Health Citizens – European Research Center

Tag: Age Bias

  • Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure

    Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure

    AI models that analyse electrocardiograms (ECGs) could help identify people at high risk of heart failure early, and potentially reduce racial disparities in heart failure outcomes. But whether these models perform equally well for different groups of patients has been little studied. This study used ECGs from 326,518 patient encounters at Stanford Hospital to evaluate a model trained to predict heart failure within five years, comparing its performance by race, ethnicity, age and sex.

    Overall, there were no significant differences in performance between racial groups, and performance declined with age. However, among patients aged 0 to 40, the model performed significantly worse for Black patients than for any other racial group, with the largest differences among young Black women.

    The researchers tested several ways of reducing these disparities: including race, ethnicity, sex and age in the model, training separate models for each racial group, and training it on data with equal racial representation. None of these improved the disparities. Adjusting the model’s decision thresholds individually by race, age and sex did lead to substantial improvements. The authors warn that machine learning tools for heart failure risk perpetuating existing disparities, and suggest these individualised thresholds as one way to reduce them.

    Learn more about this study here: https://doi.org/10.1161/CIRCHEARTFAILURE.123.010879

    Reference:

    Kaur, D., Hughes, J.W., Rogers, A.J., Kang, G., Narayan, S.M., Ashley, E.A., & Perez, M.V. (2024). Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure. Circulation: Heart Failure, 17(1), e010879.

  • Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups

    Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups

    Stroke is a leading cause of death and serious long-term disability in the US, with a particularly high risk among Black individuals. Risk prediction tools help identify who would benefit most from prevention, but they need to work equally well for everyone. This study compared several of these tools, including two stroke-specific models, a general cardiovascular risk model and two new machine learning models, using data from 62,482 Black and White adults aged 45 and over from four large US studies.

    Overall, the models performed similarly, and the new machine learning models did not improve their accuracy. However, all of them were worse at distinguishing between people who would and would not have a stroke when applied to Black individuals. Among women, the models’ accuracy score was 0.76 for White women and 0.69 for Black women. Among men, it ranged from 0.71 to 0.72 for White men and from 0.64 to 0.66 for Black men. The models also performed better in adults under 60 than in older adults.

    The authors conclude that current approaches are not enough to close this gap, and that the range of risk factors considered and the modelling techniques used need to be improved to address these racial disparities.

    Learn more about this study here: https://doi.org/10.1001/jama.2022.24683

    Reference:

    Hong, C., Pencina, M.J., Wojdyla, D.M., Hall, J.L., Judd, S.E., Cary, M., Engelhard, M.M., Berchuck, S., Xian, Y., D’Agostino, R., Howard, G., Kissela, B., & Henao, R. (2023). Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups. JAMA, 329(4), 306–317.

  • Age, sex and race bias in automated arrhythmia detectors

    Age, sex and race bias in automated arrhythmia detectors

    Algorithms that read electrocardiograms (ECGs) automatically are increasingly used to help identify heart conditions, but few studies have examined whether they perform equally well for different groups of patients. This study compared 56 open-source algorithms, developed by participants in the 2021 PhysioNet Challenge, on more than 130,000 ECGs, grouping the results by sex, age and race.

    Several performance measures were similar across groups, but others showed clear differences. Accuracy was significantly lower for Black patients and higher for Asian patients, with a gap of 39% on the test set. Accuracy also fell with age, by around 12% per decade, and results for male and female patients differed depending on the measure used.

    The researchers then retrained one of the best-performing models with an additional constraint designed to reduce performance differences across sex, race and age. This led to a small loss in overall performance but a significant reduction in bias. The authors conclude that bias depends on the model’s design, the population it is trained on and how its performance is measured, and that all of these should be closely examined.

    Learn more about this study here: https://doi.org/10.1016/j.jelectrocard.2022.07.007

    Reference:

    Perez Alday, E.A., Rad, A.B., Reyna, M.A., Sadr, N., Gu, A., Li, Q., Dumitru, M., Xue, J., Albert, D., Sameni, R., & Clifford, G.D. (2022). Age, sex and race bias in automated arrhythmia detectors. Journal of Electrocardiology, 74, 5–9.