Abstract
Purpose
Postoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study aimed to create a deep learning-based model (DLM) to predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament (OPLL).
Methods
This prospective multicenter study was conducted by the 28 institutions, and 478 patients were included in the analysis. Deep learning was used to create two predictive models of the overall postoperative complications and neurological complications, one of the major complications. These models were constructed by learning the patient's preoperative background, clinical symptoms, surgical procedures, and imaging findings. These logistic regression models were also created, and these accuracies were compared with those of the DLM.
Results
Overall complications were observed in 127 cases (26.6%). The accuracy of the DLM was 74.6 ± 3.7% for predicting the overall occurrence of complications, which was comparable to that of the logistic regression (74.1%). Neurological complications were observed in 48 cases (10.0%), and the accuracy of the DLM was 91.7 ± 3.5%, which was higher than that of the logistic regression (90.1%).
Conclusion
A new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
This work was supported by Japanese Agency for Medical Research and Development (AMED) and Health and Labour Science Research Grants. This study was approved by each institutional review board.
Funding
This work was supported by a research grant funded by Japan Agency for Medical Research and Development Grant No. JP15ek0109136 and Japanese Health Labour Sciences Research Grant Number 40. No relevant financial activities outside the submitted work.
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Ito, S., Nakashima, H., Yoshii, T. et al. Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification. Eur Spine J 32, 3797–3806 (2023). https://doi.org/10.1007/s00586-023-07562-2
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DOI: https://doi.org/10.1007/s00586-023-07562-2