Heisinger Stephan, Hitzl Wolfgang, Hobusch Gerhard M, Windhager Reinhard, Cotofana Sebastian
Department of Orthopedics and Trauma Surgery, Medical University of Vienna, 1090 Vienna, Austria.
Research Office, Biostatistics, Paracelsus Medical University, 5020 Salzburg, Austria.
J Clin Med. 2020 May 1;9(5):1298. doi: 10.3390/jcm9051298.
The aim of the study was to longitudinally investigate symptomatic and structural factors prior to total knee replacement (TKR) surgery in order to identify influential factors that can predict a patient's need for TKR surgery. In total, 165 participants (60% females; 64.5 ± 8.4 years; 29.7 ± 4.7 kg/m) receiving a TKR in any of both knees within a four-year period were analyzed. Radiographic change, knee pain, knee function and quality of life were annually assessed prior to the TKR procedure. Self-learning artificial neural networks were applied to identify driving factors for the surgical procedure. Significant worsening of radiographic structural change was observed prior to TKR ( ≤ 0.0046), whereas knee symptoms (pain, function, quality of life) worsened significantly only in the year prior to the TKR procedure. By using our prediction model, we were able to predict correctly 80% of the classified individuals to undergo TKR surgery with a positive predictive value of 84% and a negative predictive value of 73%. Our prediction model offers the opportunity to assess a patient's need for TKR surgery two years in advance based on easily available patient data and could therefore be used in a primary care setting.
本研究的目的是纵向调查全膝关节置换术（TKR）手术前的症状和结构因素，以确定能够预测患者是否需要进行TKR手术的影响因素。总共分析了165名参与者（60%为女性；年龄64.5±8.4岁；体重指数29.7±4.7kg/m²），他们在四年内双侧膝关节中的任何一侧接受了TKR手术。在TKR手术前，每年对影像学变化、膝关节疼痛、膝关节功能和生活质量进行评估。应用自学习人工神经网络来确定手术的驱动因素。在TKR手术前观察到影像学结构变化显著恶化（≤0.0046），而膝关节症状（疼痛、功能、生活质量）仅在TKR手术前一年显著恶化。通过使用我们的预测模型，我们能够正确预测80%接受TKR手术的分类个体，阳性预测值为84%，阴性预测值为73%。我们的预测模型提供了一个机会，可根据容易获得的患者数据提前两年评估患者对TKR手术的需求，因此可用于初级保健环境。
