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Table 2 Baseline patient characteristics comparing patients who progressed to RA with those who did not in ANSWER validation cohort

From: Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort

Baseline characteristics

Overall (n = 140)

RA (n = 45)

Non-RA (n = 95)

p-value

age (median, [Q1-Q3])

62.5 [48.0–72.0]

54.0 [46.0–70.0]

64.0 [50.0–73.0]

0.112

sex (female%)

67.9%

75.6%

62.4%

0.251

BMI (median, [Q1-Q3])

21.7 [19.8–24.7]

21.6 [19.8–24.8]

21.9 [19.8–24.2]

0.617

Family history of RA (positive %)

14.3%

17.8%

12.6%

0.580

Smoking (current or previous %)

67.2%

65.0%

68.40%

0.430

CRP (median, [Q1-Q3])

2.7 [0.050–14.6]

9.4 [1.0-31.4]

2.0 [0.4–0.96]

0.011

ESR_1h (median, [Q1-Q3])

18.5 [8.0–37.0]

26.0 [12.0-44.3]

15.0 [5.0–34.0]

0.024

RF (median, [Q1-Q3])

5 [5–8]

5 [4–7]

5 [5–8]

 

ACPA (median, [Q1-Q3])

0.6 [0.6–0.6]

0.6 [0.6–0.6]

0.6 [0.6–0.6]

 

MMP-3 (median, [Q1-Q3])

75.7 [40.3-134.7]

95.1 [44.5-189.9]

56.9 [39.7–118.0]

0.030

PhGA (median, [Q1-Q3])

20.0 [10.5–36.5]

30.0 [12.0–50.0]

18.0 [10.0–30.0]

0.040

PtGA (median, [Q1-Q3])

50.0 [29.0-67.5]

50.0 [32.0–68.0]

50.0 [27.0–66.0]

0.371

TJC28 (median, [Q1-Q3])

2 [1–5]

3 [1–5]

2 [1–5]

0.837

SJC28 (median, [Q1-Q3])

1 [0–3]

1.0 [0–4.0]

1 [0–3]

0.943

CDAI (median, [Q1-Q3])

12.0 [8.35–15.85]

11.8 [8.68–17.6]

12.0 [8.35–15.35]

0.578

HAQ-DI (median, [Q1-Q3])

0.5 [0.13–0.88]

0.63 [0.25–1.44]

0.38 [0.13–0.75]

0.027

Days to diagnosis (median, [Q1-Q3])

 

92 [36–273]

  
  1. Data showing descriptive statistics before imputation
  2. Abbreviations
  3. BMI: body mass index, CRP: C-reactive protein, ESR: erythrocyte sedimentation rate, RF: rheumatoid factor, ACPA: anti-citrullinated protein antibody, MMP-3: matrix metalloproteinase 3, PhGA: physician global assessment, PtGA: patient global assessment, TJC: 28 tender joint counts, SJC: 28 swollen joint counts, CDAI: clinical disease activity index, HAQ-DI: Health Assessment Questionnaire Disability Index