Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury

Fan, Guoxin1,2; Yang, Sheng3; Liu, Huaqing4; Xu, Ningze5; Chen, Yuyong1,2; He, Jie2; Su, Xiuyun2; Pang, Mao1; Liu, Bin1; Han, Lanqing4; Rong, Limin1

2022-05-01

10.1097/BRS.0000000000004267

SPINE   影响因子和分区

0362-2436

1528-1159

Study Design. A retrospective cohort study. Objective. 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). Summary of Background Data. 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. Methods. 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. Results. 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. Conclusion. 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.

critical spinal cord injury ICU length of stay prediction machine learning spinal cord injury

SCI

英语

其他

National Key Research and Development Program of China[2017YFA0105403] ; Key Research and Development Program of Guangdong Province[2019B020236002] ; Clinical innovation Research Program of Guangzhou Regenerative Medicine and Health Guangdong Laboratory[2018GZR0201006] ; Guangzhou Health Care Cooperative Innovation Major Project[201704020221] ; National Natural Science Foundation of China[82102640] ; China Postdoctoral Science Foundation[2019M663261] ; Guangdong Basic and Applied Basic Research Foundation[2019A1515111171] ; Guangzhou Science and Technology Project[202102080212] ; Medical Scientific Research Foundation of Guangdong Province[A2018547]

Neurosciences & Neurology ; Orthopedics

Clinical Neurology ; Orthopedics

WOS:000792433000003

LIPPINCOTT WILLIAMS & WILKINS

NEUROSCIENCE & BEHAVIOR

Web of Science

1.Sun Yat Sen Univ, Affiliated Hosp 3, Dept Spine Surg, 600 Tianhe Rd, Guangzhou 510630, Peoples R China
2.Southern Univ Sci & Technol Hosp, Intelligent & Digital Surg Innovat Ctr, Shenzhen, Guangdong, Peoples R China
3.Tongji Univ Sch Med, Shanghai Tenth Peoples Hosp, Dept Orthoped, Shanghai, Peoples R China
4.Artificial Intelligence Innovat Ctr, Res Inst Tsinghua, Pearl River Delta, Guangzhou 510735, Peoples R China
5.Tongji Univ, Sch Med, Shanghai, Peoples R China

Fan, Guoxin,Yang, Sheng,Liu, Huaqing,et al. Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury[J]. SPINE,2022,47(9).

Fan, Guoxin.,Yang, Sheng.,Liu, Huaqing.,Xu, Ningze.,Chen, Yuyong.,...&Rong, Limin.(2022).Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury.SPINE,47(9).

Fan, Guoxin,et al."Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury".SPINE 47.9(2022).