高性能儿科手术风险计算器：基于机器学习和儿科 NSQIP 数据的新型算法。
High-performance pediatric surgical risk calculator: A novel algorithm based on machine learning and pediatric NSQIP data.
机构信息
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA.
Advanced Analytics Group of Pediatric Urology, Department of Urology, Boston Children's Hospital, Boston, MA, USA.
出版信息
Am J Surg. 2023 Jul;226(1):115-121. doi: 10.1016/j.amjsurg.2023.03.009. Epub 2023 Mar 13.
BACKGROUNDS
New methods such as machine learning could provide accurate predictions with little statistical assumptions. We seek to develop prediction model of pediatric surgical complications based on pediatric National Surgical Quality Improvement Program(NSQIP).
METHODS
All 2012-2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined as 30-day post-operative morbidity/mortality. Morbidity was further classified as any, major and minor. Models were developed using 2012-2017 data. 2018 data was used as independent performance evaluation.
RESULTS
431,148 patients were included in the 2012-2017 training and 108,604 were included in the 2018 testing set. Our prediction models had high performance in mortality prediction at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications).
CONCLUSIONS
We developed a high-performing pediatric surgical risk prediction model. This powerful tool could potentially be used to improve the surgical care quality.
背景
新方法，如机器学习，可以在很少的统计假设下提供准确的预测。我们试图基于儿科国家手术质量改进计划（NSQIP）开发儿科手术并发症的预测模型。
方法
回顾了 2012 年至 2018 年所有儿科 NSQIP 手术。主要结局定义为术后 30 天的发病率/死亡率。发病率进一步分为任何、主要和次要。使用 2012-2017 年的数据开发模型。2018 年的数据用于独立性能评估。
结果
在 2012-2017 年的培训中纳入了 431148 例患者，在 2018 年的测试中纳入了 108604 例患者。我们的预测模型在死亡率预测方面表现出色，测试集的 AUC 为 0.94。我们的模型在所有类别中的发病率预测都优于 ACS-NSQIP 计算器（主要并发症的 AUC 为 0.90，任何并发症的 AUC 为 0.86，轻微并发症的 AUC 为 0.69）。
结论
我们开发了一种性能较高的儿科手术风险预测模型。这个强大的工具可以用来提高手术护理质量。
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