全膝关节置换术后院内身体功能延迟恢复的预测

IF 1.9 Q2 REHABILITATION Archives of rehabilitation research and clinical translation Pub Date : 2024-03-01 DOI:10.1016/j.arrct.2024.100321

Louisa T.M.A. Mulder MSc , Danielle D.P. Berghmans PhD , Peter Z. Feczko MD, PhD , Sander M.J. van Kuijk PhD , Rob A. de Bie PhD , Antoine F. Lenssen PhD

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引用次数: 0

摘要

目的通过开发和验证预测模型，包括术前体能参数和患者特征的组合，识别膝关节置换手术后院内功能恢复延迟的高风险患者。参与者260名患有膝关节骨性关节炎的成年人（N=260）（≥18岁），等待进行初级单侧全膝关节置换术，并在2016年至2020年间接受常规护理评估。0 分表示完全独立。潜在预测变量是术前体能参数和患者特征的组合。结果应用二元逻辑回归模型建立了初始模型。低德莫顿活动指数（DEMMI）、在室内使用助行器和低手握强度（HGS）是延迟院内康复的最重要预测因素。该模型经过内部验证，乐观校正 R2 为 0.07，曲线下面积为 61.2%。延迟院内康复的高风险概率用以下公式表示：Phighrisk=（1/（1+e(-(2.638-0.193×DEMMI+0.879×indoorwalkingaid-0.007×HGS))))×100%.结论该模型的预测值较低，判别能力较差。然而，术前体能与术后身体功能恢复之间存在正相关。我们的模型根据术前体能值和患者特征来区分高风险和低风险的有效性是有限的。

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Predicting Delayed In-Hospital Recovery of Physical Function After Total Knee Arthroplasty

Objective

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.

Design

Retrospective cohort study using binary logistic regression.

Setting

University hospital, orthopedic department.

Participants

260 adults (N=260) (≥18y) with knee osteoarthritis awaiting primary unilateral total knee arthroplasty and assessed during usual care between 2016 and 2020.

Intervention

Not applicable.

Main Outcome Measures

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.

Results

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 R² 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:

$P_{h i g h r i s k} = (1 / (1 + e^{(- (2.638 - 0.193 \times D E M M I + 0.879 \times i n d o o r w a l k i n g a i d - 0.007 \times H G S))})) \times 100 %$ .

Conclusions

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.

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