Abstract
Purpose
By predicting short-term postoperative outcomes before surgery, patients undergoing cervical laminoplasty (CLP) surgery could benefit from more accurate patient care strategies that could reduce the likelihood of adverse outcomes. With this study, we developed a series of machine learning (ML) models for predicting short-term postoperative outcomes and integrated them into an open-source online application.
Methods
National surgical quality improvement program database was utilized to identify individuals who have undergone CLP surgery. The investigated outcomes were prolonged length of stay (LOS), non-home discharges, 30-day readmissions, unplanned reoperations, and major complications. ML models were developed and implemented on a website to predict these three outcomes.
Results
A total of 1740 patients that underwent CLP were included in the analysis. Performance evaluation indicated that the top-performing models for each outcome were the models built with TabPFN and LightGBM algorithms. The TabPFN models yielded AUROCs of 0.830, 0.847, and 0.858 in predicting non-home discharges, unplanned reoperations, and major complications, respectively. The LightGBM models yielded AUROCs of 0.812 and 0.817 in predicting prolonged LOS, and 30-day readmissions, respectively.
Conclusion
The potential of ML approaches to predict postoperative outcomes following spine surgery is significant. As the volume of data in spine surgery continues to increase, the development of predictive models as clinically relevant decision-making tools could significantly improve risk assessment and prognosis. Here, we present an accessible predictive model for predicting short-term postoperative outcomes following CLP intended to achieve the stated objectives.
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Data availability
Restrictions apply to the availability of these data. Data were obtained from American College of Surgeons National Surgical Quality Improvement Program and are available (https://www.facs.org/quality-programs/data-and-registries/acs-nsqip/) with the permission of American College of Surgeons.
Code availability
The source code for preprocessing and analyzing the data is available on GitHub (https://github.com/mertkarabacak/NSQIP-CLP).
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Conceptualization, MK and KM; methodology, MK and KM; software, MK; formal analysis, MK; data curation, MK; writing—original draft preparation, MK; writing—review and editing, KM; visualization, MK; supervision, KM; project administration, MK and KM.
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The American College of Surgeons National Surgical Quality Improvement Program and the hospitals participating in the ACS NSQIP are the source of the data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors.
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This study was deemed exempt from approval by the Icahn School of Medicine at Mount Sinai institutional review board because it involved analysis of deidentified patient data.
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Karabacak, M., Margetis, K. Development of personalized machine learning-based prediction models for short-term postoperative outcomes in patients undergoing cervical laminoplasty. Eur Spine J 32, 3857–3867 (2023). https://doi.org/10.1007/s00586-023-07923-x
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DOI: https://doi.org/10.1007/s00586-023-07923-x