Cloudy with a Chance of Pain
Dr. Rachel Knevel
Department of Rheumatology, Leiden University Medical Center, The Netherlands
Titel of Presentation: G-Prob
Biosketch: She is an ass. prof at the Rheumatology Department of the LUMC working as clinician and scientist. During her PhD she studied the genetics of phenotypic characteristics of rheumatoid arthritis. To further specialize in bioinformatics, she was a research fellow at the Raychaudhuri lab at Brigham and Women’s Hospital, Harvard Medical school from 2016-2018. After that, she rejoined prof. Tom Huizinga’s Department of Rheumatology at the Leiden University Medical Centre, where she initiated her research group focusing on early disease differentiation and the identification of homogeneous subsets in patients with rheumatic disease by integrating the “big data” of electronic health records with genetic information.
Abstract
It is challenging to quickly diagnose slowly progressing diseases. To prioritize multiple related diagnoses, we developed G-PROB (Genetic Probability tool) to calculate the probability of different diseases for a patient using genetic risk scores. We tested G-PROB for inflammatory arthritis–causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis, and gout). After validating on simulated data, we tested G-PROB in three cohorts: 1211 patients identified by International Classification of Diseases (ICD) codes within the eMERGE database, 245 patients identified through ICD codes and medical record review within the Partners Biobank, and 243 patients first presenting with unexplained inflammatory arthritis and with final diagnoses by record review within the Partners Biobank. Calibration of G-probabilities with disease status was high, with regression coefficients from 0.90 to 1.08 (1.00 is ideal). G-probabilities discriminated true diagnoses across the three cohorts with pooled areas under the curve (95% CI) of 0.69 (0.67 to 0.71), 0.81 (0.76 to 0.84), and 0.84 (0.81 to 0.86), respectively. For all patients, at least one disease could be ruled out, and in 45% of patients, a likely diagnosis was identified with a 64% positive predictive value. In 35% of cases, the clinician’s initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease, which improved to 51% (P < 0.0001) after adding G-probabilities. Converting genotype information before a clinical visit into an interpretable probability value for five different inflammatory arthritides could potentially be used to improve the diagnostic efficiency of rheumatic diseases in clinical practice.
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