Mulder Louisa T M A, Berghmans Danielle D P, Feczko Peter Z, van Kuijk Sander M J, de Bie Rob A, Lenssen Antoine F
Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
Department of Physical Therapy, Maastricht University Medical Center+, Maastricht, The Netherlands.
Arch Rehabil Res Clin Transl. 2024 Jan 20;6(1):100321. doi: 10.1016/j.arrct.2024.100321. eCollection 2024 Mar.
To identify patients at high risk of delayed in-hospital functional recovery after knee replacement surgery by developing and validating a prediction model, including a combination of preoperative physical fitness parameters and patient characteristics.
Retrospective cohort study using binary logistic regression.
University hospital, orthopedic department.
260 adults (N=260) (≥18y) with knee osteoarthritis awaiting primary unilateral total knee arthroplasty and assessed during usual care between 2016 and 2020.
Not applicable.
Time to reach in-hospital functional independence (in days), measured by the modified Iowa Level of Assistance Scale. A score of 0 means completely independent. Potential predictor variables are a combination of preoperative physical fitness parameters and patient characteristics.
Binary logistic regression modeling was applied to develop the initial model. A low de Morton Mobility Index (DEMMI), walking aid use indoors, and a low handgrip strength (HGS) were the most important predictors of delayed in-hospital recovery. This model was internally validated and had an optimism-corrected of 0.07 and an area under curve of 61.2%. The probability of a high risk of delayed in-hospital recovery is expressed by the following equation:.
The model has a low predictive value and a poor discriminative ability. However, there is a positive association between preoperative physical fitness and postoperative recovery of physical function. The validity of our model to distinguish between high and low risk, based on preoperative fitness values and patient characteristics, is limited.
通过开发和验证一个预测模型，识别膝关节置换术后住院功能恢复延迟的高危患者，该模型包括术前体能参数和患者特征的组合。
采用二元逻辑回归的回顾性队列研究。
大学医院骨科。
260名成年人（N = 260）（≥18岁），患有膝关节骨关节炎，等待初次单侧全膝关节置换术，并在2016年至2020年的常规护理期间接受评估。
不适用。
达到住院功能独立的时间（以天为单位），通过改良的爱荷华州协助水平量表测量。得分为0表示完全独立。潜在的预测变量是术前体能参数和患者特征的组合。
应用二元逻辑回归建模来开发初始模型。低德莫顿活动指数（DEMMI）、在室内使用助行器以及低握力（HGS）是住院恢复延迟的最重要预测因素。该模型进行了内部验证，乐观校正值为0.07，曲线下面积为61.2%。住院恢复延迟高风险的概率由以下方程表示：。
该模型预测价值低，判别能力差。然而，术前体能与术后身体功能恢复之间存在正相关。基于术前体能值和患者特征，我们区分高风险和低风险的模型有效性有限。
