Machine learning approach in predicting clinically significant improvements after surgery in patients with cervical ossification of the posterior longitudinal ligament

Satoshi Maki, Takeo Furuya, Toshitaka Yoshii, Satoru Egawa, Kenichiro Sakai, Kazuo Kusano, Yukihiro Nakagawa, Takashi Hirai, Kanichiro Wada, Keiichi Katsumi, Kengo Fujii, Atsushi Kimura, Narihito Nagoshi, Tsukasa Kanchiku, Yukitaka Nagamoto, Yasushi Oshima, Kei Ando, Masahiko Takahata, Kanji Mori, Hideaki NakajimaKazuma Murata, Shunji Matsunaga, Takashi Kaito, Kei Yamada, Sho Kobayashi, Satoshi Kato, Tetsuro Ohba, Satoshi Inami, Shunsuke Fujibayashi, Hiroyuki Katoh, Haruo Kanno, Shiro Imagama, Masao Koda, Yoshiharu Kawaguchi, Katsushi Takeshita, Morio Matsumoto, Seiji Ohtori, Masashi Yamazaki, Atsushi Okawa

研究成果: Article査読

2 被引用数 (Scopus)

抄録

Study Design. A retrospective analysis of prospectively collected data. Objective. This study aimed to create a prognostic model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using machine learning (ML). Summary of Background Data. Determining surgical outcomes helps surgeons provide prognostic information to patients and manage their expectations. ML is a mathematical model that finds patterns from a large sample of data and makes predictions outperforming traditional statistical methods. Methods. Of 478 patients, 397 and 370 patients had complete follow-up information at 1 and 2years, respectively, and were included in the analysis. A minimal clinically important difference (MCID) was defined as an acquired Japanese Orthopedic Association (JOA) score of ≥2.5 points, after which a ML model that predicts whether MCID can be achieved 1 and 2years after surgery was created. Patient background, clinical symptoms, and imaging findings were used as variables for analysis. The ML model was created using LightGBM, XGBoost, random forest, and logistic regression, after which the accuracy and area under the receiver-operating characteristic curve (AUC) were calculated. Results. The mean JOA score was 10.3 preoperatively, 13.4 at 1year after surgery, and 13.5 at 2years after surgery. XGBoost showed the highest AUC (0.72) and high accuracy (67.8) for predicting MCID at 1year, whereas random forest had the highest AUC (0.75) and accuracy (69.6) for predicting MCID at 2years. Among the included features, total preoperative JOA score, duration of symptoms, body weight, sensory function of the lower extremity sub-score of the JOA, and age were identified as having the most significance in most of ML models. Conclusion. Constructing a prognostic ML model for surgical outcomes in patients with OPLL is feasible, suggesting the potential application of ML for predictive models of spinal surgery.

本文言語English
ページ(範囲)1683-1689
ページ数7
ジャーナルSpine
46
24
DOI
出版ステータスPublished - 2021 12月 15

ASJC Scopus subject areas

  • 整形外科およびスポーツ医学
  • 臨床神経学

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