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

PLoS One. 2020 Jan 15;15(1):e0226784. doi: 10.1371/journal.pone.0226784. eCollection 2020.


Introduction: An 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.

Methods: We 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.

Results: The 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).

Conclusions: We 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Alzheimer Disease / cerebrospinal fluid*
  • Alzheimer Disease / diagnosis*
  • Alzheimer Disease / physiopathology
  • Biomarkers / cerebrospinal fluid
  • Decision Support Systems, Clinical*
  • Female
  • Humans
  • Male
  • Memory*
  • Middle Aged
  • Patient Selection*
  • Sensitivity and Specificity


  • Biomarkers

Grants and funding

This study is partly funded by Combinostics. The funder provided support in the form of salaries for authors [JK and JL], and had an additional role in the study as Juha Koikkalainen and Jyrki Lötjönen developed the method and quantitative raw data were generated using Combinostics’ tools. They also reviewed the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. For development of the PredictAD tool, VTT Technical Research Centre of Finland has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 (PredictAD), and 611005 (PredictND). The latter had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.'