Machine learning insights into patient satisfaction following lateral lumbar interbody fusion

Functional outcomes, particularly improvements in low back pain, walking ability, and mental health, are the primary determinants of patient satisfaction following LLIF surgery. In contrast, surgical factors play a less significant role. Mental health emerged as a critical factor, underscoring the importance of addressing psychological recovery through preoperative counseling and personalized postoperative care. The analysis demonstrated that ML models, especially Random Forest, are effective tools for identifying the factors most predictive of postoperative satisfaction. These findings highlight the potential of ML techniques to enhance personalized treatment planning and improve outcomes by focusing on both physical and mental recovery. Further research, including multi-center studies and the integration of psychological variables, is needed to provide a more comprehensive understanding of patient satisfaction after LLIF surgery.

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Publication Details

Journal
European Spine Journal
Published
2025-02-05
DOI
https://doi.org/10.1007/s00586-025-08659-6
Citations
3
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
2.02
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article

Machine learning insights into patient satisfaction following lateral lumbar interbody fusion

Akihiko Hiyama, Daisuke Sakai, Masahiko Watanabe, Hiroyuki Katoh et al.
3 citations
European Spine Journal
Medical Imaging and Analysis
2.02
article

Machine learning insights into patient satisfaction following lateral lumbar interbody fusion

Akihiko Hiyama, Daisuke Sakai, Masahiko Watanabe, Hiroyuki Katoh, Masato Sato
article en
3 citations

Abstract

Functional outcomes, particularly improvements in low back pain, walking ability, and mental health, are the primary determinants of patient satisfaction following LLIF surgery. In contrast, surgical factors play a less significant role. Mental health emerged as a critical factor, underscoring the importance of addressing psychological recovery through preoperative counseling and personalized postoperative care. The analysis demonstrated that ML models, especially Random Forest, are effective tools for identifying the factors most predictive of postoperative satisfaction. These findings highlight the potential of ML techniques to enhance personalized treatment planning and improve outcomes by focusing on both physical and mental recovery. Further research, including multi-center studies and the integration of psychological variables, is needed to provide a more comprehensive understanding of patient satisfaction after LLIF surgery.

European Spine JournalVol. 34(9)
Tokai University (JP)
Openalex Percentile: Top 16%
Medical Imaging and Analysis
2.02
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