Effectiveness of progressive tendon-loading exercise therapy in patients with patellar tendinopathy:a randomised clinical trial

Objective To compare the effectiveness of progressive tendon-loading exercises (PTLE) with eccentric exercise therapy (EET) in patients with patellar tendinopathy (PT). Methods In a stratified, investigator-blinded, block-randomised trial, 76 patients with clinically diagnosed and ultrasound-confirmed PT were randomly assigned in a 1:1 ratio to receive either PTLE or EET. The primary end point was clinical outcome after 24 weeks following an intention-to-treat analysis, as assessed with the validated Victorian Institute of Sports Assessment for patellar tendons (VISA-P) questionnaire measuring pain, function and ability to play sports. Secondary outcomes included the return to sports rate, subjective patient satisfaction and exercise adherence. Results Patients were randomised between January 2017 and July 2019. The intention-to-treat population (mean age, 24 years, SD 4); 58 (76%) male) consisted of patients with mostly chronic PT (median symptom duration 2 years). Most patients (82%) underwent prior treatment for PT but failed to recover fully. 38 patients were randomised to the PTLE group and 38 patients to the EET group. The improvement in VISA-P score was significantly better for PTLE than for EET after 24 weeks (28 vs 18 points, adjusted mean between-group difference, 9 (95% CI 1 to 16); p=0.023). There was a trend towards a higher return to sports rate in the PTLE group (43% vs 27%, p=0.13). No significant between-group difference was found for subjective patient satisfaction (81% vs 83%, p=0.54) and exercise adherence between the PTLE group and EET group after 24 weeks (40% vs 49%, p=0.33). Conclusions In patients with PT, PTLE resulted in a significantly better clinical outcome after 24 weeks than EET. PTLE are superior to EET and are therefore recommended as initial conservative treatment for PT

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

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

Morphological variants to predict outcome of avascular necrosis in developmental dysplasia of the hip

Aims the most important complication of treatment of developmental dysplasia of the hip (ddh) is avascular necrosis (aVn) of the femoral head, which can result in proximal femoral growth disturbances leading to pain, dysfunction, and eventually to early onset osteoarthritis. in this study, we aimed to identify morphological variants in hip joint development that are predictive of a poor outcome. Methods We retrospectively reviewed all patients who developed aVn after ddh treatment, either by closed and/or open reduction, at a single institution between 1984 and 2007 with a minimal follow-up of eight years. standard pelvis radiographs obtained at ages one, two, three, five, and eight years, and at latest follow-up were retrieved. The Bucholz-Ogden classification was used to determine the type of AVN on all radiographs. Poor outcome was defined by Severin classification grade 3 or above on the latest follow-up radiographs and/or the need for secondary surgery. With statistical shape modelling, we identified the different shape variants of the hip at each age. logistic regression analysis was used to associate the different modes or shape variants with poor outcome. results In all, 135 patients with AVN were identified, with a minimum of eight years of follow-up. Mean age at time of surgery was 7.0 months (SD 0.45), and mean follow-up was 13.3 years (SD 3.7). Overall, 46% had AVN type 1 while 54% type 2 or higher. More than half of the patients (52.6%) had a poor outcome. We found 11 shape variants that were significantly associated with a poor outcome. these shape variants were predominantly linked to aVn type 2 or higher. Conclusion Specific morphological characteristics on pelvis radiographs of AVN hips were predictive for poor outcome, at a very young age. there was an overall stronger association to Bucholz-Ogden types 2-3-4 with the exception of two modes at age two and five years, linked to aVn type 1

Study protocol ROTATE-trial: anterior cruciate ligament rupture, the influence of a treatment algorithm and shared decision making on clinical outcome- a cluster randomized controlled trial

