Ab1672-Pare Developing an Online Tool to Identify Polymyalgia Rheumatica (Pmr) Symptoms Among Undiagnosed Individuals

Background: Accurate and timely diagnosis of polymyalgia rheumatica (PMR) poses several challenges, including diagnostic challenges that result from under- and mis-recognition of symptoms. Timely diagnosis is crucial for appropriate care and to achieve better health outcomes. A team of clinicians, researchers, and patient representatives developed an algorithm for the identification of symptoms indicative of PMR via an online diagnosis accelerator tool called PainSpotTM. Objectives: To develop an online tool that identifies symptoms indicative of PMR and to offer educational content to people with symptoms suggestive of PMR about the condition, its symptoms and treatment, and how to access appropriate care to receive a formal evaluation. Methods: We assembled a multistakeholder team of rheumatologists, researchers, and non-profit patient representatives. Overall objectives were defined and the team met 3 times over the span of 3 months to develop and refine questions based on a background literature review, clinical experience, and patient perspectives of their pre-diagnosis experience. The questions were integrated into the existing PainSpotTM algorithm, which accounts for over 40 potential inflammatory and musculoskeletal conditions. Based on how users respond to questions about their neck, shoulder, or hip pain, the PainSpotTM tool generates 2-3 potential diagnoses ranging from high likelihood, moderate likelihood, and low likelihood. After the algorithm changes were established, prototypes of the neck, shoulder, and hip pathways were shared with patients in the PatientSpotTM research registry with diagnosed PMR to gain feedback on the functionality of the tool based on their pre-diagnosis experiences. After completing the tool via the prototype, the patient evaluators were asked if they received a result of PMR at the end of the tool and if the questions aligned with their pre-diagnostic symptoms. Results: A total of 10 new questions and 3 existing questions were finalized for the identification of PMR symptoms. Through answer weighting and skip logic, the questions determine a user's likelihood of PMR. The final list of questions is in Table 1. A total of 4 people with PMR tested the prototype. As anticipated, three out of the 4 user testers received a result of PMR while testing the quiz, and the one who did not reported also having rheumatoid arthritis, which was a disqualifying element due to the overlap of symptoms between the two conditions. General feedback from participants was that the questions matched users' PMR symptoms "very well". A screenshot from the PainSpot tool can be found in Image 1. Conclusion: Polymyalgia rheumatica is a challenging disease to diagnose; however, an online tool (https://PainSpot.com) developed and refined through clinical input, patient experience, a multi-stakeholder group, and an iterative review process may assist in developing diagnostic algorithmic tools for difficult-to-diagnose diseases. This digital tool may help accelerate diagnosis of PMR or mimicking conditions among people suffering from undiagnosed symptoms, allowing them to seek proper care and treatment and leading to better health outcomes. The tool is now being deployed to a wider population to evaluate the proportion of people who are diagnosed with PMR after receiving a PMR result in PainSpot. REFERENCES: NIL. Table 1. Final list of questions that contribute to a result of polymyalgia rheumatica Bolded questions are weighted, meaning they contribute more to the likelihood of a polymyalgia rheumatica result. ŦQuestions without an answer of interest contribute to the skip logic of the algorithm but the answer does not impact the result. Acknowledgements: This study was done with sponsorship support from Sanofi. Disclosure of Interests: None declared.