Initial derivation of diagnostic clusters combining history elements and physical examination tests for symptomatic knee osteoarthritis. (21st May 2018) - Décary, Simon; Feldman, Debbie; Frémont, Pierre; Pelletier, Jean‐Pierre; Martel‐Pelletier, Johanne; Fallaha, Michel; Pelletier, Bruno; Belzile, Sylvain; Sylvestre, Marie‐Pierre; Vendittoli, Pascal‐André; Desmeules, François
Abstract: Introduction: The aim of the present study was to assess the validity of clusters combining history elements and physical examination tests to diagnose symptomatic knee osteoarthritis (SOA) compared with other knee disorders. Methods: This was a prospective diagnostic accuracy study, in which 279 consecutive patients consulting for a knee complaint were assessed. History elements and standardized physical examination tests were obtained independently by a physiotherapist and compared with an expert physician's composite diagnosis, including clinical examination and imaging. Recursive partitioning was used to develop diagnostic clusters for SOA. Diagnostic accuracy measures were calculated, including sensitivity, specificity, and positive and negative likelihood ratios (LR+/−), with associated 95% confidence intervals (CIs). Results: A total of 129 patients had a diagnosis of SOA (46.2%). Most cases (76%) had combined tibiofemoral and patellofemoral knee OA and 63% had radiological Kellgren–Lawrence grades of 2 or 3. Different combinations of history elements and physical examination tests were used in clusters accurately to discriminate SOA from other knee disorders. These included age of patients, body mass index, presence of valgus/varus knee misalignment, palpable knee crepitus and limited passive knee extension. Two clusters to rule in SOA reached an LR+ of 13.6 (95% CI 6.5 to 28.4) and three clusters to rule out SOA reached an LR– of 0.11 (95% CI 0.06 to 0.20). Discussion: Diagnostic clusters combining history elements and physical examination tests were able to support the differential diagnosis of SOA compared with various knee disorders without relying systematically on imaging. This could support primary care clinicians' role in the efficient management of these patients.