Development of a Prediction Model to Estimate the Risk of Walking Limitations in Patients with Total Knee Arthroplasty

Pua, Yong-Hao; Seah, Felicia Jie-Ting; Clark, Ross; Poon, Cheryl Lian-Li; Tan, Ming Jen; Chong, Hwei Chi

doi:10.3899/jrheum.150724

Cited by 18 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, since strong statistically significant predictors were found, we can still draw important conclusions about how changes in the predictor values are associated with changes in the outcome. Those values are consistent with other studies attempting to explain the variability in outcome of TKR 13 , 22 , 23 and also with other well-known prediction tools such as QRISK and Framingham score that explain around 30% of the variability in outcome 37 .…”

Section: Discussionsupporting

confidence: 91%

Development and validation of a clinical prediction model for patient-reported pain and function after primary total knee replacement surgery

Sanchez-Santos

Garriga

Judge

et al. 2018

Sci Rep

View full text Add to dashboard Cite

To develop and validate a clinical prediction model of patient-reported pain and function after undergoing total knee replacement (TKR). We used data of 1,649 patients from the Knee Arthroplasty Trial who received primary TKR across 34 centres in the UK. The external validation included 595 patients from Southampton University Hospital, and Nuffield Orthopaedic Centre (Oxford). The outcome was the Oxford Knee Score (OKS) 12-month after TKR. Pre-operative predictors including patient characteristics and clinical factors were considered. Bootstrap backward linear regression analysis was used. Low pre-operative OKS, living in poor areas, high body mass index, and patient-reported anxiety or depression were associated with worse outcome. The clinical factors associated with worse outcome were worse pre-operative physical status, presence of other conditions affecting mobility and previous knee arthroscopy. Presence of fixed flexion deformity and an absent or damaged pre-operative anterior cruciate ligament (compared with intact) were associated with better outcome. Discrimination and calibration statistics were satisfactory. External validation predicted 21.1% of the variance of outcome. This is the first clinical prediction model for predicting self-reported pain and function 12 months after TKR to be externally validated. It will help to inform to patients regarding expectations of the outcome after knee replacement surgery.

show abstract

Section: Discussionsupporting

confidence: 91%

Development and validation of a clinical prediction model for patient-reported pain and function after primary total knee replacement surgery

Sanchez-Santos

Garriga

Judge

et al. 2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The model is based on a predictor set that has limited overlap with the predictors used in the present study, and used data on postoperative recovery in addition to preoperative measurements. The predictive performance of their nomogram seems to be quite comparable to our model on walking limitations, with a reported c-statistic of 0.71 [11].…”

Section: Discussionsupporting

confidence: 55%

“…For effective expectation management, a prediction tool should ideally provide specific information on the most likely outcome on pain and function for an individual patient [6]. Pua et al [11] constructed a model on risk for walking limitations after TKA. In contrast to the models described in the previous paragraph that predict outcome in general, the model by Pua et al is the only predictive model for a specific functional outcome parameter available in literature.…”

Section: Discussionmentioning

confidence: 99%

Development of Preoperative Prediction Models for Pain and Functional Outcome After Total Knee Arthroplasty Using The Dutch Arthroplasty Register Data

