Tiulpin Aleksei, Saarakkala Simo, Mathiessen Alexander, Hammer Hilde Berner, Furnes Ove, Nordsletten Lars, Englund Martin, Magnusson Karin
Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
Osteoarthr Cartil Open. 2022 Nov 6;4(4):100319. doi: 10.1016/j.ocarto.2022.100319. eCollection 2022 Dec.
To investigate the value of ultrasonographic data in predicting total knee replacement (TKR).
Data from the Musculoskeletal Pain in Ullensaker study (MUST) was linked to the Norwegian Arthroplasty Register to form a 5-7 year prospective cohort study of 630 persons (69% women, mean (SD) age 64 (8.7) years). We examined the predictive power of ultrasound (US) features, i.e. osteophytes, meniscal extrusion, synovitis in the suprapatellar recess, femoral cartilage thickness, and quality for future knee osteoarthritis (OA) surgery. We investigated 4 main settings for multivariate predictive modeling: 1) clinical predictors (age, sex, body mass index, knee injury, familial OA and workload), 2) radiographic data (assessed by the Kellgren Lawrence grade, KL) with clinical predictors, 3) US features and clinical predictors. Finally, we also considered an ensemble of models 2) and 3) and used it as our fifth model. All models were compared using the Average Precision (AP) and the Area Under Receiver Operating Characteristic Curve (AUC) metrics.
Clinical predictors yielded AP of 0.11 (95% confidence interval [CI] 0.05-0.23) and AUC of 0.69 (0.58-0.79). Clinical predictors with KL grade yielded AP of 0.20 (0.12-0.33) and AUC of 0.81 (0.67-0.90). The clinical variables with ultrasound yielded AP of 0.17 (0.08-0.30) and AUC of 0.79 (0.69-0.86).
Ultrasonographic examination of the knee may provide added value to basic clinical and demographic descriptors when predicting TKR. While it does not achieve the same predictive performance as radiography, it can provide additional value to the radiographic examination.
探讨超声数据在预测全膝关节置换术（TKR）中的价值。
将乌伦斯aker肌肉骨骼疼痛研究（MUST）的数据与挪威关节置换登记处的数据相联系，形成一项对630人进行的5至7年前瞻性队列研究（69%为女性，平均（标准差）年龄64（8.7）岁）。我们研究了超声（US）特征的预测能力，即骨赘、半月板挤压、髌上囊滑膜炎、股骨软骨厚度以及未来膝关节骨关节炎（OA）手术的质量。我们研究了多变量预测模型的4种主要设置：1）临床预测因素（年龄、性别、体重指数、膝关节损伤、家族性OA和工作量），2）结合临床预测因素的放射学数据（通过Kellgren Lawrence分级，KL评估），3）超声特征和临床预测因素。最后，我们还考虑了模型2）和3）的组合，并将其用作我们的第五个模型。使用平均精度（AP）和受试者操作特征曲线下面积（AUC）指标对所有模型进行比较。
临床预测因素的AP为0.11（95%置信区间[CI]0.05 - 0.23），AUC为0.69（0.58 - 0.79）。临床预测因素与KL分级的AP为0.20（0.12 - 0.33），AUC为0.81（0.67 - 0.90）。临床变量与超声的AP为0.17（0.08 - 0.30），AUC为0.79（0.69 - 0.86）。
在预测TKR时，膝关节超声检查可能为基本临床和人口统计学描述提供附加价值。虽然它没有达到与放射学相同的预测性能，但它可以为放射学检查提供额外价值。
