Using gold standard patient-reported outcome measures in clinical practice -- a new approach to facilitate their use (original) (raw)

Patient-Reported Outcome Measures (PROMs) are self-administrated questionnaires that are used to assess a patient's health state, quality of life, and functional status associated with their health condition without the interpretation of the physician or anyone else [1,2]. There are growing efforts to shift from using PROMs in health research to implementing them in clinical practice [2-4]. Integrating PROMs in clinical practice can serve the entire health care system, including patients, care providers, insurers, and government regulators, and will enhance high-quality clinical care and improve shared decision-making processes [1,5,6]. From a patient's point of view, this will help to quantify health status, monitor changes over time, help to set up expectations, and increase patient engagement [5,7]. PROMs in Musculoskeletal (MSK) conditions are essential to facilitate patient-clinician communication and improve the shared decision-making process. Adding assessments from the patient's perspective provides a patient centerd approach that will help to assess disease severity as well as the effectiveness of treatments [2,8,9]. There are some commonly used diseasespecific PROMs in MSK conditions, amongst them are the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Short Form-36 (SF-36) [10,11]. Implementing PROMs in clinical practice is still a challenge [10]. The current integration of PROMs in clinical practice is minimal as they are considered complex and resource-intensive [4,12,13]. In essence, there are several barriers to real-life implementation and the adoption of PROMs in clinical practice. Amongst these are skepticism about the validity and potential Abstract Introduction: To analyze two gold-standard Patient-Reported Outcome Measures (PROMs) in knee OA (WOMAC and SF-36) and determine which questions are the most reflective of the overall score. Methods: This was a retrospective study on 4,983 patients with primary knee pain. Patients had WOMAC and SF-36 at two-time points, pre-treatment and after three months of treatment. A decision tree classifier supported with a linear mix model regression was applied to determine, identify, and categorize the most influential questions that determine the overall score in each of the questionnaires. Result: For SF-36, the most influential items were Q22 (39%), Q32 (24%), Q11 (19%), Q25 (19%). For WOMAC, the most influential predictors were Q14 (39%), Q10 (24%) and Q15 (21%). A significant improvement in WOMAC and SF-36 was seen after three months of treatment (p<0.01). For SF-36, the main predictor items were Q11, Q22 and Q32, Regression model R2=0.841, p<0.01, t[55.62]=0.001, Beta for Q22=0.409, Q32=0.352, Q11=0.278. For WOMAC, the main predictor items were Q10 and Q15, Regression model R2=0.930, p<0.01, t[35.4]=0.001, Beta for Q15=0.548, Q10=0.4639. Conclusion: Two questions from the WOMAC questionnaire predicts 93% of the overall score and four questions form the SF-36 predict 84%. The creation of a clinically meaningful assessment tool based on larger scientifically validated PROMs will help to facilitate its use by clinicians and acceptance by patients in clinical practice.