P055 Giant Cell Arteritis Diagnosis: Metadiagnosis by robust Bayesian estimate of strength of belief

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Abstract Background/Aims Metadiagnosis expresses diagnostic belief as: (i) a numeric probability, (ii) degree of confidence in the probability estimate and (iii) defines the derived information as decision thresholds. We use this approach to stratify suspected GCA patients, by disease probability, into three classes. (a) Likely to have GCA, (b) Unlikely to have GCA, and (c) Uncertain GCA status. Methods We developed a Bayesian algorithm that computes a patient’s disease probability based on their signs, symptoms and reference-standard tests. This algorithm forms the computational engine of the first iteration of an online APP. The computational engine incorporated statistical weights (likelihood ratios) from a meta-analysis of 68 unique studies (14 037 unique patients with suspected GCA). The algorithm requires the specification of a disease prevalence. This is the “prior” probability any patient has GCA. We then update this probability incrementally with each additional item of information elicited from the questionnaire proforma (e.g. female? headache? visual abnormality? …) by incremental evidence aggregation. Finally, the outcome of the patient's reference standard test is incorporated (e.g. positive, negative, indeterminate) and a final posterior GCA probability is computed for each patient. Results Quantitative GCA risk estimate for each individual patient, and how that was altered by the result of reference standard test, produces three distinct groups: a) Low probability group - estimated risk of GCA is significantly lower than the prevalence in the cohort. The reference standard test in this group was uniformly negative.b) intermediate probability group - estimated risk of GCA is around the same as the prevalence in the cohort. The reference standard test (TAB) is almost always negative in this group, but not exclusively.c) high probability group - estimated risk of GCA far exceeds the base or population risk level, and the reference standard test (TAB) is typically confirmatory in this group. Conclusion Applying Bayesian algorithmic metadiagnosis, based on established values of how indicators of disease impact on diagnosis, allows numerical disease prediction and non-mathematically communicating the precision of diagnosis may enhance informed patient choice leading to personalised, targeted intervention. An inbuilt artificial neural network (a deep learning method feedback-loop) trains the algorithm to identify the value of all relevant indicators and so improves predictive accuracy. We plan further studies to establish how this approach could impact on patient well-being and resource utilisation. Disclosure L. Clearkin: None.