learning approaches that incorporate dynamic intraoperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification. Machine learning methods have the potential for real-time identification of high-risk patients who may benefit from personalized risk-reduction strategies. (c) 2021 Elsevier Inc. All rights reserved.
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