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Clinical risk stratification: Development and validation of the DAAE score, a tool for estimating patient risk of transition to secondary progressive multiple sclerosis

TA. Fuchs, R. Zivadinov, T. Pryshchepova, B. Weinstock-Guttman, MG. Dwyer, RHB. Benedict, N. Bergsland, D. Jakimovski, T. Uher, JR. Jelgerhuis, F. Barkhof, BMJ. Uitdehaag, J. Killestein, EMM. Strijbis, MM. Schoonheim

. 2024 ; 89 (-) : 105755. [pub] 20240706

Language English Country Netherlands

Document type Journal Article, Validation Study

BACKGROUND: Because secondary progressive multiple sclerosis (SPMS) is associated with worse prognosis, early predictive tools are needed. We aimed to use systematic literature review and advanced methods to create and validate a clinical tool for estimating individual patient risk of transition to SPMS over five years. METHODS: Data from the Jacobs Multiple Sclerosis Center (JMSC) and the Multiple Sclerosis Center Amsterdam (MSCA) was collected between 1994 and 2022. Participants were relapsing-remitting adult patients at initial evaluation. We created the tool in four stages: (1) identification of candidate predictors from systematic literature review, (2) ordinal cutoff determination, (3) feature selection, (4) feature weighting. RESULTS: Patients in the development/internal-validation/external-validation datasets respectively (n = 787/n = 522/n = 877) had a median age of 44.1/42.4/36.6 and disease duration of 7.7/6.2/4.4 years. From these, 12.6 %/10.2 %/15.4 % converted to SPMS (median=4.9/5.2/5.0 years). The DAAE Score was named from included predictors: Disease duration, Age at disease onset, Age, EDSS. It ranges from 0 to 12 points, with risk groups of very-low=0-2, low=3-7, medium=8-9, and high≥10. Risk of transition to SPMS increased proportionally across these groups in development (2.7 %/7.4 %/18.8 %/40.2 %), internal-validation (2.9 %/6.8 %/26.8 %/36.5 %), and external-validation (7.5 %/9.6 %/22.4 %/37.5 %). CONCLUSION: The DAAE Score estimates individual patient risk of transition to SPMS consistently across datasets internationally using clinically-accessible data. With further validation, this tool could be used for clinical risk estimation.

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$a Fuchs, Tom A $u MS Center Amsterdam, Department of Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, location VUmc, De Boelelaan 1108, Amsterdam 1081 HZ, the Netherlands; Department of Neurology and Psychiatry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States; Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States. Electronic address: t.fuchs@amsterdamumc.nl
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$a Clinical risk stratification: Development and validation of the DAAE score, a tool for estimating patient risk of transition to secondary progressive multiple sclerosis / $c TA. Fuchs, R. Zivadinov, T. Pryshchepova, B. Weinstock-Guttman, MG. Dwyer, RHB. Benedict, N. Bergsland, D. Jakimovski, T. Uher, JR. Jelgerhuis, F. Barkhof, BMJ. Uitdehaag, J. Killestein, EMM. Strijbis, MM. Schoonheim
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$a BACKGROUND: Because secondary progressive multiple sclerosis (SPMS) is associated with worse prognosis, early predictive tools are needed. We aimed to use systematic literature review and advanced methods to create and validate a clinical tool for estimating individual patient risk of transition to SPMS over five years. METHODS: Data from the Jacobs Multiple Sclerosis Center (JMSC) and the Multiple Sclerosis Center Amsterdam (MSCA) was collected between 1994 and 2022. Participants were relapsing-remitting adult patients at initial evaluation. We created the tool in four stages: (1) identification of candidate predictors from systematic literature review, (2) ordinal cutoff determination, (3) feature selection, (4) feature weighting. RESULTS: Patients in the development/internal-validation/external-validation datasets respectively (n = 787/n = 522/n = 877) had a median age of 44.1/42.4/36.6 and disease duration of 7.7/6.2/4.4 years. From these, 12.6 %/10.2 %/15.4 % converted to SPMS (median=4.9/5.2/5.0 years). The DAAE Score was named from included predictors: Disease duration, Age at disease onset, Age, EDSS. It ranges from 0 to 12 points, with risk groups of very-low=0-2, low=3-7, medium=8-9, and high≥10. Risk of transition to SPMS increased proportionally across these groups in development (2.7 %/7.4 %/18.8 %/40.2 %), internal-validation (2.9 %/6.8 %/26.8 %/36.5 %), and external-validation (7.5 %/9.6 %/22.4 %/37.5 %). CONCLUSION: The DAAE Score estimates individual patient risk of transition to SPMS consistently across datasets internationally using clinically-accessible data. With further validation, this tool could be used for clinical risk estimation.
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$a Zivadinov, Robert $u Department of Neurology and Psychiatry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States; Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States; Center for Biomedical Imaging at Clinical Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States
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$a Pryshchepova, Tetyana $u MS Center Amsterdam, Department of Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, location VUmc, De Boelelaan 1108, Amsterdam 1081 HZ, the Netherlands
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$a Weinstock-Guttman, Bianca $u Department of Neurology and Psychiatry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States
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$a Dwyer, Michael G $u Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States
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$a Benedict, Ralph H B $u Department of Neurology and Psychiatry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States
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$a Bergsland, Niels $u Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States
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$a Jakimovski, Dejan $u Department of Neurology and Psychiatry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States; Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States
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$a Uher, Tomas $u Department of Neurology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
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$a Jelgerhuis, Julia R $u MS Center Amsterdam, Department of Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, location VUmc, De Boelelaan 1108, Amsterdam 1081 HZ, the Netherlands
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$a Barkhof, Frederik $u MS Center Amsterdam, Department of Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, United Kingdom
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$a Uitdehaag, Bernard M J $u MS Center Amsterdam, Department of Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, the Netherlands
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$a Killestein, Joep $u MS Center Amsterdam, Department of Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, the Netherlands
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$a Strijbis, Eva M M $u MS Center Amsterdam, Department of Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, the Netherlands
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$a Schoonheim, Menno M $u MS Center Amsterdam, Department of Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, location VUmc, De Boelelaan 1108, Amsterdam 1081 HZ, the Netherlands
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