Han Changxiao, Yang Guangyi, Wen Haibao, Fu Minrui, Peng Bochen, Xu Bo, Yin Xunlu, Wang Ping, Zhu Liguo, Feng Minshan
Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Beijing University of Chinese Medicine, Beijing, 100102, China.
Chin Med. 2025 May 27;20(1):74. doi: 10.1186/s13020-025-01131-z.
Neck pain (NP) ranks among the leading causes of years lived with disability worldwide. While spinal manipulation is a common physical therapy intervention for NP, its variable patient responses and inherent risks necessitate careful patient selection. This study aims to develop and validate a machine learning-based prediction model to identify NP patients most likely to benefit from spinal manipulation.
This multicenter study analyzed 623 NP patients in a retrospective cohort and 319 patients from a separate hospital for external validation, with data collected between May 2020 and November 2024. Treatment success was defined as achieving ≥ 50% reduction in Numerical Rating Scale (NRS) and ≥ 30% reduction in Neck Disability Index (NDI) after two weeks of spinal manipulation. We compared data imputation methods through density plots, and conducted δ-adjusted sensitivity analysis. Then employed both Boruta algorithm and LASSO regression to select relevant predictors from 40 initial features, and four feature subsets (Boruta-selected, LASSO-selected, intersection, and union) were evaluated to determine the optimal combination. Nine machine learning algorithms were tested using internal validation (70% training, 30% testing) and external validation. Performance metrics included Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, F1-score, sensitivity, specificity, and predictive values. The SHAP framework enhanced model interpretability. Youden's Index was applied to determine the optimal predictive probability threshold for clinical decision support, and a web-based application was developed for clinical implementation.
The combined LASSO and Boruta algorithms identified nine optimal predictors, with the union feature set achieving superior performance. Among the algorithms tested, the Multilayer Perceptron (MLP) model demonstrated optimal performance with an AUC of 0.823 (95% CI 0.750, 0.874) in the test set, showing consistency between training (AUC = 0.829) and test performance. External validation confirmed robust performance (AUC: 0.824, accuracy: 0.765, F1 score: 0.76) with satisfactory calibration (Brier score = 0.170). SHAP analysis highlighted the significant predictive value of clinical measurements and patient characteristics. Based on Youden's Index, the optimal predictive probability threshold was 0.603, yielding a sensitivity of 0.762 and specificity of 0.802. The model was implemented as a web-based application providing real-time probability calculations and interactive SHAP force plots.
Our machine learning model demonstrates robust performance in identifying suitable candidates for spinal manipulation among neck pain patients, offering clinicians an evidence-based practical tool to optimize patient selection and potentially improve treatment outcomes.
颈部疼痛（NP）是全球导致残疾生存年数的主要原因之一。虽然脊柱推拿是治疗NP的常见物理治疗干预方法，但其患者反应的变异性和固有风险使得需要谨慎选择患者。本研究旨在开发并验证一种基于机器学习的预测模型，以识别最有可能从脊柱推拿中获益的NP患者。
这项多中心研究分析了回顾性队列中的623例NP患者以及另一家医院的319例患者进行外部验证，数据收集于2020年5月至2024年11月之间。治疗成功定义为在脊柱推拿两周后数字评定量表（NRS）降低≥50%且颈部残疾指数（NDI）降低≥30%。我们通过密度图比较了数据插补方法，并进行了δ调整敏感性分析。然后使用Boruta算法和LASSO回归从40个初始特征中选择相关预测因子，并评估了四个特征子集（Boruta选择的、LASSO选择的、交集和并集）以确定最佳组合。使用内部验证（70%训练，30%测试）和外部验证测试了九种机器学习算法。性能指标包括受试者工作特征曲线下面积（AUC）、准确性、F1分数、敏感性、特异性和预测值。SHAP框架增强了模型的可解释性。应用约登指数确定用于临床决策支持的最佳预测概率阈值，并开发了基于网络的应用程序用于临床实施。
联合LASSO和Boruta算法确定了九个最佳预测因子，其中并集特征集表现出卓越性能。在测试的算法中，多层感知器（MLP）模型在测试集中表现最佳，AUC为0.823（95%CI 0.750，0.874），显示出训练（AUC = 0.829）和测试性能之间的一致性。外部验证证实了强大的性能（AUC：0.824，准确性：0.765，F1分数：0.76）以及令人满意的校准（布里尔分数 = 0.170）。SHAP分析突出了临床测量和患者特征的显著预测价值。基于约登指数，最佳预测概率阈值为0.603，敏感性为0.762，特异性为0.802。该模型被实现为一个基于网络的应用程序，提供实时概率计算和交互式SHAP力场图。
我们的机器学习模型在识别颈部疼痛患者中适合进行脊柱推拿的合适人选方面表现出强大性能，为临床医生提供了一种基于证据的实用工具，以优化患者选择并可能改善治疗结果。
