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European Spine Journal
Published in: European Spine Journal 11/2023

17-05-2023 | Original Article

Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury

Authors: Meng-Pan Li, Wen-Cai Liu, Jia-Bao Wu, Kun Luo, Yu Liu, Yu Zhang, Shi-Ning Xiao, Zhi-Li Liu, Shan-Hu Huang, Jia-Ming Liu

Published in: European Spine Journal | Issue 11/2023

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Abstract

Purpose

The purpose of this study was to establish the best prediction model for postoperative nosocomial pulmonary infection through machine learning (ML) and assist physicians to make accurate diagnosis and treatment decisions.

Methods

Patients with spinal cord injury (SCI) who admitted to a general hospital between July 2014 and April 2022 were included in this study. The data were segmented according to the ratio of seven to three, 70% were randomly selected to train the model, and the other 30% were used for testing. We used LASSO regression to screen the variables, and the selected variables were used in the construction of six different ML models. Shapley additive explanations and permutation importance were used to explain the output of the ML models. Finally, sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were used as the evaluation index of the model.

Results

A total of 870 patients were enrolled in this study, of whom 98 (11.26%) developed pulmonary infection. Seven variables were used for ML model construction and multivariate logistic regression analysis. Among these variables, age, ASIA scale and tracheotomy were found to be the independent risk factors for postoperative nosocomial pulmonary infection in SCI patients. Meanwhile, the prediction model based on RF algorithm performed best in the training and test sets. (AUC = 0.721, accuracy = 0.664, sensitivity = 0.694, specificity = 0.656).

Conclusion

Age, ASIA scale and tracheotomy were the independent risk factors of postoperative nosocomial pulmonary infection in SCI. The prediction model based on RF algorithm had the best performance.
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Metadata
Title
Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury
Authors
Meng-Pan Li
Wen-Cai Liu
Jia-Bao Wu
Kun Luo
Yu Liu
Yu Zhang
Shi-Ning Xiao
Zhi-Li Liu
Shan-Hu Huang
Jia-Ming Liu
Publication date
17-05-2023
Publisher
Springer Berlin Heidelberg
Published in
European Spine Journal / Issue 11/2023
Print ISSN: 0940-6719
Electronic ISSN: 1432-0932
DOI
https://doi.org/10.1007/s00586-023-07772-8

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Author's Reply

Answer to the Letter to the Editor of T. Sugita et al. concerning "Alterations of 3D acetabular and lower limb parameters in adolescent idiopathic scoliosis" by Karam M, et al. (Eur Spine J [2020]; 29(8):2010–2017): Does windswept deformity of the knee exist in 274 participants with adolescent idiopathic scoliosis?

Letter to the Editor

Letter to the Editor concerning "Alterations of 3D acetabular and lower limb parameters in adolescent idiopathic scoliosis" by Karam M, et al. (Eur Spine J [2020]; 29(8):2010–2017): Does windswept deformity of the knee exist in 274 participants with adolescent idiopathic scoliosis?

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