Fan Guoxin, Yang Sheng, Liu Huaqing, Xu Ningze, Chen Yuyong, He Jie, Su Xiuyun, Pang Mao, Liu Bin, Han Lanqing, Rong Limin
Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China.
Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China.
Spine (Phila Pa 1976). 2022 May 1;47(9):E390-E398. doi: 10.1097/BRS.0000000000004267. Epub 2021 Oct 22.
A retrospective cohort study.
The objective of the study was to develop machine-learning (ML) classifiers for predicting prolonged intensive care unit (ICU)-stay and prolonged hospital-stay for critical patients with spinal cord injury (SCI).
Critical patients with SCI in ICU need more attention. SCI patients with prolonged stay in ICU usually occupy vast medical resources and hinder the rehabilitation deployment.
A total of 1599 critical patients with SCI were included in the study and labeled with prolonged stay or normal stay. All data were extracted from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III-IV Database. The extracted data were randomly divided into training, validation and testing (6:2:2) subdatasets. A total of 91 initial ML classifiers were developed, and the top three initial classifiers with the best performance were further stacked into an ensemble classifier with logistic regressor. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicting outcome was prolonged ICU-stay, while the secondary predicting outcome was prolonged hospital-stay.
In predicting prolonged ICU-stay, the AUC of the ensemble classifier was 0.864 ± 0.021 in the three-time five-fold cross-validation and 0.802 in the independent testing. In predicting prolonged hospital-stay, the AUC of the ensemble classifier was 0.815 ± 0.037 in the three-time five-fold cross-validation and 0.799 in the independent testing. Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top three initial classifiers varied a lot in either predicting prolonged ICU-stay or discriminating prolonged hospital-stay.
The ensemble classifiers successfully predict the prolonged ICU-stay and the prolonged hospital-stay, which showed a high potential of assisting physicians in managing SCI patients in ICU and make full use of medical resources.Level of Evidence: 3.
一项回顾性队列研究。
本研究的目的是开发机器学习（ML）分类器，用于预测脊髓损伤（SCI）重症患者在重症监护病房（ICU）的延长住院时间和在医院的延长住院时间。
ICU中的SCI重症患者需要更多关注。在ICU中延长住院时间的SCI患者通常占用大量医疗资源，并阻碍康复部署。
本研究共纳入1599例SCI重症患者，并标记为延长住院或正常住院。所有数据均从电子ICU协作研究数据库和重症监护医学信息集市III-IV数据库中提取。提取的数据被随机分为训练、验证和测试（6:2:2）子数据集。共开发了91个初始ML分类器，性能最佳的前三个初始分类器进一步与逻辑回归器堆叠成一个集成分类器。曲线下面积（AUC）是评估所有分类器预测性能的主要指标。主要预测结果是ICU住院时间延长，次要预测结果是医院住院时间延长。
在预测ICU住院时间延长方面，集成分类器在三次五折交叉验证中的AUC为0.864±0.021，在独立测试中为0.802。在预测医院住院时间延长方面，集成分类器在三次五折交叉验证中的AUC为0.815±0.037，在独立测试中为0.799。决策曲线分析显示了集成分类器的优点，因为前三个初始分类器的曲线在预测ICU住院时间延长或区分医院住院时间延长方面差异很大。
集成分类器成功预测了ICU住院时间延长和医院住院时间延长，显示出在协助医生管理ICU中的SCI患者和充分利用医疗资源方面具有很高的潜力。证据水平：3。
