Predictors and characteristics of models that predict GCA with just clinical findings as well as using TA-MRA in parallel
| . | Clinical variables to predict GCA . | Clinical variables with TA-MRA to predict GCA . | ||
|---|---|---|---|---|
| . | β (s.e.) . | OR (95% CI) . | β (s.e.) . | OR (95% CI) . |
| N (events) | 268 (101) | 268 (101) | ||
| Intercept | −4.58 (0.99) | – | −2.33 (0.35) | – |
| Age | 0.04 (0.01) | 1.04 (1.01, 1.07) | – | – |
| Jaw claudication | 0.52 (0.33) | 1.69 (0.89, 3.20) | – | – |
| Vision loss | 0.88 (0.40) | 2.41 (1.10, 5.28) | – | – |
| Temporal arterial tenderness | 0.54 (0.31) | 1.71 (0.92, 3.12) | 0.61 (0.34) | 1.83 (0.95, 3.55) |
| Log CRP | 0.95 (0.22) | 2.57 (1.66, 4.00) | 0.81 (0.25) | 2.24 (1.38, 3.62) |
| TA-MRA | – | – | 2.71 (0.35) | 15.03 (7.50, 30.12) |
| AIC | 314.351 | 258.678 | ||
| Spiegelhalter Hosmer-Lemeshow | 0.01/ p=0.91 | 0.00/ p=1.00 | ||
| 6.32/ p=0.61 | 2.48/ p=0.96 | |||
| ROC | 0.753 (0.694, 0.811) | 0.827 (0.774, 0.881) | ||
| . | Clinical variables to predict GCA . | Clinical variables with TA-MRA to predict GCA . | ||
|---|---|---|---|---|
| . | β (s.e.) . | OR (95% CI) . | β (s.e.) . | OR (95% CI) . |
| N (events) | 268 (101) | 268 (101) | ||
| Intercept | −4.58 (0.99) | – | −2.33 (0.35) | – |
| Age | 0.04 (0.01) | 1.04 (1.01, 1.07) | – | – |
| Jaw claudication | 0.52 (0.33) | 1.69 (0.89, 3.20) | – | – |
| Vision loss | 0.88 (0.40) | 2.41 (1.10, 5.28) | – | – |
| Temporal arterial tenderness | 0.54 (0.31) | 1.71 (0.92, 3.12) | 0.61 (0.34) | 1.83 (0.95, 3.55) |
| Log CRP | 0.95 (0.22) | 2.57 (1.66, 4.00) | 0.81 (0.25) | 2.24 (1.38, 3.62) |
| TA-MRA | – | – | 2.71 (0.35) | 15.03 (7.50, 30.12) |
| AIC | 314.351 | 258.678 | ||
| Spiegelhalter Hosmer-Lemeshow | 0.01/ p=0.91 | 0.00/ p=1.00 | ||
| 6.32/ p=0.61 | 2.48/ p=0.96 | |||
| ROC | 0.753 (0.694, 0.811) | 0.827 (0.774, 0.881) | ||
Predictors and characteristics of models that predict GCA with just clinical findings as well as using TA-MRA in parallel
| . | Clinical variables to predict GCA . | Clinical variables with TA-MRA to predict GCA . | ||
|---|---|---|---|---|
| . | β (s.e.) . | OR (95% CI) . | β (s.e.) . | OR (95% CI) . |
| N (events) | 268 (101) | 268 (101) | ||
| Intercept | −4.58 (0.99) | – | −2.33 (0.35) | – |
| Age | 0.04 (0.01) | 1.04 (1.01, 1.07) | – | – |
| Jaw claudication | 0.52 (0.33) | 1.69 (0.89, 3.20) | – | – |
| Vision loss | 0.88 (0.40) | 2.41 (1.10, 5.28) | – | – |
| Temporal arterial tenderness | 0.54 (0.31) | 1.71 (0.92, 3.12) | 0.61 (0.34) | 1.83 (0.95, 3.55) |
| Log CRP | 0.95 (0.22) | 2.57 (1.66, 4.00) | 0.81 (0.25) | 2.24 (1.38, 3.62) |
| TA-MRA | – | – | 2.71 (0.35) | 15.03 (7.50, 30.12) |
| AIC | 314.351 | 258.678 | ||
| Spiegelhalter Hosmer-Lemeshow | 0.01/ p=0.91 | 0.00/ p=1.00 | ||
| 6.32/ p=0.61 | 2.48/ p=0.96 | |||
| ROC | 0.753 (0.694, 0.811) | 0.827 (0.774, 0.881) | ||
| . | Clinical variables to predict GCA . | Clinical variables with TA-MRA to predict GCA . | ||
|---|---|---|---|---|
| . | β (s.e.) . | OR (95% CI) . | β (s.e.) . | OR (95% CI) . |
| N (events) | 268 (101) | 268 (101) | ||
| Intercept | −4.58 (0.99) | – | −2.33 (0.35) | – |
| Age | 0.04 (0.01) | 1.04 (1.01, 1.07) | – | – |
| Jaw claudication | 0.52 (0.33) | 1.69 (0.89, 3.20) | – | – |
| Vision loss | 0.88 (0.40) | 2.41 (1.10, 5.28) | – | – |
| Temporal arterial tenderness | 0.54 (0.31) | 1.71 (0.92, 3.12) | 0.61 (0.34) | 1.83 (0.95, 3.55) |
| Log CRP | 0.95 (0.22) | 2.57 (1.66, 4.00) | 0.81 (0.25) | 2.24 (1.38, 3.62) |
| TA-MRA | – | – | 2.71 (0.35) | 15.03 (7.50, 30.12) |
| AIC | 314.351 | 258.678 | ||
| Spiegelhalter Hosmer-Lemeshow | 0.01/ p=0.91 | 0.00/ p=1.00 | ||
| 6.32/ p=0.61 | 2.48/ p=0.96 | |||
| ROC | 0.753 (0.694, 0.811) | 0.827 (0.774, 0.881) | ||
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