Patient dissatisfaction following total knee arthroplasty: external validation of a new prediction model

Zabawa, Luke; Li, Keren; Chmell, Samuel J.

doi:10.1007/s00590-019-02375-w

Cited by 17 publications

(17 citation statements)

References 16 publications

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“…(2020) [ 18 ] Moderate Low Moderate Low Moderate Moderate Moderate Moderate Zabawa et al. (2019) [ 14 ] Moderate Moderate Moderate Low Low Moderate Low Moderate Kunze et al. (2019) [ 39 ] Low Low Moderate Low Low Moderate Low Moderate Clement et al.…”

Section: Resultsmentioning

confidence: 99%

“…Patients with higher levels of pain before TKA surgery had lower postoperative functional scores. However, improvements in pain scores, functional knee scores, and patient satisfaction were greater in this group [ [13] , [14] , [15] , [16] ]. Huijbregts et al.…”

Section: Resultsmentioning

confidence: 99%

“…Preoperative PROM scores, particularly knee scores, were strongly correlated with TKA outcome [ 5 , 14 , 15 , [18] , [19] , [20] , [21] ]. Maratt et al.…”

Section: Resultsmentioning

confidence: 99%

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Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

Batailler

Lording²,

Massari

et al. 2021

Arthroplasty Today

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Background: Predictive modeling promises to improve our understanding of what variables influence patient satisfaction after total knee arthroplasty (TKA). The purpose of this article was to systematically review the relevant literature using predictive models of clinical outcomes after TKA. The aim was to identify the predictor strategies used for systematic data collection with the highest likelihood of success in predicting clinical outcomes. Methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol systematic review was conducted using 3 databases (MEDLINE, EMBASE, and PubMed) to identify all clinical studies that had used predictive models or that assessed predictive features for outcomes after TKA between 1996 and 2020. The ROBINS-I tool was used to evaluate the quality of the studies and the risk of bias. Results: A total of 75 studies were identified of which 48 met our inclusion criteria. Preoperative predictive factors strongly associated with postoperative clinical outcomes were knee pain, knee-specific Patient-Reported Outcome Measure (PROM) scores, and mental health scores. Demographic characteristics, pre-existing comorbidities, and knee alignment had an inconsistent association with outcomes. The outcome measures that correlated best with the predictive models were improvement of PROM scores, pain scores, and patient satisfaction. Conclusions: Several algorithms, based on PROM improvement, patient satisfaction, or pain after TKA, have been developed to improve decision-making regarding both indications for surgery and surgical strategy. Functional features such as preoperative pain and PROM scores were highly predictive for clinical outcomes after TKA. Some variables such as demographics data or knee alignment were less strongly correlated with TKA outcomes. Level of evidence: Systematic review e Level III.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

Batailler

Lording²,

Massari

et al. 2021

Arthroplasty Today

View full text Add to dashboard Cite

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“…To the best our knowledge, the present study is the first prediction system based on several postoperative lower-extremity angles of alignment to predict objective clinical outcomes. Many other score systems predicting clinical outcomes focused on patients’ satisfactory instead of the objective outcomes, and others did not include all radiographic alignment features [ 47 , 48 ]. The present study provides a new way of thinking to evaluate the quality of total knee arthroplasty.…”

Section: Discussionmentioning

confidence: 99%

How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment

Chen

Deng

et al. 2020

BMC Musculoskelet Disord

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Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good (R 2 = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA.

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“…A "forgotten knee" is the common goal of patients undergoing TKA and surgeons. In fact, many patients do not obtain a natural knee joint, which leads to much dissatisfaction [10]. As far as we know, patients undergoing TKA are mainly characterized by the loss of range of motion (ROM) and fine motor of the knee compared with healthy people.…”

Section: Introductionmentioning

confidence: 99%

Gait Analysis after Total Knee Arthroplasty Assisted by 3D-Printed Personalized Guide

Sun

Zhang

Peng

et al. 2020

BioMed Research International

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Background. With the development of three-dimensional printing (3DP) technology, the patient-specific instrumentation (PSI) has been widely applied in total knee arthroplasty (TKA). The purpose of this study was to compare the gait parameters of patients with 3DP personalized guide-assisted and standard TKA. Methods. Retrospective analysis of the advanced knee OA cases in our hospital between June 2017 and June 2018 was conducted. 30 cases received 3DP personalized guide-assisted TKA (group A), and 60 patients who underwent standard TKA during the same period were in group B and group C according to the computed tomography (CT) measurement results, each with 30 cases. Hip–knee–ankle angle (HKA), patella transverse axis-femoral transepicondylar axis angle (PFA), and gait parameters were statistically analyzed. The function was assessed by Hospital for Specific Surgery (HSS) and Knee Society Score (KSS). Results. The mean follow-up period was 12.3 months in the three groups. The knee max flexion angle at the swing phase of group A was larger than group B and group C (P<0.05), there was no statistically significant difference in other gait parameters. The mean PFA of group A was smaller than that of group B and group C (P<0.05). While, the HKA, HSS, and KSS scores of three groups showed no significant difference. Conclusion. 3DP personalized guide technology could improve the gait parameters after surgery, specifically reflected in the knee max flexion angle at the swing phase. It could also assist in the reconstruction of more accurate patellar tracking and rotational alignment in TKA, avoiding the occurrence of insufficient or excessive extorsion.

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Patient dissatisfaction following total knee arthroplasty: external validation of a new prediction model

Cited by 17 publications

References 16 publications

Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment

Gait Analysis after Total Knee Arthroplasty Assisted by 3D-Printed Personalized Guide

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