ArticleThe British journal of general practice : the journal of the Royal College of General Practitioners2015
A clinical prediction rule for meniscal tears in primary care: development and internal validation using a multicentre study.
Article in The British journal of general practice : the journal of the Royal College of General Practitioners, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 14 citations in OpenAlex.
- Digital Health Applications to Establish a Remote Diagnosis of Orthopedic Knee Disorders: Scoping Review.Journal of medical Internet research · 2023Article
- Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Article
- Anamnestic prediction of bucket handle compared to other tear patterns of the medial meniscus in stable knees.Acta orthopaedica et traumatologica turcica · 2016Article
- Severity of Carpal Tunnel Syndrome and Diagnostic Accuracy of Hand and Body Anthropometric Measures.PloS one · 2016Article
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4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn primary care, meniscal tears are difficult to detect. A quick and easy clinical prediction rule based on patient history and a single meniscal test may help physicians to identify high-risk patients for referral for magnetic resonance imaging (MRI).
aimThe study objective was to develop and internally validate a clinical prediction rule (CPR) for the detection of meniscal tears in primary care. DESIGN AND
settingIn a cross-sectional multicentre study, 121 participants from primary care were included if they were aged 18-65 years with knee complaints that existed for <6 months, and who were suspected to suffer from a meniscal tear.
methodOne diagnostic physical meniscal test and 14 clinical variables were considered to be predictors of MRI outcome. Using known predictors for the presence of meniscal tears, a 'quick and easy' CPR was derived.
resultsThe final CPR included the variables sex, age, weight-bearing during trauma, performing sports, effusion, warmth, discolouration, and Deep Squat test. The final model had an AUC of 0.76 (95% CI = 0.72 to 0.80). A cut-point of 150 points yielded an overall sensitivity of 86.1% and a specificity of 45.5%. For this cut-point, the positive predictive value was 55.0%, and the negative predictive value was 81.1%. A scoring system was provided including the corresponding predicted probabilities for a meniscal tear.
conclusionThe CPR improved the detection of meniscal tears in primary care. Further evaluation of the CPR in new primary care patients is needed, however, to assess its usefulness.
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