Can We Improve Prediction of Adverse Surgical Outcomes? Development of a Surgical Complexity Score Using a Novel Machine Learning Technique
Can We Improve Prediction of Adverse Surgical Outcomes? Development of a Surgical Complexity Score Using a Novel Machine Learning Technique
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DOI：
10.1016/j.jamcollsurg.2019.09.015
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发表时间：
2020-01-01
影响因子：
5.2
通讯作者：
Ejaz, Aslam
中科院分区：
文献类型：
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作者：
Hyer, J. Madison;White, Susan;Ejaz, Aslam
背景：目前缺乏一种最佳方法来量化手术复杂性，该方法使用来自管理账单数据的患者合并症。我们试图开发一种新的，易于使用的手术复杂性评分，以准确地预测不良后果的患者进行elective surgery.Study DESIGN：一种新的手术复杂性评分是使用100%医疗保险住院和门诊标准分析文件（SAF）从2012年至2016年（n = 1,049,160）。将合并症输入机器学习算法，以分配权重，从而最大化与多个术后结局（包括发病率、再入院、死亡率和术后过度使用）的相关性。预测能力与3个最常用的风险调整指数进行比较：Charlson Comorbid指数（CCI），Elixhauser Comorbid指数（ECI）和医疗保险和医疗补助服务中心的分层条件类别（CMS-HCC）。患者接受结肠切除术（12.6%），腹主动脉瘤修复冠状动脉旁路移植术（13.0%），全髋关节置换术（22.0%），全膝关节置换术（43.0%），或肺切除术（5.0%）。复杂性评分对所有不良结局具有良好至非常好的预测能力。复杂性评分预测围手术期发病率的准确性最高（曲线下面积[AUC]：0.868，95% CI 0.866 - 0.869）;这优于CCI（AUC：0.717，95%CI 0.715至0.719），ECI（AUC：0.799，95%CI 0.797至0.800），与CMS-HCC相似（AUC：0.862，95%CI 0.861至0.863）。同样，复杂性评分在预测90天再入院方面优于其他3个合并症指数（AUC：0.707，95% CI 0.705 - 0.709），30天再入院（AUC：0.717，95% CI 0.715 - 0.720）和术后过度使用（AUC：0.817，95% CI 0.814至0.820）。与最常用的合并症和手术风险评分相比，新的手术复杂性评分优于CCI，ECI，和CMS-HCC在预测术后发病率、30天再入院率、90天再入院率和术后过度使用方面的作用。(C)2019年美国外科医生学会出版社：Elsevier Inc. All rights reserved.
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 superuse. (C) 2019 by the American College of Surgeons. Published by Elsevier Inc. All rights reserved.