Tolk

Waarsing

Steenbergen

et al. 2020

The Journal of Arthroplasty

View full text Add to dashboard Cite

Background: One of the main determinants of treatment satisfaction after total knee arthroplasty (TKA) is the fulfillment of preoperative expectations. For optimal expectation management, it is useful to accurately predict the treatment result. Multiple patient factors registered in the Dutch Arthroplasty Register (LROI) can potentially be utilized to estimate the most likely treatment result. The aim of the present study is to create and validate models that predict residual symptoms for patients undergoing primary TKA for knee osteoarthritis. Methods: Data were extracted from the LROI of all TKA patients who had preoperative and postoperative patient-reported outcome measures registered. Multivariable logistic regression analyses were performed to construct predictive algorithms for satisfaction, treatment success, and residual symptoms concerning pain at rest and during activity, sit-to-stand movement, stair negotiation, walking, performance of activities of daily living, kneeling, and squatting. We assessed predictive performance by examining measures of calibration and discrimination. Results: Data of 7071 patients could be included for data analysis. Residual complaints on kneeling (female 72%/male 59%) and squatting (female 71%/male 56%) were reported most frequently, and least residual complaints were scored for walking (female 16%/male 12%) and pain at rest (female 18%/male 14%). The predictive algorithms were presented as clinical calculators that present the probability of residual symptoms for an individual patient. The models for residual symptoms concerning sit-to-stand movement, stair negotiation, walking, activities of daily living, and treatment success showed acceptable discriminative values (area under the curve 0.68-0.74). The algorithms for residual complaints regarding kneeling, squatting, pain, and satisfaction showed less favorable results (area under the curve 0.58-0.64). The calibration curves showed adequate calibration for most of the models. Conclusion: A considerable proportion of patients have residual complaints after TKA. The present study showed that demographic and patient-reported outcome measure data collected in the LROI can be used to predict the probability of residual symptoms after TKA. The models developed in the present study predict the chance of residual symptoms for an individual patient on 10 specific items concerning treatment success, functional outcome, and pain relief. This prediction can be useful for individualized expectation management in patients planned for TKA.

show abstract

“…As far as we know, no study has combined self-perceived questionnaires with a wide range of physical tests in a large population. Furthermore, no other study has objectively measured gait parameters in an extensive group of patients and used it in a prediction model [ 19 ]. This information is clinically relevant to determine the overall status of a patient.…”

Section: Introductionmentioning

confidence: 99%

Limited predictive value of pre-surgical level of functioning for functioning at 3 and 12 months after TKA

Berghmans

Lenssen

Emans

et al. 2018

Knee Surg Sports Traumatol Arthrosc

View full text Add to dashboard Cite

Purpose A total knee arthroplasty (TKA) is a cost-effective option to relieve pain and improve knee function in patients suffering from osteoarthritis. However, results differ among patients. The predictive value of pre-surgically assessed factors on the level of functioning after 3 and 12 months was investigated in this study. Methods This study used an inception cohort design and a follow-up of 12 months. One hundred and fifty patients who were to receive a TKA were assessed pre-surgically with an International Classification of Functioning, Disability and Health (ICF) core assessment set: Knee Society Score (KSS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Short-Form 12 (SF12), Patient-Specific Function Scale (PSFS), range of motion (ROM), quadriceps and hamstring strength and gait parameters. The main outcome measure was WOMAC-Function at 3 and 12 months after surgery. Results Pre-surgical physical and mental health on the SF12 and functioning and stiffness on the WOMAC explained 23% of the variance in the level of functioning 3 months after surgery. Pre-surgical knee function measured with the KSS-Knee, and functioning as assessed by WOMAC-Function explained 16% of the variance of the level of functioning 12 months after surgery. Conclusions The results of this study show that better functioning before surgery, less knee stiffness and a better physical and mental health to some extent predict better functioning 3 months after surgery. This effect is less evident at 12 months. This study is clinically relevant since it provides benchmark data for health care providers who want to compare their individual patients. Level of evidence II.

show abstract

Development of a Prediction Model to Estimate the Risk of Walking Limitations in Patients with Total Knee Arthroplasty

Abstract: A small but sizable proportion of patients with TKA had persistent mobility limitations. Our prediction model may help to risk-stratify patients, and external validation is required before the model can be used in clinical practice.

Cited by 18 publications

References 33 publications

Development and validation of a clinical prediction model for patient-reported pain and function after primary total knee replacement surgery

Development and validation of a clinical prediction model for patient-reported pain and function after primary total knee replacement surgery

Development of Preoperative Prediction Models for Pain and Functional Outcome After Total Knee Arthroplasty Using The Dutch Arthroplasty Register Data

Limited predictive value of pre-surgical level of functioning for functioning at 3 and 12 months after TKA

Contact Info

Product

Resources

About