| 贾永鹏.个体化预测老年人膝骨性关节炎患病风险的列线图模型构建[J].浙江中西医结合杂志,2024,34(7): |
| 个体化预测老年人膝骨性关节炎患病风险的列线图模型构建 |
| Construction of a column chart model for individualized prediction of the risk of knee osteoarthritis in the elderly |
| 投稿时间：2023-09-06 修订日期：2024-04-15 |
| DOI： |
| 中文关键词: 膝骨性关节炎 列线图模型 个体化预测 |
| 英文关键词:Knee osteoarthritis Column chart model Individualized prediction |
| 基金项目: |
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| 摘要点击次数: 936 |
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| 中文摘要: |
| 目的? 分析老年人发生膝骨性关节炎的影响因素,并构建个体化预测老年人膝骨性关节炎患病风险的列线图模型。方法? 回顾性选取2020年5月~2023年5月本院骨科收诊的426例老年患者为研究对象,按照7:3分为建模组(n=298)和验证组(n=128)。根据膝骨性关节炎的发生与否将建模组患者分为膝骨性关节炎组(n=57)和无膝骨性关节炎组(n=241)。收集可能影响老年人发生膝骨性关节炎的因素,并采用多因素Logistic回归模型分析老年人发生膝骨性关节炎的因素；R3.6.3软件构建预测老年人膝骨性关节炎风险的列线图模型；ROC曲线和校准曲线评估列线图模型预测老年人膝骨性关节炎风险的区分度和一致性。结果? 建模组和验证组在年龄、民族、文化程度、骨关节炎家族史、工作姿势、高血压等方面比较差异无统计学意义(P＜0.05)；无膝骨性关节炎组和膝骨性关节炎组在性别(?2=10.957)、久居地是否阴暗潮湿(?2=8.650)、骨关节炎家族史(?2=8.835)、工作姿势(?2=7.962)和工作类型(?2=8.408)方面比较差异具有统计学意义(P＜0.05)；多因素Logistic回归分析结果显示,性别(OR=3.283,95% CI:1.704~6.325)、久居地阴暗潮湿(OR=3.528,95% CI:1.703~7.308)、骨关节炎家族史(OR=3.415,95% CI:1.483~7.866)、工作姿势(OR=2.471,95% CI:1.236~4.939)、工作类型(OR=2.115,95% CI:1.033~4.327)为老年人患膝骨性关节炎的影响因素(P＜0.05)。建模组和验证组ROC曲线下面积分别为0.835(95%CI:0.766-0.905)和0.841(95%CI:0.753-0.928),Hosmer-Lemeshow拟合优度检验结果分别为?2=13.667,P=0.091和?2=11.405,P=0.180。结论? 性别、久居地阴暗潮湿、骨关节炎家族史、工作姿势、工作类型为老年人患膝骨性关节炎的影响因素,以此构建的列线图模型预测老年人膝骨性关节炎患病风险的区分度和一致性较好。 |
| 英文摘要: |
| Objective: To analyze the influencing factors of knee osteoarthritis in the elderly and construct an individualized column chart model to predict the risk of knee osteoarthritis in the elderly. Methods: A retrospective study was conducted on 426 elderly patients admitted to the orthopedics department of our hospital from May 2020 to May 2023. They were grouped into a modeling group (n=298) and a validation group (n=128) at 7:3. According to the occurrence of knee osteoarthritis, patients in the modeling group were grouped into knee osteoarthritis group (n=57) and non knee osteoarthritis group (n=241). The factors that may affect the occurrence of knee osteoarthritis in the elderly were collected, and a multivariate logistic regression model was applied to analyze the factors that affected the occurrence of knee osteoarthritis in the elderly; R3.6.3 software was applied to construct a column chart model for predicting the risk of knee osteoarthritis in the elderly; ROC curve and calibration curve were applied to evaluate the discrimination and consistency of column chart models in predicting the risk of knee osteoarthritis in the elderly. Results There was no statistically obvious difference between the modeling group and the validation group in terms of age, ethnicity, educational level, family history of osteoarthritis, work posture, hypertension, etc.(P＜0.05); there were statistically obvious differences between the non knee osteoarthritis group and the knee osteoarthritis group in terms of gender (?2=10.957), whether the long-term residence was dark and humid (?2=8.650), family history of osteoarthritis (?2=8.835), work posture (?2=7.962), and work type (?2=8.408)(P＜0.05); the results of multivariate logistic regression analysis showed that gender (OR=3.283, 95%CI: 1.704~6.325), dark and humid long-term residence (OR=3.528, 95%CI: 1.703~7.308), family history of osteoarthritis (OR=3.415, 95%CI: 1.483~7.866), work posture (OR=2.471, 95%CI: 1.236~4.939), and work type (OR=2.115, 95%CI: 1.033~4.327) were the influencing factors for knee osteoarthritis in the elderly(P＜0.05). The areas under the ROC curve for the modeling group and validation group was 0.835 (95%CI: 0.766-0.905) and 0.841 (95%CI: 0.753-0.928), respectively. The results of the Hosmer-Lemeshow goodness of fit test were ?2=13.667, P=0.091, and ?2=11.405, P=0.180, respectively. Conclusion: Gender, dark and humid long-term residence, family history of osteoarthritis, work posture, and work type are the influencing factors of knee osteoarthritis in the elderly. The column chart model constructed based on this has good discrimination and consistency in predicting the risk of knee osteoarthritis in the elderly. |
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