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

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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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Acknowledgements

This work is supported by the Key Research and Development Program of Jiangxi Province, China (No.20223BBG71S02), the central government guides local funds for scientific and technological development (No. 20222ZDH04095) and “Double Thousand Plan” Talent Project of Jiangxi Province, China.

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Author notes

  1. Meng-Pan Li and Wen-Cai Liu have contributed equally to this work and share the first authorship.

Authors and Affiliations

  1. Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People’s Republic of China

    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

  2. The First Clinical Medical College of Nanchang University, Nanchang, 330006, People’s Republic of China

    Meng-Pan Li & Wen-Cai Liu

  3. Department of Orthopaedics, Shanghai Jiao Tong University Affifiliated Sixth People’s Hospital, Shanghai, China

    Wen-Cai Liu

  4. Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People’s Republic of China

    Jia-Bao Wu, Kun Luo, Yu Liu, Yu Zhang, Shi-Ning Xiao, Zhi-Li Liu, Shan-Hu Huang & Jia-Ming Liu

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  1. Meng-Pan Li
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Correspondence to Shan-Hu Huang or Jia-Ming Liu.

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No benefits in any form have been or will be received from any commercial party related to the subject of this manuscript.

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This study was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University, and cases from the First Affiliated Hospital of Nanchang University signed a written informed consent form.

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Li, MP., Liu, WC., Wu, JB. et al. Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury. Eur Spine J 32, 3825–3835 (2023). https://doi.org/10.1007/s00586-023-07772-8

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  • DOI: https://doi.org/10.1007/s00586-023-07772-8

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