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Five Questions to Identify Patients With Osteoarthritis of the Knee

Authors :
Bernhard Springer
Wenzel Waldstein
Kilian Rueckl
Friedrich Boettner
Ulrich Bechler
Source :
The Journal of arthroplasty. 35(1)
Publication Year :
2019

Abstract

Background To treat the increasing number of patients with osteoarthritis (OA) of the knee, high-volume institutions rely on central referral services as first contact point. Depending on the grading of arthritis, patients will be referred to a nonoperative or operative care provider. The present study reports on a simple 5-step questionnaire to identify patients with OA (Kellgren/Lawrence [KL] grade ≥2) of the knee to improve efficiency of referrals. Methods We included 998 patients who contacted the physician referral service at the author’s institution complaining of knee pain and divided them into 2 groups. The study group included patients with an appointment and consisted of 646 patients (345 women [53.4%] and 301 men [46.6%]). X-rays of the knee were graded according to the KL classification system. The control group of patients who did not make an appointment consisted of 352 patients (187 women [53.1%] and 165 men [46.9%]). These patients were contacted to evaluate whether they had been diagnosed with OA of the knee since their initial call, to assure that the study group was not exposed to a selection bias. Results Logistic regression revealed 5 questions as significant predictors for OA of the knee (KL grade ≥2). When combining both groups, an 86.9% sensitivity, a 73.3% specificity, and an 84.3% overall accuracy were reached, when patients answered 3 or more questions positively. Conclusion The present study revealed a simple 5-step questionnaire to identify patients with OA of the knee. Implementation of the questionnaire has the potential to improve the accuracy of referral processes and streamline organization before the first appointment.

Details

ISSN :
15328406
Volume :
35
Issue :
1
Database :
OpenAIRE
Journal :
The Journal of arthroplasty
Accession number :
edsair.doi.dedup.....a9ff36f987a352c386ac0193a4afaad5