PCE predicts ocular diseases using primary care data

The single composite metric Pooled Cohort Equations (PCE) helps identify the risk for multiple ocular diseases using data readily available in primary care, a study has shown.

Electronic health record data from the All of Us Research Program were used in this retrospective cohort study that included a total of 35,909 adult participants aged 40 to 79 years with complete variables for PCE calculation within a 6-month period between 2009 and 2015.

The authors computed individual-level PCE scores and categorized these into four PCE risk categories: low (<5 percent), borderline (5 percent to 7.4 percent), intermediate (7.5 percent to 19.9 percent), and high (≥20 percent). They performed time-to-event analyses using Kaplan–Meier curves and univariate and multivariable Cox proportional hazards regression models, adjusted for race, BMI, chronic kidney disease, and education.

Higher PCE risk categories significantly correlated with an elevated risk of ocular diseases. Compared with the low-risk group, the high-risk group had the greatest risk for age-related macular degeneration (AMD; hazard ratio [HR], 6.22), diabetic retinopathy (DR; HR, 5.93), glaucoma (HR, 2.33), retinal vein occlusion (HR, 3.38), and hypertensive retinopathy (HTR; HR, 4.47; p<0.001 for all).

The highest adjusted C-indices were seen with AMD (0.72), DR (0.751), and HTR (0.768), while moderate adjusted C-indices were observed with glaucoma (0.625) and RVO (0.654).

The findings persisted in different follow-up periods.

In component-adjustment sensitivity models, the association between PCE and AMD was driven by age, while associations for DR and HTR remained robust.

These results indicate that “PCE could be incorporated into primary care settings to identify individuals who would benefit from earlier ophthalmologic evaluation and prevention strategies,” the authors said.