Does Takayasu arteritis differ between children and adolescents? Insights from a Russian multicenter cohort

Table 6 Logistic regression model for the prediction of biologic disease-modifying antirheumatic drugs prescription

Parameter	Value
Model characteristics	Model characteristics
Number of patients (n)	78
Nagelkerke R2, %	12.3
Model P value	0.006
Predictor: Follow-up duration (per 1 month)	Predictor: Follow-up duration (per 1 month)
Odds ratio	1.030
95%CI	1.007-1.052
P value	0.010
Equation	Equation
Logit function (Z)	Z = -0.802 + 0.029 × follow-up (months)
Predicted probability (P)	P = 1/(1 + e-z)
Discriminatory performance	Discriminatory performance
Area under the curve	0.704
95%CI	0.589-0.820
P value	0.002
Optimal cut-off (Youden index)	0.396
Sensitivity at cut-off, %	89.7
Specificity at cut-off, %	46.2
Additional predictors assessed	Additional predictors assessed
Other baseline clinical/laboratory parameters	No statistically significant associations identified

CI: Confidence interval.