ArticlePloS one2020
Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Alzheimer's disease: insights into pathology, molecular mechanisms, and therapy.Protein & cell · 2025Review
- Deciphering Perspectives: A European Survey on Clinical Decision Support Tools for Dementia and Alzheimer's Disease.Dementia and geriatric cognitive disorders · 2025Article
- Brain MRI volumetry and atrophy rating scales as predictors of amyloid status and eligibility for anti-amyloid treatment in a real-world memory clinic setting.Journal of neurology · 2024Article
- Computerized decision support to optimally funnel patients through the diagnostic pathway for dementia.Alzheimer's research & therapy · 2024Article
- Use of a digital tool to support the diagnostic process in memory clinics-a usability study.Alzheimer's research & therapy · 2024Article
- Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.PloS one · 2024Article
- How does artificial intelligence in radiology improve efficiency and health outcomes?Pediatric radiology · 2022Review
- Assessing the Views of Professionals, Patients, and Care Partners Concerning the Use of Computer Tools in Memory Clinics: International Survey Study.JMIR formative research · 2021Article
- Alzheimer's disease.Lancet (London, England) · 2021Review
- Biomarker testing in MCI patients-deciding who to test.Alzheimer's research & therapy · 2021Article
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Authors and funding
15 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAn accurate and timely diagnosis for Alzheimer's disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination.
methodsWe included 535 subjects (139 controls, 286 Alzheimer's disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients.
resultsThe computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed).
conclusionsWe used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.
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