Department of Anesthesiology, The Affiliated Wuxi NO.2 People's Hospital of Nanjing Medical University, Wuxi, Jiangsu, China.
Department of Anesthesiology, Wuxi NO.2 People's Hospital, Wuxi, Jiangsu, China.
Comput Math Methods Med. 2022 Sep 26;2022:5005195. doi: 10.1155/2022/5005195. eCollection 2022.
This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals.
A total of 5386 individuals above 45 years old were obtained from the National Health and Nutrition Examination Survey. Participants were randomly divided into a training set and a test set at a 7 : 3 ratio. The training set was used to create a prediction model, whereas the test set was used to validate the proposed model. We constructed multiple predictive models based on three machine learning methods: logistic regression, random forest, and Extreme Gradient Boosting. The model performance was evaluated by areas under the receiver (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. Additionally, we created a simplified nomogram based on logistic regression for better clinical application.
About 31.4% (1690) individuals were with self-reported knee pain. The logistic regression showed that female gender (odds ratio [OR] = 1.28), pain elsewhere (OR = 4.64), and body mass index (OR = 1.05) were significantly associated with increased risk of knee pain. In the test set, the logistic regression (AUC = 0.71) showed similar but slightly higher accuracy than the random forest (AUC = 0.70), while the performance of the Extreme Gradient Boosting model was less reliable (AUC = 0.59). Based on mean decrease accuracy, the most important first five predictions were pain elsewhere, waist circumference, body mass index, age, and gender. Additionally, the most important first five predictions with the highest mean decrease Gini index were pain elsewhere, body mass index, waist circumference, triglycerides, and age. The nomogram model showed good discrimination ability with an AUC of 0.75 (0.73-0.77), a sensitivity of 0.72, specificity of 0.71, a positive predictive value of 0.45, and a negative predictive value of 0.88.
This study proposed a convenient nomogram tool to evaluate the risk of knee pain for the middle-aged and elderly US population in primary care. All the input variables can be easily obtained in a clinical setting, and no additional radiologic assessments were required.
本研究使用机器学习方法建立了中老年人膝关节疼痛预测模型。
本研究共纳入 5386 名年龄大于 45 岁的个体，来自全国健康与营养调查。参与者以 7∶3 的比例随机分为训练集和测试集。训练集用于创建预测模型，而测试集用于验证所提出的模型。我们基于三种机器学习方法（逻辑回归、随机森林和极端梯度提升）构建了多个预测模型。通过接收者工作特征曲线下面积（AUC）、灵敏度、特异性、阳性预测值和阴性预测值来评估模型性能。此外，我们基于逻辑回归创建了一个简化的诺模图，以方便临床应用。
约 31.4%（1690 人）报告有膝关节疼痛。逻辑回归显示，女性（比值比 [OR] = 1.28）、其他部位疼痛（OR = 4.64）和体重指数（OR = 1.05）与膝关节疼痛风险增加显著相关。在测试集中，逻辑回归（AUC = 0.71）的准确性与随机森林（AUC = 0.70）相似，但稍高，而极端梯度提升模型的性能不太可靠（AUC = 0.59）。基于平均减少准确性，最重要的前五个预测因素是其他部位疼痛、腰围、体重指数、年龄和性别。此外，基于平均减少基尼指数，最重要的前五个预测因素是其他部位疼痛、体重指数、腰围、甘油三酯和年龄。列线图模型显示出良好的区分能力，AUC 为 0.75（0.73-0.77），灵敏度为 0.72，特异性为 0.71，阳性预测值为 0.45，阴性预测值为 0.88。
本研究提出了一种方便的列线图工具，用于评估美国中老年人群在初级保健中膝关节疼痛的风险。所有输入变量都可以在临床环境中轻松获得，且无需额外的影像学评估。
