The Added Value of Radiographs in Diagnosing Knee Osteoarthritis Is Similar for General Practitioners and Secondary Care Physicians; Data from the CHECK Early Osteoarthritis Cohort

Abstract

Objective: The purpose of this study was to evaluate the added value of radiographs for diagnosing knee osteoarthritis (KOA) by general practitioners (GPs) and secondary care physicians (SPs). Methods: Seventeen GPs and nineteen SPs were recruited to evaluate 1185 knees from the CHECK cohort (presenters with knee pain in primary care) for the presence of clinically relevant osteoarthritis (OA) during follow-up. Experts were required to make diagnoses independently, first based on clinical data only and then on clinical plus radiographic data, and to provide certainty scores (ranging from 1 to 100, where 1 was “certainly no OA” and 100 was “certainly OA”). Next, experts held consensus meetings to agree on the final diagnosis. With the final diagnosis as gold standard, diagnostic indicators were calculated (sensitivity, specificity, positive/negative predictive value, accuracy and positive/negative likelihood ratio) for all knees, as well as for clinically “certain” and “uncertain” knees, respectively. Student paired t-tests compared certainty scores. Results: Most diagnoses of GPs (86%) and SPs (82%) were “consistent” after assessment of radiographic data. Diagnostic indicators improved similarly for GPs and SPs after evaluating the radiographic data, but only improved relevantly in clinically “uncertain” knees. Radiographs added some certainty to “consistent” OA knees (GP 69 vs. 72, p < 0.001; SP 70 vs. 77, p < 0.001), but not to the consistent no OA knees (GP 21 vs. 22, p = 0.16; SP 20 vs. 21, p = 0.04). Conclusions: The added value of radiographs is similar for GP and SP, in terms of diagnostic accuracy and certainty. Radiographs appear to be redundant when clinicians are certain of their clinical diagnosis.

Evaluation of the Diagnostic Performance of American College of Rheumatology, EULAR, and National Institute for Health and Clinical Excellence Criteria Against Clinically Relevant Knee Osteoarthritis: Data From the CHECK Cohort

“…The details of the diagnostic process were presented in our previous study. 9 In brief, we invited both general practitioners and secondary care physicians to evaluate participants' longitudinal (5-, 8-, and 10-year follow-up) clinical and radiographic data. Clinical data consisted of demographics (including sex, age, racial background, marital status, menopausal status, educational level, chronic diseases, occupation, smoking status, and alcohol usage); measurement of body mass index; physical examinations (presence of knee pain, morning stiffness in knee, knee warmth, bony tenderness, crepitus, knee pain on extension and flexion, and range of motion); Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, function, and stiffness scores; numeric rating scale pain scores (pain level in the past week); and incidence of other diseases (quadriceps tendinitis, intraarticular fracture, Baker's cyst, ligament or meniscus damage, osteochondritis dissecans, plica syndrome, and septic arthritis).…”

“…The details of the diagnostic process were presented in our previous study 9 . In brief, we invited both general practitioners and secondary care physicians to evaluate participants’ longitudinal (5‐, 8‐, and 10‐year follow‐up) clinical and radiographic data.…”

A machine learning approach reveals features related to clinicians’ diagnosis of clinically relevant knee osteoarthritis

“…Despite the widespread use, a paucity of studies exists on which clinical features drive clinicians to make the OA diagnosis. Moreover, the focus in diagnosis-making could be different between primary and secondary care (given the different specialty knowledge) and between the circumstances with and without radiographs [15]; the features are preferably to be identified for each situation.…”

“…Here, we performed a post hoc analysis on the data from a previous task [15,18], in which general practitioners (GPs) and secondary care physicians (SPs) were recruited to evaluate patients' longitudinal medical data to diagnose whether clinically relevant knee OA was present. With the help of machine learning algorithms, the primary aim of this study was to identify the highly-ranked clinical features related to the diagnosis by GPs and SPs, in the situation with or without access to radiographs, respectively.…”

“…We stored clinical and radiographic data in special software (built in-house) for optimal presentation. Details of software training and diagnosis making process have been described in our previous studies [15,24]. A brief description of the diagnosis-making process is given below.…”