| 李凌峰,王思群.全膝关节置换术后膝关节功能康复个体化列线图预测模型的建立与验证[J].安徽医药,2023,27(12):2442-2446. |
| 全膝关节置换术后膝关节功能康复个体化列线图预测模型的建立与验证 |
| Establishment and validation of an individualized nomogram prediction model for knee functional recovery after total knee arthroplasty |
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| DOI：10.3969/j.issn.1009-6469.2023.12.024 |
| 中文关键词: 关节成形术，置换，膝 功能恢复 列线图 危险因素 预测模型 |
| 英文关键词: Arthroplasty, replacement, knee Functional recovery Nomogram Risk factor Predictive modeling |
| 基金项目:上海市第五人民医院人才培养项目( 2020WYRCSG01) |
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| 中文摘要: |
| 目的进行全膝关节置换术( TKA)后膝关节功能康复个体化列线图预测模型的建立与验证。方法选取 2019年 1月至 2021年 12月复旦大学附属上海市第五人民医院接收的 150例膝骨关节炎病人为建模组。选取 2020年 1月至 2021年 12月该院接收的 102例膝骨关节炎病人为验证组。将建模组病人分为康复不理想组( n=72)和康复理想组( n=78)。对建模组病人采用 logistic回归法筛选影响 TKA后膝关节功能康复不理想的危险因素；采用 R软件构建预测 TKA后膝关节功能康复不理想的列线图模型；用受试者操作特征(ROC)曲线评价模型区分度，校准曲线评估列线图模型一致性。结果康复不理想组年龄 ≥ 65岁比例 58.33%(42/72)、糖尿病比例 41.67%(30/72)、未有软组织平衡术比例 63.89%(46/72)、术后胫骨平台后倾角 ≥5°比例 45.83%(33/72)、术前 HSS评分 <45分比例 56.94%(41/72)、术后 VAS评分 ≥6分比例 62.50%(45/72)高于康复理想组［ 28.21%(22/78)、 19.23%(15/78)、32.05%(25/78)、 23.08%(18/78)、 26.92%(21/78)、32.05%(25/78)］(P<0.05)。 logistic回归分析显示，年龄≥65岁、未有软组织平衡术、术后胫骨平台后倾角 ≥5°、术前 HSS评分 <45分、术后 VAS评分 ≥6分是 TKA后膝关节功能康复不理想危险因素( P<0.05)。基于危险因素用 R软件建立列线图模型，建模组曲线下面积( AUC)及其 95%CI为 0.88(0.82，0.93)，验证组 AUC及其 95%CI为 0.89(0.82，0.95)，列线图模型的区分度良好，列线图模型的校准曲线预测值与实际值基本一致， Hos. mer-Lemeshow拟合优度检验显示，建模组 χ2=7.36，P=0.499；验证组 χ2=7.13，P=0.415。结论构建的预测 TKA后膝关节功能康复不理想列线图模型具有较大临床价值，可指导个体化治疗。 |
| 英文摘要: |
| Objective To establish and verify an individualized nomogram prediction model for knee joint functional recovery after total knee arthroplasty (TKA).Methods A total of 150 patients with knee osteoarthritis who received treatment at Shanghai Fifth Peo.ple's Hospital affiliated to Fudan University from January 2019 to December 2021 were selected as the modeling group. The 102 pa.tients with knee osteoarthritis who received in this hospital from January 2020 to December 2021 were selected as the validation group.The patients in the modeling group were divided into a poor recovery group (n=72) and an ideal recovery group (n=78). A logistic regres.sion method was used to screen the risk factors affecting poor knee joint functional recovery after TKA in the modeling group. R soft. ware was used to construct a nomogram model for predicting poor functional recovery of knee joint after TKA. A receiver operating char.acteristic (ROC) curve was used to evaluate the model discrimination, and the calibration curve was used to evaluate the nomogrammodel consistency.Results The proportion of age ≥ 65 years in the poor recovery group was 58.33% (42/72), the proportion of diabe.tes was 41.67% (30/72), the proportion of nonsoft tissue balance was 63.89% (46/72), the proportion of postoperative tibial plateau pos.terior inclination angle ≥ 5° was 45.83% (33/72), the proportion of preoperative HSS score < 45 points was 56.94% (41/72), and the pro.portion of postoperative VAS score ≥ 6 was 62.50% (45/72) higher than those in the ideal recovery group [28.21% (22/78), 19.23% (15/78), 32.05% (25/78), 23.08% (18/78), 26.92% (21/78), and 32.05% (25/78)] (P<0.05). Logistic regression analysis showed that age ≥65 years, nonsoft tissue balance surgery, postoperative tibial plateau posterior inclination ≥ 5°, preoperative HSS score < 45, and post.operative VAS score ≥ 6 were risk factors for poor functional recovery of the knee joint after TKA (P<0.05). Based on the risk factors, a nomogram model was established with R software, and the area under the curve (AUC) of the modeling group was 0.88 [95%CI: (0.82, 0.93)], and the area under the ROC curve of the validation group was 0.89 [95%CI: (0.82, 0.95)], the discrimination of the nomogrammodel was good, the predicted value of calibration curve of the nomogram model was basically consistent with the actual value, and theHosmer.Lemeshow goodness-of-fit test showed that the modeling group χ2=7.36, P=0.499; validation group χ2=7.13, P=0.415.Conclu. sion The constructed nomogram model for predicting poor knee joint functional recovery after TKA has great clinical value and canguide individualized treatment. |
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