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Objectives <p>To identify independent risk and protective factors for acupuncture response in community-dwelling patients with chronic low back pain, and to develop and validate a clinical prediction model incorporating traditional Chinese medicine (TCM) diagnostic components, thereby providing a tool for individualized clinical decision-making and risk stratification in community acupuncture practice.</p> Methods <p>A total of 500 patients with chronic non-specific low back pain who received acupuncture treatment at the Ningbo Jiangbei Zhuangqiao Community Health Service Center between January 2023 and November 2025 were retrospectively enrolled. Patients were randomly split into a training cohort (<i>n</i> = 350) for model development and a test cohort (<i>n</i> = 150) for internal validation using a 7:3 ratio. Predictors were selected via LASSO regression, and a multivariable logistic regression model was constructed and presented as a clinical nomogram. SHapley Additive exPlanations (SHAP) analysis was employed to quantify the global importance of features and their directional association with the outcome. Model performance was comprehensively evaluated by assessing discrimination (receiver operating characteristic curve), calibration (calibration curve), clinical utility (decision curve analysis), and generalizability (performance in the internal/external validation sets).</p> Results <p>Multivariable analysis identified longer disease duration (OR = 1.170), radiating leg pain (OR = 1.998), and the Qi-Stagnation-Blood-Stasis syndrome pattern (OR = 3.701) as independent risk factors for poor acupuncture response (all <i>p</i> &lt; 0.05), while acupoint Weizhong (BL40) selection (OR = 0.267) and combined therapy (OR = 0.214) were independent protective factors. SHAP analysis confirmed disease duration and the Qi-Stagnation-Blood-Stasis pattern as the top contributors to the prediction. The developed nomogram demonstrated excellent discrimination in the training (AUC = 0.819), test (AUC = 0.828), and external validation (AUC = 0.788) cohorts. The model showed good calibration (Hosmer-Lemeshow test <i>p</i> &gt; 0.05) and provided a clear clinical net benefit across a wide threshold probability range (25%-90%).</p> Conclusions <p>This study identifies a TCM syndrome pattern (Qi-Stagnation-Blood-Stasis) and acupoint selection as independent predictors for acupuncture response in community-based low back pain management. The developed nomogram, integrating TCM and clinical features, demonstrates good predictive performance and clinical utility upon internal and preliminary external validation. Its broader implementation requires further confirmation through larger, multicenter prospective studies and could be enhanced by the future integration of objective biomarkers.</p>

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Development and validation of a clinical prediction model for acupuncture response in community-dwelling patients with chronic low back pain: a retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Jianfei Wang,
  • Tianci Hu,
  • Minjie Zhao,
  • Chunrong Wu

摘要

Objectives

To identify independent risk and protective factors for acupuncture response in community-dwelling patients with chronic low back pain, and to develop and validate a clinical prediction model incorporating traditional Chinese medicine (TCM) diagnostic components, thereby providing a tool for individualized clinical decision-making and risk stratification in community acupuncture practice.

Methods

A total of 500 patients with chronic non-specific low back pain who received acupuncture treatment at the Ningbo Jiangbei Zhuangqiao Community Health Service Center between January 2023 and November 2025 were retrospectively enrolled. Patients were randomly split into a training cohort (n = 350) for model development and a test cohort (n = 150) for internal validation using a 7:3 ratio. Predictors were selected via LASSO regression, and a multivariable logistic regression model was constructed and presented as a clinical nomogram. SHapley Additive exPlanations (SHAP) analysis was employed to quantify the global importance of features and their directional association with the outcome. Model performance was comprehensively evaluated by assessing discrimination (receiver operating characteristic curve), calibration (calibration curve), clinical utility (decision curve analysis), and generalizability (performance in the internal/external validation sets).

Results

Multivariable analysis identified longer disease duration (OR = 1.170), radiating leg pain (OR = 1.998), and the Qi-Stagnation-Blood-Stasis syndrome pattern (OR = 3.701) as independent risk factors for poor acupuncture response (all p < 0.05), while acupoint Weizhong (BL40) selection (OR = 0.267) and combined therapy (OR = 0.214) were independent protective factors. SHAP analysis confirmed disease duration and the Qi-Stagnation-Blood-Stasis pattern as the top contributors to the prediction. The developed nomogram demonstrated excellent discrimination in the training (AUC = 0.819), test (AUC = 0.828), and external validation (AUC = 0.788) cohorts. The model showed good calibration (Hosmer-Lemeshow test p > 0.05) and provided a clear clinical net benefit across a wide threshold probability range (25%-90%).

Conclusions

This study identifies a TCM syndrome pattern (Qi-Stagnation-Blood-Stasis) and acupoint selection as independent predictors for acupuncture response in community-based low back pain management. The developed nomogram, integrating TCM and clinical features, demonstrates good predictive performance and clinical utility upon internal and preliminary external validation. Its broader implementation requires further confirmation through larger, multicenter prospective studies and could be enhanced by the future integration of objective biomarkers.