PREDICT-juvenile-stroke: PRospective evaluation of a prediction score determining individual clinical outcome three months after ischemic stroke in young adults - a study protocol.


Journal

BMC neurology
ISSN: 1471-2377
Titre abrégé: BMC Neurol
Pays: England
ID NLM: 100968555

Informations de publication

Date de publication:
04 Jan 2023
Historique:
received: 26 05 2022
accepted: 02 12 2022
entrez: 3 1 2023
pubmed: 4 1 2023
medline: 6 1 2023
Statut: epublish

Résumé

Although of high individual and socioeconomic relevance, a reliable prediction model for the prognosis of juvenile stroke (18-55 years) is missing. Therefore, the study presented in this protocol aims to prospectively validate the discriminatory power of a prediction score for the 3 months functional outcome after juvenile stroke or transient ischemic attack (TIA) that has been derived from an independent retrospective study using standard clinical workup data. PREDICT-Juvenile-Stroke is a multi-centre (n = 4) prospective observational cohort study collecting standard clinical workup data and data on treatment success at 3 months after acute ischemic stroke or TIA that aims to validate a new prediction score for juvenile stroke. The prediction score has been developed upon single center retrospective analysis of 340 juvenile stroke patients. The score determines the patient's individual probability for treatment success defined by a modified Rankin Scale (mRS) 0-2 or return to pre-stroke baseline mRS 3 months after stroke or TIA. This probability will be compared to the observed clinical outcome at 3 months using the area under the receiver operating characteristic curve. The primary endpoint is to validate the clinical potential of the new prediction score for a favourable outcome 3 months after juvenile stroke or TIA. Secondary outcomes are to determine to what extent predictive factors in juvenile stroke or TIA patients differ from those in older patients and to determine the predictive accuracy of the juvenile stroke prediction score on other clinical and paraclinical endpoints. A minimum of 430 juvenile patients (< 55 years) with acute ischemic stroke or TIA, and the same number of older patients will be enrolled for the prospective validation study. The juvenile stroke prediction score has the potential to enable personalisation of counselling, provision of appropriate information regarding the prognosis and identification of patients who benefit from specific treatments. The study has been registered at https://drks.de on March 31, 2022 ( DRKS00024407 ).

Sections du résumé

BACKGROUND BACKGROUND
Although of high individual and socioeconomic relevance, a reliable prediction model for the prognosis of juvenile stroke (18-55 years) is missing. Therefore, the study presented in this protocol aims to prospectively validate the discriminatory power of a prediction score for the 3 months functional outcome after juvenile stroke or transient ischemic attack (TIA) that has been derived from an independent retrospective study using standard clinical workup data.
METHODS METHODS
PREDICT-Juvenile-Stroke is a multi-centre (n = 4) prospective observational cohort study collecting standard clinical workup data and data on treatment success at 3 months after acute ischemic stroke or TIA that aims to validate a new prediction score for juvenile stroke. The prediction score has been developed upon single center retrospective analysis of 340 juvenile stroke patients. The score determines the patient's individual probability for treatment success defined by a modified Rankin Scale (mRS) 0-2 or return to pre-stroke baseline mRS 3 months after stroke or TIA. This probability will be compared to the observed clinical outcome at 3 months using the area under the receiver operating characteristic curve. The primary endpoint is to validate the clinical potential of the new prediction score for a favourable outcome 3 months after juvenile stroke or TIA. Secondary outcomes are to determine to what extent predictive factors in juvenile stroke or TIA patients differ from those in older patients and to determine the predictive accuracy of the juvenile stroke prediction score on other clinical and paraclinical endpoints. A minimum of 430 juvenile patients (< 55 years) with acute ischemic stroke or TIA, and the same number of older patients will be enrolled for the prospective validation study.
DISCUSSION CONCLUSIONS
The juvenile stroke prediction score has the potential to enable personalisation of counselling, provision of appropriate information regarding the prognosis and identification of patients who benefit from specific treatments.
TRIAL REGISTRATION BACKGROUND
The study has been registered at https://drks.de on March 31, 2022 ( DRKS00024407 ).

Identifiants

pubmed: 36597038
doi: 10.1186/s12883-022-03003-7
pii: 10.1186/s12883-022-03003-7
pmc: PMC9811707
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2

Informations de copyright

© 2023. The Author(s).

Références

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Auteurs

Sonja Schönecker (S)

Department of Neurology, Ludwig-Maximilians-Universität München, Marchioninistr. 15, D-81377, Munich, Germany.

Verena Hoffmann (V)

Institute for Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.

Fady Albashiti (F)

Center for Medical Data Integration and Analysis, Ludwig-Maximilians-Universität München, Munich, Germany.

Reinhard Thasler (R)

Center for Medical Data Integration and Analysis, Ludwig-Maximilians-Universität München, Munich, Germany.

Marlien Hagedorn (M)

Center for Medical Data Integration and Analysis, Ludwig-Maximilians-Universität München, Munich, Germany.

Marie-Luise Louiset (ML)

Institute of Laboratory Medicine, Ludwig-Maximilians-Universität München, Munich, Germany.

Anna Kopczak (A)

Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität München, Munich, Germany.

Jennifer Rösler (J)

Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.

Enayatullah Baki (E)

Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.

Silke Wunderlich (S)

Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.

Florian Kohlmayer (F)

Institute of Medical Informatics, Statistics and Epidemiology, University Hospital rechts der Isar of the Technical University Munich, Munich, Germany.

Klaus Kuhn (K)

Institute of Medical Informatics, Statistics and Epidemiology, University Hospital rechts der Isar of the Technical University Munich, Munich, Germany.

Martin Boeker (M)

Institute of Medical Informatics, Statistics and Epidemiology, University Hospital rechts der Isar of the Technical University Munich, Munich, Germany.

Johannes Tünnerhoff (J)

Department of Neurology & Stroke, Eberhard Karls University Tübingen, Tübingen, Germany.
Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Tübingen, Germany.

Sven Poli (S)

Department of Neurology & Stroke, Eberhard Karls University Tübingen, Tübingen, Germany.
Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Tübingen, Germany.

Ulf Ziemann (U)

Department of Neurology & Stroke, Eberhard Karls University Tübingen, Tübingen, Germany.
Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Tübingen, Germany.

Oliver Kohlbacher (O)

Department of Computer Science, Center for Bioinformatics and Quantitative Biology Center, Eberhard-Karls-University Tübingen, Tübingen, Germany.
Max Planck Institute for Developmental Biology, Tübingen, Germany.

Katharina Althaus (K)

Department of Neurology, University of Ulm, Ulm, Germany.

Susanne Müller (S)

Department of Neurology, University of Ulm, Ulm, Germany.

Albert Ludolph (A)

Department of Neurology, University of Ulm, Ulm, Germany.

Hans A Kestler (HA)

Intitute of Medical Systems Biology, University of Ulm, Ulm, Germany.

Ulrich Mansmann (U)

Institute for Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.
Pettenkofer School for Public Health, Munich, Germany.

Marianne Dieterich (M)

Department of Neurology, Ludwig-Maximilians-Universität München, Marchioninistr. 15, D-81377, Munich, Germany.
German Center for Neurodegenerative Diseases (DZNE), Ludwig-Maximilians-Universität München, Munich, Germany.
German Center for Vertigo and Balance Disorders, Ludwig-Maximilians-Universität München, Munich, Germany.
Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.

Lars Kellert (L)

Department of Neurology, Ludwig-Maximilians-Universität München, Marchioninistr. 15, D-81377, Munich, Germany. lars.kellert@med.uni-muenchen.de.

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Classifications MeSH