Open AccessSelection 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
Author(s) -
Hanneke F.M. RhodiusMeester,
Ingrid S. van Maurik,
Juha Koikkalainen,
Antti Tolonen,
Kristian Steen Frederiksen,
Steen G. Hasselbalch,
Hilkka Soininen,
Sanna Kaisa Herukka,
Anne M. Remes,
Charlotte E. Teunissen,
Frederik Barkhof,
Yolande A.L. Pijnenburg,
Philip Scheltens,
Jyrki Lötjönen,
Wiesje M. van der Flier
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0226784
Subject(s) - medicine , biomarker , dementia , confidence interval , neuropsychology , clinical decision support system , memory clinic , oncology , disease , cerebrospinal fluid , pathology , bioinformatics , decision support system , psychiatry , artificial intelligence , cognition , biology , computer science , biochemistry , chemistry
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.