Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study

Marie Bruun (Corresponding Author), Kristian S. Frederiksen, Hanneke F.M. Rhodius-Meester, Marta Baroni, Le Gjerum, Juha Koikkalainen, Timo Urhemaa, Antti Tolonen, Mark van Gils, Tong Tong, Ricardo Guerrero, Daniel Rueckert, Nadia Dyremose, Birgitte Bo Andersen, Anja H. Simonsen, Afina W. Lemstra, Merja Hallikainen, Sudhir Kurl, Sanna-Kaisa Herukka, Anne M. RemesGunhild Waldemar, Hilkka Soininen, Patrizia Mecocci, Wiesje M. van der Flier, Jyrki Lötjönen, Steen G. Hasselbalch

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Background: Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. Objectives: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. Methods: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. Results: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011). Conclusion: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians’ confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.
Original languageEnglish
Pages (from-to)91-101
Number of pages11
JournalCurrent Alzheimer Research
Volume16
Issue number2
DOIs
Publication statusPublished - Feb 2019
MoE publication typeA1 Journal article-refereed

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Clinical Decision Support Systems
Validation Studies
Dementia
Differential Diagnosis
Lewy Body Disease
Frontotemporal Dementia
Vascular Dementia
Neuropsychological Tests
Visual Analog Scale
Multicenter Studies
Cerebrospinal Fluid
Alzheimer Disease
Biomarkers
Demography
Prospective Studies

Keywords

  • Computer-assisted diagnosis
  • neurodegenerative disease
  • CDSS
  • differential diagnosis
  • Alzheimer´s disease
  • Frontotemporal disease
  • Dementia with Lewy body
  • Vascular dementia

Cite this

Bruun, M., Frederiksen, K. S., Rhodius-Meester, H. F. M., Baroni, M., Gjerum, L., Koikkalainen, J., ... Hasselbalch, S. G. (2019). Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study. Current Alzheimer Research, 16(2), 91-101. https://doi.org/10.2174/1567205016666190103152425
Bruun, Marie ; Frederiksen, Kristian S. ; Rhodius-Meester, Hanneke F.M. ; Baroni, Marta ; Gjerum, Le ; Koikkalainen, Juha ; Urhemaa, Timo ; Tolonen, Antti ; van Gils, Mark ; Tong, Tong ; Guerrero, Ricardo ; Rueckert, Daniel ; Dyremose, Nadia ; Bo Andersen, Birgitte ; Simonsen, Anja H. ; Lemstra, Afina W. ; Hallikainen, Merja ; Kurl, Sudhir ; Herukka, Sanna-Kaisa ; Remes, Anne M. ; Waldemar, Gunhild ; Soininen, Hilkka ; Mecocci, Patrizia ; van der Flier, Wiesje M. ; Lötjönen, Jyrki ; Hasselbalch, Steen G. / Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics : The PredictND Validation Study. In: Current Alzheimer Research. 2019 ; Vol. 16, No. 2. pp. 91-101.
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abstract = "Background: Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. Objectives: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. Methods: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. Results: In total, 747 patients completed the follow-up visits (53{\%} female, 69±10 years). The etiological diagnosis changed in 13{\%} of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100{\%}) increased (ΔVAS=3.0{\%}, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2{\%}, p=0.0011). Conclusion: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians’ confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.",
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author = "Marie Bruun and Frederiksen, {Kristian S.} and Rhodius-Meester, {Hanneke F.M.} and Marta Baroni and Le Gjerum and Juha Koikkalainen and Timo Urhemaa and Antti Tolonen and {van Gils}, Mark and Tong Tong and Ricardo Guerrero and Daniel Rueckert and Nadia Dyremose and {Bo Andersen}, Birgitte and Simonsen, {Anja H.} and Lemstra, {Afina W.} and Merja Hallikainen and Sudhir Kurl and Sanna-Kaisa Herukka and Remes, {Anne M.} and Gunhild Waldemar and Hilkka Soininen and Patrizia Mecocci and {van der Flier}, {Wiesje M.} and Jyrki L{\"o}tj{\"o}nen and Hasselbalch, {Steen G.}",
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Bruun, M, Frederiksen, KS, Rhodius-Meester, HFM, Baroni, M, Gjerum, L, Koikkalainen, J, Urhemaa, T, Tolonen, A, van Gils, M, Tong, T, Guerrero, R, Rueckert, D, Dyremose, N, Bo Andersen, B, Simonsen, AH, Lemstra, AW, Hallikainen, M, Kurl, S, Herukka, S-K, Remes, AM, Waldemar, G, Soininen, H, Mecocci, P, van der Flier, WM, Lötjönen, J & Hasselbalch, SG 2019, 'Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study', Current Alzheimer Research, vol. 16, no. 2, pp. 91-101. https://doi.org/10.2174/1567205016666190103152425

Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics : The PredictND Validation Study. / Bruun, Marie (Corresponding Author); Frederiksen, Kristian S.; Rhodius-Meester, Hanneke F.M.; Baroni, Marta; Gjerum, Le; Koikkalainen, Juha; Urhemaa, Timo; Tolonen, Antti; van Gils, Mark; Tong, Tong; Guerrero, Ricardo; Rueckert, Daniel; Dyremose, Nadia; Bo Andersen, Birgitte; Simonsen, Anja H.; Lemstra, Afina W.; Hallikainen, Merja; Kurl, Sudhir; Herukka, Sanna-Kaisa; Remes, Anne M.; Waldemar, Gunhild; Soininen, Hilkka; Mecocci, Patrizia; van der Flier, Wiesje M.; Lötjönen, Jyrki; Hasselbalch, Steen G.

In: Current Alzheimer Research, Vol. 16, No. 2, 02.2019, p. 91-101.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics

T2 - The PredictND Validation Study

AU - Bruun, Marie

AU - Frederiksen, Kristian S.

AU - Rhodius-Meester, Hanneke F.M.

AU - Baroni, Marta

AU - Gjerum, Le

AU - Koikkalainen, Juha

AU - Urhemaa, Timo

AU - Tolonen, Antti

AU - van Gils, Mark

AU - Tong, Tong

AU - Guerrero, Ricardo

AU - Rueckert, Daniel

AU - Dyremose, Nadia

AU - Bo Andersen, Birgitte

AU - Simonsen, Anja H.

AU - Lemstra, Afina W.

AU - Hallikainen, Merja

AU - Kurl, Sudhir

AU - Herukka, Sanna-Kaisa

AU - Remes, Anne M.

AU - Waldemar, Gunhild

AU - Soininen, Hilkka

AU - Mecocci, Patrizia

AU - van der Flier, Wiesje M.

AU - Lötjönen, Jyrki

AU - Hasselbalch, Steen G.

PY - 2019/2

Y1 - 2019/2

N2 - Background: Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. Objectives: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. Methods: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. Results: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011). Conclusion: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians’ confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.

AB - Background: Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. Objectives: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. Methods: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. Results: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011). Conclusion: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians’ confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.

KW - Computer-assisted diagnosis

KW - neurodegenerative disease

KW - CDSS

KW - differential diagnosis

KW - Alzheimer´s disease

KW - Frontotemporal disease

KW - Dementia with Lewy body

KW - Vascular dementia

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