Impact of a clinical decision support tool on prediction of progression in early-stage dementia: A prospective validation study

Marie Bruun; Kristian S. Frederiksen; Hanneke F.M. Rhodius-Meester; Marta Baroni; Le Gjerum; Juha Koikkalainen; Timo Urhemaa; Antti Tolonen; Mark Van Gils; Daniel Rueckert; Nadia Dyremose; Birgitte B. Andersen; Afina W. Lemstra; Merja Hallikainen; Sudhir Kurl; Sanna Kaisa Herukka; Anne M. Remes; Gunhild Waldemar; Hilkka Soininen; Patrizia Mecocci; Wiesje M. Van Der Flier; Jyrki Lötjönen; Steen G. Hasselbalch

doi:10.1186/s13195-019-0482-3

Impact of a clinical decision support tool on prediction of progression in early-stage dementia: A prospective validation study

Marie Bruun, Kristian S. Frederiksen, Hanneke F.M. Rhodius-Meester, Marta Baroni, Le Gjerum, Juha Koikkalainen, Timo Urhemaa, Antti Tolonen, Mark Van Gils, Daniel Rueckert, Nadia Dyremose, Birgitte B. Andersen, Afina W. Lemstra, Merja Hallikainen, Sudhir Kurl, Sanna Kaisa Herukka, Anne M. Remes, Gunhild Waldemar, Hilkka Soininen, Patrizia MecocciWiesje M. Van Der Flier, Jyrki Lötjönen, Steen G. Hasselbalch

Research output: Contribution to journal › Article › Scientific › peer-review

22 Citations (Scopus)

Abstract

Background: In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics. Methods: In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0-100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy. Results: After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI - 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI - 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (Δ VAS = 4%, p <.0001). Conclusions: Adding the PredictND tool to the clinical evaluation increased clinicians' confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.

Original language	English
Article number	25
Journal	Alzheimer's Research And Therapy
Volume	11
Issue number	1
DOIs	https://doi.org/10.1186/s13195-019-0482-3
Publication status	Published - 20 Mar 2019
Externally published	Yes
Publication type	Not Eligible

Funding

HS has served in advisory boards for ACImmune and MERK. WMvdF performs contract research for Biogen and has research programs funded by the ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, Stichting Dioraphte, Gieskes-Strijbis Fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche BV, Janssen Stellar, and Combinostics. All funding is paid to her institution. JL and JK are shareholders in Combinostics Oy that owns the following IPR related to the patent: (1) J. Koikkalainen and J. Lotjonen. A method for inferring the state of a system, US7,840,510 B2, PCT/ FI2007/050277. (2) J. Lotjonen, J. Koikkalainen and J. Mattila. State Inference in a heterogeneous system, PCT/FI2010/050545. FI20125177. The other authors declare that they have no competing interests. This work was co-funded by the European Commission under grant agreement 611005 (PredictND). For development of the PredictND tool, VTT Technical Research Center of Finland Ltd has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 and 611005. Furthermore, the work was supported by the Ellen Mørch´s foundation and the family Hede Nielsen´s foundation.

Keywords

Alzheimer's disease
CDSS
Computer-assisted
Conversion
Dementia
Mild cognitive impairment
Progression
Subjective cognitive decline

ASJC Scopus subject areas

Neurology
Clinical Neurology
Cognitive Neuroscience

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10.1186/s13195-019-0482-3

Cite this

Bruun, M., Frederiksen, K. S., Rhodius-Meester, H. F. M., Baroni, M., Gjerum, L., Koikkalainen, J., Urhemaa, T., Tolonen, A., Van Gils, M., Rueckert, D., Dyremose, N., Andersen, B. B., Lemstra, A. W., Hallikainen, M., Kurl, S., Herukka, S. K., Remes, A. M., Waldemar, G., Soininen, H., ... Hasselbalch, S. G. (2019). Impact of a clinical decision support tool on prediction of progression in early-stage dementia: A prospective validation study. Alzheimer's Research And Therapy, 11(1), Article 25. https://doi.org/10.1186/s13195-019-0482-3

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title = "Impact of a clinical decision support tool on prediction of progression in early-stage dementia: A prospective validation study",

abstract = "Background: In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics. Methods: In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0-100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy. Results: After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI - 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI - 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (Δ VAS = 4%, p <.0001). Conclusions: Adding the PredictND tool to the clinical evaluation increased clinicians' confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.",

keywords = "Alzheimer's disease, CDSS, Computer-assisted, Conversion, Dementia, Mild cognitive impairment, Progression, Subjective cognitive decline",

