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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

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Rhodius-Meester, HFM, van Maurik, IS, Koikkalainen, J, Tolonen, A, Frederiksen, KS, Hasselbalch, SG, Soininen, H, Herukka, S-K, Remes, AM, Teunissen, CE, Barkhof, F, Pijnenburg, YAL, Scheltens, P, Lötjönen, J & van der Flier, WM 2020, '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, bind 15, nr. 1, s. e0226784. https://doi.org/10.1371/journal.pone.0226784

APA

CBE

Rhodius-Meester HFM, van Maurik IS, Koikkalainen J, Tolonen A, Frederiksen KS, Hasselbalch SG, Soininen H, Herukka S-K, Remes AM, Teunissen CE, Barkhof F, Pijnenburg YAL, Scheltens P, Lötjönen J, van der Flier WM. 2020. 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. 15(1):e0226784. https://doi.org/10.1371/journal.pone.0226784

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Author

Rhodius-Meester, Hanneke F M ; van Maurik, Ingrid S ; Koikkalainen, Juha ; Tolonen, Antti ; Frederiksen, Kristian S ; Hasselbalch, Steen G ; Soininen, Hilkka ; Herukka, Sanna-Kaisa ; Remes, Anne M ; Teunissen, Charlotte E ; Barkhof, Frederik ; Pijnenburg, Yolande A L ; Scheltens, Philip ; Lötjönen, Jyrki ; van der Flier, Wiesje M. / 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. I: PLoS One. 2020 ; Bind 15, Nr. 1. s. e0226784.

Bibtex

@article{62930d610142492f94ec1b9bea46f8fb,
title = "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",
abstract = "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.",
author = "Rhodius-Meester, {Hanneke F M} and {van Maurik}, {Ingrid S} and Juha Koikkalainen and Antti Tolonen and Frederiksen, {Kristian S} and Hasselbalch, {Steen G} and Hilkka Soininen and Sanna-Kaisa Herukka and Remes, {Anne M} and Teunissen, {Charlotte E} and Frederik Barkhof and Pijnenburg, {Yolande A L} and Philip Scheltens and Jyrki L{\"o}tj{\"o}nen and {van der Flier}, {Wiesje M}",
year = "2020",
doi = "10.1371/journal.pone.0226784",
language = "English",
volume = "15",
pages = "e0226784",
journal = "P L o S One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "1",

}

RIS

TY - JOUR

T1 - 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

AU - Rhodius-Meester, Hanneke F M

AU - van Maurik, Ingrid S

AU - Koikkalainen, Juha

AU - Tolonen, Antti

AU - Frederiksen, Kristian S

AU - Hasselbalch, Steen G

AU - Soininen, Hilkka

AU - Herukka, Sanna-Kaisa

AU - Remes, Anne M

AU - Teunissen, Charlotte E

AU - Barkhof, Frederik

AU - Pijnenburg, Yolande A L

AU - Scheltens, Philip

AU - Lötjönen, Jyrki

AU - van der Flier, Wiesje M

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pone.0226784

DO - 10.1371/journal.pone.0226784

M3 - Journal article

VL - 15

SP - e0226784

JO - P L o S One

JF - P L o S One

SN - 1932-6203

IS - 1

ER -

ID: 59246649