Improvement of an External Predictive Model Based on New Information Using a Synthetic Data Approach : Application to CADASIL
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology..
Background and Objectives: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most frequent hereditary cerebral small vessel disease. It is caused by mutations of the NOTCH3 gene. The disease evolves progressively over decades leading to stroke, disability, cognitive decline, and functional dependency. The course and clinical severity of CADASIL seem heterogeneous. Predictive models are thus needed to improve prognostic evaluation and inform future clinical trials. A predictive model of the 3-year variation in the Mattis Dementia Rating Scale (MDRS), which reflects the global cognitive performance of patients with CADASIL, was previously proposed. This model made predictions based on demographic, clinical, and MRI data. We aimed to improve this existing predictive model by integrating a new potential factor, the location of the genetic mutation in the different epidermal growth factor (EGFr) domains of the NOTCH3 gene, dichotomized into EGFr domains 1 to 6 or 7 to 34.
Methods: We used a new synthetic data approach to improve the initial predictive model by incorporating additional genetic information. This method combined the predicted outcomes from the previous model and 5 "synthetic" data sets with the observed outcome in a new data set. We then applied a multiple imputation method for missing data on the mutation location.
Results: The new data set included 367 patients who were followed up for 30 to 42 months. In the multivariable model with synthetic data, patients with NOTCH3 mutations in EGFr domains 7 to 34 had an additional average decrease of -1.4 points (standard error 0.67, p = 0.035) in their MDRS score variation over 3 years compared with patients with mutations located in EGFr domains 1 to 6. Cross-validation results highlighted the improved predictive performance of the enhanced model. Moreover, the model estimation was found to be more robust than fitting a model without synthetic data.
Discussion: The use of synthetic data improved the predictive model of MDRS change over 3 years in CADASIL. The predictive performance and estimation robustness of the predictive model were enhanced using this approach, whether genetic information was used. A statistically significant association between the location of the mutation in the NOTCH3 gene and the 3-year MDRS score variation was detected.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
Neurology. Genetics - 9(2023), 5 vom: 20. Okt., Seite e200091 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chhoa, Henri [VerfasserIn] |
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| Anmerkungen: |
Date Revised 19.01.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1212/NXG.0000000000200091 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367258870 |
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| 520 | |a Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. | ||
| 520 | |a Background and Objectives: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most frequent hereditary cerebral small vessel disease. It is caused by mutations of the NOTCH3 gene. The disease evolves progressively over decades leading to stroke, disability, cognitive decline, and functional dependency. The course and clinical severity of CADASIL seem heterogeneous. Predictive models are thus needed to improve prognostic evaluation and inform future clinical trials. A predictive model of the 3-year variation in the Mattis Dementia Rating Scale (MDRS), which reflects the global cognitive performance of patients with CADASIL, was previously proposed. This model made predictions based on demographic, clinical, and MRI data. We aimed to improve this existing predictive model by integrating a new potential factor, the location of the genetic mutation in the different epidermal growth factor (EGFr) domains of the NOTCH3 gene, dichotomized into EGFr domains 1 to 6 or 7 to 34 | ||
| 520 | |a Methods: We used a new synthetic data approach to improve the initial predictive model by incorporating additional genetic information. This method combined the predicted outcomes from the previous model and 5 "synthetic" data sets with the observed outcome in a new data set. We then applied a multiple imputation method for missing data on the mutation location | ||
| 520 | |a Results: The new data set included 367 patients who were followed up for 30 to 42 months. In the multivariable model with synthetic data, patients with NOTCH3 mutations in EGFr domains 7 to 34 had an additional average decrease of -1.4 points (standard error 0.67, p = 0.035) in their MDRS score variation over 3 years compared with patients with mutations located in EGFr domains 1 to 6. Cross-validation results highlighted the improved predictive performance of the enhanced model. Moreover, the model estimation was found to be more robust than fitting a model without synthetic data | ||
| 520 | |a Discussion: The use of synthetic data improved the predictive model of MDRS change over 3 years in CADASIL. The predictive performance and estimation robustness of the predictive model were enhanced using this approach, whether genetic information was used. A statistically significant association between the location of the mutation in the NOTCH3 gene and the 3-year MDRS score variation was detected | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Chabriat, Hugues |e verfasserin |4 aut | |
| 700 | 1 | |a Anato, Adelina Joanita |e verfasserin |4 aut | |
| 700 | 1 | |a Bamba, Mamadou |e verfasserin |4 aut | |
| 700 | 1 | |a Zittoun, Florent |e verfasserin |4 aut | |
| 700 | 1 | |a Chevret, Sylvie |e verfasserin |4 aut | |
| 700 | 1 | |a Biard, Lucie |e verfasserin |4 aut | |
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