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 Daniel Rueckert and Nadia Dyremose and Andersen, {Birgitte B.} and Lemstra, {Afina W.} and Merja Hallikainen and Sudhir Kurl and Herukka, {Sanna Kaisa} 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.}",

note = "Funding Information: HS has served in advisory boards for ACImmune and MERK. WMvdF performs contract research for Biogen and has research programs funded by the ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, Stichting Dioraphte, Gieskes-Strijbis Fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche BV, Janssen Stellar, and Combinostics. All funding is paid to her institution. JL and JK are shareholders in Combinostics Oy that owns the following IPR related to the patent: (1) J. Koikkalainen and J. Lotjonen. A method for inferring the state of a system, US7,840,510 B2, PCT/ FI2007/050277. (2) J. Lotjonen, J. Koikkalainen and J. Mattila. State Inference in a heterogeneous system, PCT/FI2010/050545. FI20125177. The other authors declare that they have no competing interests. Funding Information: This work was co-funded by the European Commission under grant agreement 611005 (PredictND). For development of the PredictND tool, VTT Technical Research Center of Finland Ltd has received funding from European Union{\textquoteright}s Seventh Framework Programme for research, technological development and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 and 611005. Furthermore, the work was supported by the Ellen M{\o}rch´s foundation and the family Hede Nielsen´s foundation. Publisher Copyright: {\textcopyright} 2019 The Author(s).",

year = "2019",

month = mar,

day = "20",

doi = "10.1186/s13195-019-0482-3",

language = "English",

volume = "11",

number = "1",

}

Bruun, M, Frederiksen, KS, Rhodius-Meester, HFM, Baroni, M, Gjerum, L, Koikkalainen, J, Urhemaa, T, Tolonen, A , Van Gils, M, Rueckert, D, Dyremose, N, Andersen, BB, Lemstra, AW, Hallikainen, M, Kurl, S, Herukka, SK, 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 prediction of progression in early-stage dementia: A prospective validation study', Alzheimer's Research And Therapy, vol. 11, no. 1, 25. https://doi.org/10.1186/s13195-019-0482-3

TY - JOUR

T1 - Impact of a clinical decision support tool on prediction of progression in early-stage dementia

T2 - A prospective 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 - Rueckert, Daniel

AU - Dyremose, Nadia

AU - Andersen, Birgitte B.

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.

N1 - Funding Information: HS has served in advisory boards for ACImmune and MERK. WMvdF performs contract research for Biogen and has research programs funded by the ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, Stichting Dioraphte, Gieskes-Strijbis Fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche BV, Janssen Stellar, and Combinostics. All funding is paid to her institution. JL and JK are shareholders in Combinostics Oy that owns the following IPR related to the patent: (1) J. Koikkalainen and J. Lotjonen. A method for inferring the state of a system, US7,840,510 B2, PCT/ FI2007/050277. (2) J. Lotjonen, J. Koikkalainen and J. Mattila. State Inference in a heterogeneous system, PCT/FI2010/050545. FI20125177. The other authors declare that they have no competing interests. Funding Information: This work was co-funded by the European Commission under grant agreement 611005 (PredictND). For development of the PredictND tool, VTT Technical Research Center of Finland Ltd has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 and 611005. Furthermore, the work was supported by the Ellen Mørch´s foundation and the family Hede Nielsen´s foundation. Publisher Copyright: © 2019 The Author(s).

PY - 2019/3/20

Y1 - 2019/3/20

N2 - Background: In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics. Methods: In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0-100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy. Results: After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI - 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI - 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (Δ VAS = 4%, p <.0001). Conclusions: Adding the PredictND tool to the clinical evaluation increased clinicians' confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.

AB - Background: In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics. Methods: In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0-100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy. Results: After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI - 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI - 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (Δ VAS = 4%, p <.0001). Conclusions: Adding the PredictND tool to the clinical evaluation increased clinicians' confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.

KW - Alzheimer's disease

KW - CDSS

KW - Computer-assisted

KW - Conversion

KW - Dementia

KW - Mild cognitive impairment

KW - Progression

KW - Subjective cognitive decline

UR - http://www.scopus.com/inward/record.url?scp=85063294881&partnerID=8YFLogxK

U2 - 10.1186/s13195-019-0482-3

DO - 10.1186/s13195-019-0482-3

M3 - Article

C2 - 30894218

AN - SCOPUS:85063294881

SN - 1758-9193

VL - 11

JO - Alzheimer's Research And Therapy

JF - Alzheimer's Research And Therapy

IS - 1

M1 - 25

ER -

Impact of a clinical decision support tool on prediction of progression in early-stage dementia: A prospective validation study

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