ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images

To develop an automated retina layer thickness measurement tool for the ImageJ platform, to quantitate nuclear layers following the retina contour. We developed the ThicknessTool (TT), an automated thickness measurement plugin for the ImageJ platform. To calibrate TT, we created a calibration dataset of mock binary skeletonized mask images with increasing thickness masks and different rotations. Following, we created a training dataset and performed an agreement analysis of thickness measurements between TT and two masked manual observers. Finally, we tested the performance of TT measurements in a validation dataset of retinal detachment images. In the calibration dataset, there were no differences in layer thickness between measured and known thickness masks, with an overall coefficient of variation of 0.00%. Training dataset measurements of immunofluorescence retina nuclear layers disclosed no significant differences between TT and any observer's average outer nuclear layer (ONL) (p = 0.998), inner nuclear layer (INL) (p = 0.807), and ONL/INL ratio (p = 0.944) measurements. Agreement analysis showed that bias between TT vs. observers' mean was lower than between any observers' mean against each other in the ONL (0.77 ± 0.34 µm vs 3.25 ± 0.33 µm) and INL (1.59 ± 0.28 µm vs 2.82 ± 0.36 µm). Validation dataset showed that TT can detect significant and true ONL thinning (p = 0.006), more sensitive than manual measurement capabilities (p = 0.069). ThicknessTool can measure retina nuclear layers thickness in a fast, accurate, and precise manner with multi-platform capabilities. In addition, the TT can be customized to user preferences and is freely available to download

Disability Status and Secondary Prevention Among Survivors of Stroke: A Cross‐Sectional Analysis of the 2011 to 2018 National Health and Nutrition Examination Survey

Background Among survivors of stroke, adherence to secondary prevention care is associated with decreased risk of recurrent stroke. However, not all survivors of stroke use secondary stroke prevention treatment. We examined the association between the disability status of survivors of stroke and their treatment and control of diabetes, hyperlipidemia, and hypertension. Methods and Results In a cross‐sectional analysis of the 2011 to 2018 National Health and Nutrition Examination Survey, we compared diabetes, hyperlipidemia, and hypertension treatment and control rates among self‐reported survivors of stroke age ≥20 years with and without disability. Disability was defined as self‐reporting any of 5 physical or 4 functional domains assessed using a structured questionnaire. Logistic regression models adjusted for age, sex, race and ethnicity, and history of medical conditions were used to estimate associations between disability status and risk factor treatment and control. The mean age of survivors of stroke was 65.1 years, and 55.5% were female; 76% (95% CI, 72.7%–79.3%) self‐reported at least 1 disability. Age‐standardized treatment rates for diabetes, hyperlipidemia, and hypertension were 33.1% (95% CI, 26.9%–39.2%), 67.5% (95% CI, 62.6%–72.3%), and 78.4% (95% CI, 74.6%–82.2%), respectively. Age‐standardized control rates for diabetes, hyperlipidemia, and hypertension were 86.8% (95% CI, 83.8%–89.8%), 20.5% (95% CI, 15.0%–25.9%), and 47.1% (95% CI, 42.6%–51.7%), respectively. In adjusted models, those with and without disabilities had similar odds of risk factor treatment and control. Conclusions In the United States, three‐quarters of survivors of stroke self‐reported a disability, and these patients had similar odds of diabetes, hyperlipidemia, and hypertension treatment and control compared with those without disability