Interpretable Machine Learning Models for Short- and Long-term Prognostic Prediction and Risk Factor Identification in Radiofrequency Treatment of Lumbar Facetogenic Pain: A Retrospective Cohort Study with Temporal Validation.

Yunfei Wang, Ziyang Chen, Junjie Lu, Qingqing He, Jingyuan Liu, Zhifei Cui, Chengjie Huang,Tao Chen,Zhihai Su,Hai Lu

Spine（2025）SCI 2区

Cited 0|Views4

Abstract

STUDY DESIGN:Retrospective cohort study. OBJECTIVE:To develop machine learning (ML) models integrating clinical/imaging variables for predicting 3- and 6-month outcomes of radiofrequency (RF) treatment in lumbar facetogenic pain, and an independent temporal validation cohort was used to evaluate the model's performance. Shapley Additive Explanations (SHAP) analysis was utilized to identify key variables and construct a simplified model. SUMMARY OF BACKGROUND DATA:Early identification of RF-responsive patients remains challenging, with limited non-invasive prognostic tools available. METHODS:Six ML models were trained using 16 clinical/imaging variables from 372 RF-treated patients. Model performance was evaluated via AUROC, with SHAP analysis identifying key variables. Simplified models using clinical-only, imaging-only, and SHAP-selected variables were compared. RESULTS:In the discovery (n=312) and temporal validation (n=60) cohorts, 141 and 26 patients had unsuccessful 3-month outcomes, respectively. The logistic model outperformed others, achieving AUROCs of 0.834 (95% CI: 0.725-0.942) and 0.818 (0.713-0.923) for 3-month prediction in discovery and validation cohorts. Simplified models showed comparable performance (discovery AUROC: 0.795-0.837; validation: 0.699-0.814). Six-month predictions demonstrated similar robustness (discovery AUROC: 0.813; validation: 0.783). Decision curve analysis confirmed the logistic model's clinical utility, providing net benefits at threshold probabilities >40%. CONCLUSIONS:The Logistic model, which is based on clinical and imaging variables, has the potential to facilitate early screening of patients who might benefit from RF treatment in the short- and long-term. SHAP analysis helps evaluate the impact of variables and build simplified models with comparable performance. The key variables identified in this study require further verification through external geographic validations. LEVEL OF EVIDENCE:3.

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【要点】：本研究开发了一种结合临床和影像学变量的机器学习模型，用于预测射频治疗腰椎源性疼痛的3个月和6个月疗效，并通过Shapley Additive Explanations（SHAP）分析识别关键变量，构建了简化的预测模型。

【方法】：使用16个临床和影像学变量训练了6种机器学习模型，并通过AUROC评估模型性能，利用SHAP分析确定关键变量。

【实验】：在发现队列（n=312）和时序验证队列（n=60）中，对射频治疗后的3个月和6个月疗效进行预测。逻辑回归模型表现最佳，发现队列和验证队列的3个月预测AUROC分别为0.834和0.818。简化模型在发现队列和验证队列中的表现与原模型相当，3个月预测AUROC分别为0.795-0.837和0.699-0.814。6个月预测也显示出相似的稳健性（发现队列AUROC：0.813；验证队列：0.783）。使用的数据集名称未在文本中明确提及。