Table 5.
Multivariable Impact and Contribution of Single Bilateral VFI on VRQoL
| Exposure: Single VFI | β Coefficient* (95% CI) | P Value | % Change | Standardized Dominance Statistic† |
|---|---|---|---|---|
| Outcome: VRQoL overall | ||||
| VAI | ||||
| No | Reference | NA | NA | |
| Yes | −0.34 (−0.54, −0.14) | <0.001 | −8.43 | 17.6 |
| CSI | ||||
| No | Reference | NA | NA | |
| Yes | −0.33 (−0.50, −0.17) | <0.001 | −8.28 | 24.9 |
| CVI | ||||
| No | Reference | NA | NA | |
| Yes | −0.20 (−0.43, 0.04) | 0.100 | −4.93 | 4.1 |
| DPI | ||||
| No | Reference | NA | NA | |
| Yes | −0.25 (−0.40, −0.10) | 0.001 | −6.27 | 15.0 |
| Outcome: Visual Functioning | ||||
| VAI | ||||
| No | Reference | NA | NA | |
| Yes | −0.28 (−0.42, −0.13) | <0.001 | −7.69 | 20.4 |
| CSI | ||||
| No | Reference | NA | NA | |
| Yes | −0.23 (−0.35, −0.12) | <0.001 | −6.55 | 24.0 |
| CVI | ||||
| No | Reference | NA | NA | |
| Yes | −0.21 (−0.38, −0.04) | 0.013 | −5.94 | 7.6 |
| DPI | ||||
| No | Reference | NA | NA | |
| Yes | −0.17 (−0.28, −0.07) | 0.002 | −4.94 | 15.1 |
| Outcome: Emotional Well-being | ||||
| VAI | ||||
| No | Reference | NA | NA | |
| Yes | −0.29 (−0.52, −0.07) | 0.012 | −5.83 | 13.5 |
| CSI | ||||
| No | Reference | NA | NA | |
| Yes | −0.30 (−0.49, −0.12) | 0.002 | −6.03 | 20.9 |
| CVI | ||||
| No | Reference | NA | NA | |
| Yes | −0.05 (−0.32, 0.21) | 0.711 | −1.03 | 0.8 |
| DPI | ||||
| No | Reference | NA | NA | |
| Yes | −0.30 (−0.48, −0.13) | <0.001 | −6.07 | 18.4 |
Minimal clinically important difference (defined as 0.5 SD of baseline VRQo) = 0.63. All models were adjusted for age, gender, ethnicity, socioeconomic status, living alone, smoking status, alcohol frequency, body mass index, diabetes, hypertension, dyslipidemia, CVD, CKD, and polypharmacy. Furthermore, each of the single VFI models was mutually adjusted for the other VFIs. Adjusted R2 values of the models with overall, visual functioning and emotional well-being scores of VRQoL are 0.0593, 0.0703, and 0.0376, respectively.
Coefficients derived from linear regression models.
Standardized dominance statistics are expressed in percentages