Knevel Rachel, le Cessie Saskia, Terao Chikashi C, Slowikowski Kamil, Cui Jing, Huizinga Tom W J, Costenbader Karen H, Liao Katherine P, Karlson Elizabeth W, Raychaudhuri Soumya
Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, Netherlands.
Sci Transl Med. 2020 May 27;12(545). doi: 10.1126/scitranslmed.aay1548.
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% ( < 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.
快速诊断进展缓慢的疾病具有挑战性。为了对多个相关诊断进行优先级排序，我们开发了G-PROB（遗传概率工具），以使用遗传风险评分计算患者患不同疾病的概率。我们对导致炎症性关节炎的疾病（类风湿性关节炎、系统性红斑狼疮、脊柱关节炎、银屑病关节炎和痛风）测试了G-PROB。在对模拟数据进行验证后，我们在三个队列中测试了G-PROB：通过电子医疗记录与基因组学整合研究（eMERGE）数据库中的国际疾病分类（ICD）代码识别出的1211名患者、通过ICD代码和合作伙伴生物样本库中的病历审查识别出的245名患者，以及首次出现不明原因炎症性关节炎并通过合作伙伴生物样本库中的病历审查获得最终诊断的243名患者。G概率与疾病状态的校准度很高，回归系数在0.90至1.08之间（理想值为1.00）。G概率在三个队列中区分了真正的诊断，合并曲线下面积（95%置信区间）分别为0.69（0.67至0.71）、0.81（0.76至0.84）和0.84（0.81至0.86）。对于所有患者，至少可以排除一种疾病，在45%的患者中，确定了一个可能的诊断，阳性预测值为64%。在35%的病例中，临床医生的初步诊断是错误的。初始临床诊断解释了最终疾病39%的变异性，在加入G概率后提高到51%（<0.0001）。在临床就诊前将基因型信息转换为五种不同炎症性关节炎的可解释概率值，可能有助于提高临床实践中风湿性疾病的诊断效率。
