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<p>After the clinical implementation of amyloid-β-targeting therapies for people with cognitive impairment due to Alzheimer’s disease (AD), there is an urgent need to efficiently identify this patient population in primary care. Therefore, we created a brief and self-administered digital cognitive test battery (BioCog). Based on its sub-scores, a logistic regression model was developed in a secondary care cohort (<i>n</i> = 223) and then evaluated in an independent primary care cohort comprising 19 primary care centers (<i>n</i> = 403). In primary care, BioCog had an accuracy of 85% when using a single cutoff to define cognitive impairment, which was significantly better than the assessment of primary care physicians (accuracy 73%). The accuracy increased to 90% when using a two-cutoff approach. BioCog had significantly higher accuracy than standard paper-and-pencil tests (that is, Mini-Mental State Examination, Montreal Cognitive Assessment, Mini-Cog) and another digital cognitive test. Furthermore, BioCog combined with a blood test could detect clinical, biomarker-verified AD with an accuracy of 90% (one cutoff), significantly better than standard-of-care (accuracy 70%) or when using the blood test alone (accuracy 80%). In conclusion, this proof-of-concept study shows that a brief, self-administered digital cognitive test battery can detect cognitive impairment and, when combined with a blood test, accurately identify clinical AD in primary care.</p>

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Primary care detection of Alzheimer’s disease using a self-administered digital cognitive test and blood biomarkers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月15日

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  • Pontus Tideman,
  • Linda Karlsson,
  • Olof Strandberg,
  • Susanna Calling,
  • Ruben Smith,
  • Patrik Midlöv,
  • Philip B. Verghese,
  • Joel B. Braunstein,
  • Niklas Mattsson-Carlgren,
  • Erik Stomrud,
  • Sebastian Palmqvist,
  • Oskar Hansson

摘要

After the clinical implementation of amyloid-β-targeting therapies for people with cognitive impairment due to Alzheimer’s disease (AD), there is an urgent need to efficiently identify this patient population in primary care. Therefore, we created a brief and self-administered digital cognitive test battery (BioCog). Based on its sub-scores, a logistic regression model was developed in a secondary care cohort (n = 223) and then evaluated in an independent primary care cohort comprising 19 primary care centers (n = 403). In primary care, BioCog had an accuracy of 85% when using a single cutoff to define cognitive impairment, which was significantly better than the assessment of primary care physicians (accuracy 73%). The accuracy increased to 90% when using a two-cutoff approach. BioCog had significantly higher accuracy than standard paper-and-pencil tests (that is, Mini-Mental State Examination, Montreal Cognitive Assessment, Mini-Cog) and another digital cognitive test. Furthermore, BioCog combined with a blood test could detect clinical, biomarker-verified AD with an accuracy of 90% (one cutoff), significantly better than standard-of-care (accuracy 70%) or when using the blood test alone (accuracy 80%). In conclusion, this proof-of-concept study shows that a brief, self-administered digital cognitive test battery can detect cognitive impairment and, when combined with a blood test, accurately identify clinical AD in primary care.