Knie-artrose

Fietsen verstandig?

Feit

Bij hardlopen wordt de knie belast met 6x uw lichaamsgewicht bij elke stap

Máxima Medisch Centrum

Value-Based Health Care

ESSKA Accredited Teacher

Orthopedie Groot Eindhoven

Goede uitleg is essentieel voor juiste keuze behandeling

Meniscus-scheur

Voorste kruisbandreconstructie

Achterste kruisbandletsel

Zorg op maat

YouTube kanaal dr RPA Janssen

Internationaal wetenschappelijk onderzoek

Kruisbandoperaties knie

RPA Janssen MD PhD

Kruisbandoperaties

Waardegedreven zorg

Last van knie-artrose?

Spreekuur / Policlinique / Outpatient clinic

Nederlandse Verenging voor Arthroscopie

Máxima Medisch Centrum

Kruisbandoperaties bij kinderen

Veel pijn bij ernstige knie-artrose?

Samen met uw arts gekozen voor een knie-operatie?

Eenzijdige knie-artrose knie en actief leven?

Máxima Medisch Centrum Eindhoven-Veldhoven

Kwaliteit van leven

Samen kiezen voor de beste behandeling

Sporten met plezier

Oplossingen voor de lange termijn

Associate Professor Knee Reconstruction

Fact

Kneecap pain is a regular occurrence in fitness knee rehabilitation

Knee osteoarthritis

Cycling wise?

Fact

When running, the knee is loaded with 6x your body weight with each step

Nieuwe publicatie J Arthroplasty

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

Tolk JJ, Waarsing JEH, Janssen RPA, van Steenbergen LN, Bierma-Zeinstra SMA, Reijman M
J Arthroplasty. 2019 Oct 18. pii: S0883-5403(19)30944-1. doi: 10.1016/j.arth.2019.10.010. [Epub ahead of print]

Abstract

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.