Limits...
Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: A Longitudinal Study.

Gracitelli CP, Tatham AJ, Boer ER, Abe RY, Diniz-Filho A, Rosen PN, Medeiros FA - PLoS ONE (2015)

Bottom Line: Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors.UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022).The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18).

View Article: PubMed Central - PubMed

Affiliation: Visual Performance Laboratory, Department of Ophthalmology, University of California, San Diego, California, United States of America; Department of Ophthalmology, Federal University of São Paulo, São Paulo, São Paulo, Brazil.

ABSTRACT

Purpose: To evaluate the ability of longitudinal Useful Field of View (UFOV) and simulated driving measurements to predict future occurrence of motor vehicle collision (MVC) in drivers with glaucoma.

Design: Prospective observational cohort study.

Participants: 117 drivers with glaucoma followed for an average of 2.1 ± 0.5 years.

Methods: All subjects had standard automated perimetry (SAP), UFOV, driving simulator, and cognitive assessment obtained at baseline and every 6 months during follow-up. The driving simulator evaluated reaction times to high and low contrast peripheral divided attention stimuli presented while negotiating a winding country road, with central driving task performance assessed as "curve coherence". Drivers with MVC during follow-up were identified from Department of Motor Vehicle records.

Main outcome measures: Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors.

Results: Mean age at baseline was 64.5 ± 12.6 years. 11 of 117 (9.4%) drivers had a MVC during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of MVC, with a hazard ratio (HR) of 2.19 per 1 SD slower reaction time (95% CI, 1.30 to 3.69; P = 0.003). UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022). Global SAP visual field indices in the better or worse eye were not predictive of MVC. The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18).

Conclusions: Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of MVC in glaucoma patients. These findings may help improve the understanding of factors associated with driving impairment related to glaucoma.

No MeSH data available.


Related in: MedlinePlus

Kaplan-Meier survival curve estimating the cumulative probability of motor vehicle collision during follow-up.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4591330&req=5

pone.0138288.g001: Kaplan-Meier survival curve estimating the cumulative probability of motor vehicle collision during follow-up.

Mentions: The study included 117 subjects with glaucoma with a mean ± standard deviation age of 64.5 ± 12.6 years at baseline. Subjects were followed for an average of 2.1 ± 0.5 years with an average of 3.1 ± 2.3 visits during follow-up. 11 patients (9.4%) had at least one MVC during follow-up. Fig 1 illustrates the cumulative probability of having a MVC during the study. Table 1 shows baseline demographic and clinical characteristics of subjects who had MVC versus those who did not. At baseline, only the parameter measuring performance on the central driving task (curve coherence) was significantly different between the two groups. The individual characteristics of patient are shown in S1 Table.


Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: A Longitudinal Study.

Gracitelli CP, Tatham AJ, Boer ER, Abe RY, Diniz-Filho A, Rosen PN, Medeiros FA - PLoS ONE (2015)

Kaplan-Meier survival curve estimating the cumulative probability of motor vehicle collision during follow-up.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4591330&req=5

pone.0138288.g001: Kaplan-Meier survival curve estimating the cumulative probability of motor vehicle collision during follow-up.
Mentions: The study included 117 subjects with glaucoma with a mean ± standard deviation age of 64.5 ± 12.6 years at baseline. Subjects were followed for an average of 2.1 ± 0.5 years with an average of 3.1 ± 2.3 visits during follow-up. 11 patients (9.4%) had at least one MVC during follow-up. Fig 1 illustrates the cumulative probability of having a MVC during the study. Table 1 shows baseline demographic and clinical characteristics of subjects who had MVC versus those who did not. At baseline, only the parameter measuring performance on the central driving task (curve coherence) was significantly different between the two groups. The individual characteristics of patient are shown in S1 Table.

Bottom Line: Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors.UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022).The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18).

View Article: PubMed Central - PubMed

Affiliation: Visual Performance Laboratory, Department of Ophthalmology, University of California, San Diego, California, United States of America; Department of Ophthalmology, Federal University of São Paulo, São Paulo, São Paulo, Brazil.

ABSTRACT

Purpose: To evaluate the ability of longitudinal Useful Field of View (UFOV) and simulated driving measurements to predict future occurrence of motor vehicle collision (MVC) in drivers with glaucoma.

Design: Prospective observational cohort study.

Participants: 117 drivers with glaucoma followed for an average of 2.1 ± 0.5 years.

Methods: All subjects had standard automated perimetry (SAP), UFOV, driving simulator, and cognitive assessment obtained at baseline and every 6 months during follow-up. The driving simulator evaluated reaction times to high and low contrast peripheral divided attention stimuli presented while negotiating a winding country road, with central driving task performance assessed as "curve coherence". Drivers with MVC during follow-up were identified from Department of Motor Vehicle records.

Main outcome measures: Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors.

Results: Mean age at baseline was 64.5 ± 12.6 years. 11 of 117 (9.4%) drivers had a MVC during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of MVC, with a hazard ratio (HR) of 2.19 per 1 SD slower reaction time (95% CI, 1.30 to 3.69; P = 0.003). UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022). Global SAP visual field indices in the better or worse eye were not predictive of MVC. The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18).

Conclusions: Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of MVC in glaucoma patients. These findings may help improve the understanding of factors associated with driving impairment related to glaucoma.

No MeSH data available.


Related in: MedlinePlus