Background: Anterior cruciate ligament (ACL) rupture is a very common knee injury in the sport active population. There is much debate on which treatment (operative or non-operative) is best for the individual patient. In order to give a more personalized recommendation we aim to evaluate the effectiveness and cost-effectiveness of a treatment algorithm for patients with a complete primary ACL rupture. Methods: The ROTATE-trial is a multicenter, open-labeled cluster randomized controlled trial with superiority design. Randomization will take place on hospital level (n = 10). Patients must meet all the following criteria: aged 18 year or older, with a complete primary ACL rupture (confirmed by MRI and physical examination) and maximum of 6 weeks of non-operative treatment. Exclusion criteria consists of multi ligament trauma indicated for surgical intervention, presence of another disorder that affects the activity level of the lower limb, pregnancy, and insufficient command of the Dutch language. The intervention to be investigated will be an adjusted treatment decision strategy, including an advice from our treatment algorithm. Patient reported outcomes will be conducted at baseline, 3, 6, 12 and 24 months. Physical examination of the knee at baseline, 12 and 24 months. Primary outcome will be function of the knee measured by the International Knee Documentation Committee (IKDC) questionnaire. Secondary outcomes are, among others, the Tegner activity score, the Knee injury and Osteoarthritis Outcome Score (KOOS) and the 9-item Shared Decision Making Questionnaire (SDM-Q-9). Healthcare use, productivity and satisfaction with ((non-)operative) care are also measured by means of questionnaires. In total 230 patients will be included, resulting in 23 patients per hospital. Discussion: The ROTATE study aims to evaluate the effectiveness and cost-effectiveness of a treatment algorithm for patients with a complete primary ACL rupture compared to current used treatment strategy. Using a treatment algorithm might give the much-wanted personalized treatment recommendation. Trial registration: This study is approved by the Medical Research Ethics Committee of Erasmus Medical Center in Rotterdam and prospectively registered at the Dutch Trial Registry on May 13th, 2020. Registration number: NL8637. Keywords: Anterior cruciate ligament rupture; Cost-effectiveness; Shared decision making; Treatment algorithm

Development of preoperative prediction models for pain and functional outcome after total knee arthroplasty using the Dutch arthroplasty register data

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

Development of preoperative prediction models for pain and functional outcome after total knee arthroplasty using the Dutch arthroplasty register data

\u3cp\u3eBACKGROUND: 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.\u3c/p\u3e\u3cp\u3eMETHODS: 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.\u3c/p\u3e\u3cp\u3eRESULTS: 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.\u3c/p\u3e\u3cp\u3eCONCLUSION: 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.\u3c/p\u3

Outcome prediction for treatment of knee osteoarthritis with a total knee arthroplasty. Development and validation of a prediction model for pain and functional outcome using the Dutch arthroplasty register (LROI) data

Background One of the main determinants of treatment satisfaction after total knee arthroplasty (TKA) is the fulfilment of preoperative expectations. For optimal expectation management it is useful to be able accurately predict the treatment result. Multiple patient factors that are obtained for registration in the Dutch Arthroplasty Registry (LROI) are associated with the treatment result. Therefore, these factors can potentially be utilised to estimate the most likely outcome on pain and functional outcome for an individual patient\u3cbr/\u3e\u3cbr/\u3eObjectives The aim of the present study was to create and validate models that predict residual symptoms on 10 specific outcome parameters at 12-month follow-up for patients undergoing primary TKA for knee osteoarthritis.\u3cbr/\u3e\u3cbr/\u3eMethods Data was extracted from the LROI on TKA patients who had pre- and postoperative PROMs registered in the LROI registry. Multiple logistic regression analyses were performed to construct predictive algorithms for satisfaction, treatment success, and residual symptoms concerning pain in rest and during activity, sit-to-stand movement, stair negotiation, walking, performance of activities of daily living, kneeling and squatting. Models were developed for men and women separately. We assessed predictive performance by examining measures of calibration and discrimination.\u3cbr/\u3e\u3cbr/\u3eResults Data of 7071 patients could be included for data analysis. Residual complaints on kneeling (♀72%/ ♂59%) and squatting (♀71%/ ♂56%) were reported most frequently, and least residual complaints were scored for walking (♀16%/ ♂12%) and pain in rest (♀18%/ ♂14%). The predictive algorithms for residual symptoms concerning sit-to-stand movement, stair negotiation, walking, activities of daily living and treatment success showed acceptable discriminative values (AUC 0.68 – 0.74). The prediction models for residual complaints regarding kneeling, squatting, pain and satisfaction showed the least favourable results (AUC 0.58 – 0.64). The calibration curves showed adequate calibration for most of the models.\u3cbr/\u3e\u3cbr/\u3eConclusion A considerable proportion of patients has residual complaints after TKA. The present study showed that demographic and PROMs data collected for the LROI registry, can be used to predict the chance for residual symptoms after TKA. The predictive models that have been developed can be useful for individual expectation management in patients planned for TKA for knee osteoarthritis