Background Visual impairment frequently occurs amongst older people. Therefore, the aim of this study was to investigate the predictive value of visual impairment on functioning, quality of life... Show moreBackground Visual impairment frequently occurs amongst older people. Therefore, the aim of this study was to investigate the predictive value of visual impairment on functioning, quality of life and mortality in people aged 85 years. Methods From the Leiden 85-plus Study, 548 people aged 85 years were eligible for this study. Visual acuity was measured at baseline by Early Treatment Diabetic Retinopathy Study charts (ETDRS). According to the visual acuity (VA) three groups were made, defined as no (VA > 0.7), moderate (0.5 <= VA <= 0.7) or severe visual impairment (VA < 0.5). Quality of life, physical, cognitive, psychological and social functioning were measured annually for 5 years. For mortality, participants were followed until the age of 95. Results At baseline, participants with visual impairment scored lower on physical, cognitive, psychological and social functioning and quality of life (p < 0.001). Compared to participants with no visual impairment, participants with moderate and severe visual impairment had an accelerated deterioration in basic activities of daily living (respectively 0.27-point (p = 0.017) and 0.35 point (p = 0.018)). In addition, compared to participants with no visual impairment, the mortality risk was 1.83 (95% CI 1.43, 2.35) for participants with severe visual impairment. Discussion In very older adults, visual impairment predicts accelerated deterioration in physical functioning. In addition, severely visually impaired adults had an increased mortality risk. A pro-active attitude, focussing on preventing and treating visual impairment could possibly contribute to the improvement of physical independence, wellbeing and successful aging in very old age. Show less
Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.Study Design and Setting: We... Show moreObjective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified.Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study.Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations. (C) 2020 The Authors. Published by Elsevier Inc. Show less
