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.
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 cli...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0226784 |
| _version_ | 1818678182241370112 |
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| author | Hanneke F M Rhodius-Meester Ingrid S van Maurik Juha Koikkalainen Antti Tolonen Kristian S 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 |
| author_facet | Hanneke F M Rhodius-Meester Ingrid S van Maurik Juha Koikkalainen Antti Tolonen Kristian S 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 |
| author_sort | Hanneke F M Rhodius-Meester |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T09:11:12Z |
| format | Article |
| id | doaj.art-d230e97885c542eebef34abcaed775eb |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T09:11:12Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d230e97885c542eebef34abcaed775eb2022-12-21T21:55:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01151e022678410.1371/journal.pone.0226784Selection 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.Hanneke F M Rhodius-MeesterIngrid S van MaurikJuha KoikkalainenAntti TolonenKristian S FrederiksenSteen G HasselbalchHilkka SoininenSanna-Kaisa HerukkaAnne M RemesCharlotte E TeunissenFrederik BarkhofYolande A L PijnenburgPhilip ScheltensJyrki LötjönenWiesje M van der FlierINTRODUCTION: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.https://doi.org/10.1371/journal.pone.0226784 |
| spellingShingle | Hanneke F M Rhodius-Meester Ingrid S van Maurik Juha Koikkalainen Antti Tolonen Kristian S 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 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 |
| 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. |
| title_full | 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. |
| title_fullStr | 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. |
| title_full_unstemmed | 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. |
| title_short | 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. |
| title_sort | 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 |
| url | https://doi.org/10.1371/journal.pone.0226784 |
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