Netarsudil as an Adjunctive Therapy: Efficacy and Factors Contributing to a Favorable IOP-Lowering Effect

Table 5

Logistic regression analysis exploring pertinent covariates of responder status through stepwise selection using AIC criteria¹.

Model	Model 1: initial responder			Model 2: long-term responder			Model 3: robust long-term responder			Model 4: super responder
	Odds ratio	95% CI	value	Odds ratio	95% CI	value	Odds ratio	95% CI	value	Odds ratio	95% CI	value
Intercept	0.31	0.211–0.446	<0.001	0.535	0.381–0.744	<0.001	0.061	0.003–0.303	0.007	0.36	0.130.0.882	0.033
Glaucoma type: POAG (vs. other)							0.493	0.228–1.085	0.074
Glaucoma type: PFX (vs. other)							0.32	0.067–1.143	0.105
Had prior laser	1.699	0.944–3.051	0.076	0.584	0.314–1.055	0.08
Had prior surgery										3.127	0.984–11.078	0.061
Low baseline IOP (vs. very high)							5.573	0.938–107.04	0.116	0	N/A	0.989
Medium baseline IOP (vs. very high)							9.353	1.790–172.81	0.034	0.013	0.001–0.080	<0.001
High baseline IOP (vs. very high)							3.221	0.476–63.997	0.3	0.249	0.068–0.827	0.027

CI = confidence interval; IOP = intraocular pressure; POAG = primary open-angle glaucoma; PXG = pseudoexfoliative glaucoma. ¹Stepwise-selected “best” models created from the full model, which include all of the possible baseline covariates (seen in Table 1). The automated procedure chooses the best model, which is the best combination of the aforementioned covariates. ²CI cannot be estimated here since 0 super responders fall into the “low” baseline IOP category. ³IOP baseline categories: low (IOP < 15 mmHg), medium (15 mmHg ≤ IOP < 20 mmHg), high (20 mmHg ≤ IOP < 25 mmHg), and very high (IOP ≥ 25 mmHg).