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).
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
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 (∆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 (∆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
VL - 11
SP - 25
JO - Alzheimer's Research and Therapy
JF - Alzheimer's Research and Therapy
SN - 1758-9193
IS - 1
M1 - 25
ER -