RT Journal Article
SR Electronic
T1 Using genetics to differentiate patients with similar symptoms: application to inflammatory arthritis in the rheumatology outpatient clinic
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 813220
DO 10.1101/813220
A1 Rachel Knevel
A1 Saskia le Cessie
A1 Chikashi C. Terao
A1 Kamil Slowikowski
A1 Jing Cui
A1 Tom W.J. Huizinga
A1 Karen H. Costenbader
A1 Katherine P. Liao
A1 Elizabeth W. Karlson
A1 Soumya Raychaudhuri
YR 2019
UL http://biorxiv.org/content/early/2019/10/22/813220.abstract
AB Slow developing complex diseases are a clinical diagnostic challenge. Since genetic information is increasingly available prior to a patient’s first visit to a clinic, it might improve diagnostic accuracy. We aimed to devise a method to convert genetic information into simple probabilities discriminating between multiple diagnoses in patients presenting with inflammatory arthritis.We developed G-Prob, which calculates for each patient the genetic probability for each of multiple possible diseases. We tested this for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis and gout). After validating in simulated data, we tested G-Prob in biobank cohorts in which genetic data were linked to electronic medical records: - 1,200 patients identified by ICD-codes within the eMERGE database (n= 52,623);- 245 patients identified through ICD codes and review of medical records within the Partners Biobank (n=12,604);- 243 patients selected prospectively with final diagnoses by medical record review within the Partners Biobank (n=12,604). The calibration of G-Prob with the disease status was high (with regression coefficients ranging from 0.90-1.08 (ideal would be 1.00) in all cohorts. G-Prob’s discriminative ability was high in all cohorts with pooled Area Under the Curve (AUC)=0.69 [95%CI 0.67-0.71], 0.81 [95%CI 0.76-0.84] and 0.84 [95%CI 0.81-0.86]. For all patients, at least one disease could be ruled out, and in 45% of patients a most likely diagnosis could be identified with an overall 64% positive predictive value. In 35% of instances the clinician’s initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease prediction which improved to 51% (P<0.0001) by adding G-Prob genetic data.In conclusion, by converting genotypes into an interpretable probability value for five different inflammatory arthritides, we can better discriminate and diagnose rheumatic diseases. Genotypes available prior to a clinical visit could be considered part of patients’ medical history and potentially used to improve precision and diagnostic efficiency in clinical practice.