External validation of dementia risk models in a United Kingdom population-based prospective cohort among stroke...

Stroke-survivors are at a significantly elevated risk of post-stroke cognitive impairment and dementia, yet early identification of high-risk cases remains a challenge. Dementia risk models developed for the general population may not translate well to stroke populations due to unique pathophysiological mechanisms, risk factor profiles, and accelerated cognitive decline often observed after cerebrovascular events. The aim of this study was to assess the accuracy of existing dementia models in a population-based stroke cohort.

This study externally validated 11 risk prediction models in a population-based cohort of stroke survivors from the European Prospective Investigation of Cancer-Norfolk study. Model predictive performance was assessed in line with their development parameters as well as over six follow-up periods using Cox proportional hazards and Fine and Gray competing-risk models with an outcome of all-cause dementia. Model fit was evaluated for discrimination and calibration. Participants were eligible if they had experienced a stroke without any prior diagnosis of dementia.

In total, 3782 stroke survivors were followed for a mean of 3.89 years (SD, 4.72), with a maximum follow-up of 25.70 years, with 662 later diagnosed dementia. Model discriminative performance was generally poor across all models (c-statistic range: 0.52-0.69), with inconsistent calibration across visual and statistical assessments. Most models showed minimal predictive advantage compared to a model incorporating age as the sole predictor.

Dementia risk prediction models developed for the general population have limited transportability to stroke populations. Given the high risk of dementia post-stroke, there is an urgent need to develop and validate stroke-specific prediction models that account for unique risk factors in this population. This will enable clinicians to identify stroke survivors who may benefit from early intervention and enhanced monitoring to reduce their dementia risk.

After a stroke, people are at much greater risk of developing problems with their memory and thinking. Many will then go onto develop dementia. At present there is no agreement on how to identify such individuals so that they can receive support earlier. One potential way to identify at-risk individuals is using "risk calculators" to predict an individual's risk of developing dementia. These risk calculators use individual characteristics in combination to generate a score as to how likely someone will go onto develop dementia. However, many of these risk calculators have not been tested in stroke patients. The aim of this study was to assess how accurate these calculators are to identify dementia in stroke patients over time. We tested these calculators in a large database of stroke patients and looked at the performance of some common risk calculators. We found that these risk calculators did not perform accurately in stroke patients to predict dementia over various time points. The same result was found no matter what type of risk calculator or what patient characteristics were included in the calculator. Furthermore, as age is the biggest predictor of dementia over time, we wanted to see how using a calculator based on age alone compared to these other more complicated calculators that contained more patient factors. Again, there was very little predictive advantage gained between these more complicated calculators versus ones that contained age alone. In summary, this study calls for further work in developing stroke-specific risk calculators so that these individuals can then be identified earlier rather than relying on existing calculators, which have been shown to be fairly inaccurate in stroke patients.