Using Polygenic Risk Scores to Aid Diagnosis of Patients With Early Inflammatory Arthritis : Results From the Norfolk Arthritis Register
© 2023 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology..
OBJECTIVE: There is growing evidence that genetic data are of benefit in the rheumatology outpatient setting by aiding early diagnosis. A genetic probability tool (G-PROB) has been developed to aid diagnosis has not yet been tested in a real-world setting. Our aim was to assess whether G-PROB could aid diagnosis in the rheumatology outpatient setting using data from the Norfolk Arthritis Register (NOAR), a prospective observational cohort of patients presenting with early inflammatory arthritis.
METHODS: Genotypes and clinician diagnoses were obtained from patients from NOAR. Six G-probabilities (0%-100%) were created for each patient based on known disease-associated odds ratios of published genetic risk variants, each corresponding to one disease of rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, spondyloarthropathy, gout, or "other diseases." Performance of the G-probabilities compared with clinician diagnosis was assessed.
RESULTS: We tested G-PROB on 1,047 patients. Calibration of G-probabilities with clinician diagnosis was high, with regression coefficients of 1.047, where 1.00 is ideal. G-probabilities discriminated clinician diagnosis with pooled areas under the curve (95% confidence interval) of 0.85 (0.84-0.86). G-probabilities <5% corresponded to a negative predictive value of 96.0%, for which it was possible to suggest >2 unlikely diseases for 94% of patients and >3 for 53.7% of patients. G-probabilities >50% corresponded to a positive predictive value of 70.4%. In 55.7% of patients, the disease with the highest G-probability corresponded to clinician diagnosis.
CONCLUSION: G-PROB converts complex genetic information into meaningful and interpretable conditional probabilities, which may be especially helpful at eliminating unlikely diagnoses in the rheumatology outpatient setting.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:76 |
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| Enthalten in: |
Arthritis & rheumatology (Hoboken, N.J.) - 76(2024), 5 vom: 05. Apr., Seite 696-703 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hum, Ryan M [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 25.04.2024 Date Revised 25.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/art.42760 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365014060 |
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| 100 | 1 | |a Hum, Ryan M |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Using Polygenic Risk Scores to Aid Diagnosis of Patients With Early Inflammatory Arthritis |b Results From the Norfolk Arthritis Register |
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| 500 | |a Date Revised 25.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology. | ||
| 520 | |a OBJECTIVE: There is growing evidence that genetic data are of benefit in the rheumatology outpatient setting by aiding early diagnosis. A genetic probability tool (G-PROB) has been developed to aid diagnosis has not yet been tested in a real-world setting. Our aim was to assess whether G-PROB could aid diagnosis in the rheumatology outpatient setting using data from the Norfolk Arthritis Register (NOAR), a prospective observational cohort of patients presenting with early inflammatory arthritis | ||
| 520 | |a METHODS: Genotypes and clinician diagnoses were obtained from patients from NOAR. Six G-probabilities (0%-100%) were created for each patient based on known disease-associated odds ratios of published genetic risk variants, each corresponding to one disease of rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, spondyloarthropathy, gout, or "other diseases." Performance of the G-probabilities compared with clinician diagnosis was assessed | ||
| 520 | |a RESULTS: We tested G-PROB on 1,047 patients. Calibration of G-probabilities with clinician diagnosis was high, with regression coefficients of 1.047, where 1.00 is ideal. G-probabilities discriminated clinician diagnosis with pooled areas under the curve (95% confidence interval) of 0.85 (0.84-0.86). G-probabilities <5% corresponded to a negative predictive value of 96.0%, for which it was possible to suggest >2 unlikely diseases for 94% of patients and >3 for 53.7% of patients. G-probabilities >50% corresponded to a positive predictive value of 70.4%. In 55.7% of patients, the disease with the highest G-probability corresponded to clinician diagnosis | ||
| 520 | |a CONCLUSION: G-PROB converts complex genetic information into meaningful and interpretable conditional probabilities, which may be especially helpful at eliminating unlikely diagnoses in the rheumatology outpatient setting | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Observational Study | |
| 700 | 1 | |a Sharma, Seema D |e verfasserin |4 aut | |
| 700 | 1 | |a Stadler, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Viatte, Sebastien |e verfasserin |4 aut | |
| 700 | 1 | |a Ho, Pauline |e verfasserin |4 aut | |
| 700 | 1 | |a Nair, Nisha |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Chenfu |e verfasserin |4 aut | |
| 700 | 1 | |a Yap, Chuan Fu |e verfasserin |4 aut | |
| 700 | 1 | |a Soomro, Mehreen |e verfasserin |4 aut | |
| 700 | 1 | |a Plant, Darren |e verfasserin |4 aut | |
| 700 | 1 | |a Humphreys, Jenny H |e verfasserin |4 aut | |
| 700 | 1 | |a MacGregor, Alexander |e verfasserin |4 aut | |
| 700 | 1 | |a Yates, Max |e verfasserin |4 aut | |
| 700 | 1 | |a Verstappen, Suzanne |e verfasserin |4 aut | |
| 700 | 1 | |a Barton, Anne |e verfasserin |4 aut | |
| 700 | 1 | |a Bowes, John |e verfasserin |4 aut | |
| 700 | 0 | |a all NOAR collaborators |e verfasserin |4 aut | |
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