前頚部椎間板摘出術および固定術後の早期融合を予測するための解釈可能な機械学習モデルの開発と検証

Retrospective case-control study.

This study aimed to develop and preliminarily validate a machine learning (ML) model for predicting the likelihood of early fusion (EF) after anterior cervical discectomy and fusion (ACDF) and to explore the influential factors.

ACDF is a commonly performed procedure, where EF plays an important role in achieving favorable outcomes. However, EF varies substantially among patients, and reliable predictive approaches remain limited.

We retrospectively analyzed 1,039 surgical segments from 840 patients who underwent ACDF between 2013 and 2020. EF, defined as radiographic fusion within 3 months, was assessed using standard imaging criteria. Basic information, laboratory indicators, perioperative data, and radiological parameters were collected. After multiple imputation and dimensionality reduction, nine ML algorithms were trained, evaluated and compared. SHapley Additive exPlanations (SHAP) were applied for model interpretation.

Among the nine algorithms, stochastic gradient boosting (SGB) exhibited the highest predictive ability, with an AUC of 0.884 in the training set and 0.830 in the testing set. SHAP analysis indicated that preoperative functional spinal unit (FSU) range of motion (ROM), ΔFSU height, fasting plasma glucose (FPG), calcium (Ca), low-density lipoprotein cholesterol (LDL-C), surgical type, age, and femoral bone mineral density (BMD) were the most influential factors. Higher preoperative FSU ROM, FPG, LDL-C, age, and two-level surgery were associated with a lower probability of EF, whereas optimal ΔFSU height and higher Ca and femoral BMD were favored EF.

This exploratory study established an ML-based approach for predicting EF after ACDF, with the SGB algorithm showing relatively strong predictive performance. The identified influential factors may provide preliminary insights for individualized clinical assessment and perioperative management, warranting further validation in multicenter settings.