Dynamic prediction models improved the risk classification of type 2 diabetes compared with classical static models
Copyright © 2021. Published by Elsevier Inc..
OBJECTIVE: Dynamic prediction models use the repeated measurements of predictors to estimate coefficients that link the longitudinal predictors to a static model (i.e. Cox regression). This study aims to develop and validate a dynamic prediction for incident type 2 diabetes (T2DM) as the outcome.
STUDY DESIGN AND SETTING: Data from the Tehran lipid and glucose study was used to develop (n = 5291 individuals; phases 1 to 3) and validate (n = 3147 individuals; phases 3 to 6) the dynamic prediction model among individuals aged ≥ 20 years. We used repeated measurements of fasting plasma glucose (FPG) or waist circumference (WC) in the framework of the joint modeling (JM) of longitudinal and time-to-event analysis.
RESULTS: Compared with the Cox which used just baseline data, JM showed the same discrimination, better calibration, and higher clinical usefulness (i.e. with a net benefit considering both true and false positive decisions); all were shown with repeated measurements of FPG/WC. Additionally, in our study, the dynamic models improve the risk reclassification (net reclassification index 33% for FPG and 24% for WC model).
CONCLUSION: Dynamic prediction models, compared with the static one could yield significant improvements in the prediction of T2DM. The complexity of the dynamic models could be addressed by using decision support systems.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:140 |
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| Enthalten in: |
Journal of clinical epidemiology - 140(2021) vom: 15. Dez., Seite 33-43 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Asgari, Samaneh [VerfasserIn] |
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| Links: |
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| Themen: |
Blood Glucose |
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| Anmerkungen: |
Date Completed 24.01.2022 Date Revised 24.01.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jclinepi.2021.08.026 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM329978489 |
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| 245 | 1 | 0 | |a Dynamic prediction models improved the risk classification of type 2 diabetes compared with classical static models |
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| 500 | |a Date Completed 24.01.2022 | ||
| 500 | |a Date Revised 24.01.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021. Published by Elsevier Inc. | ||
| 520 | |a OBJECTIVE: Dynamic prediction models use the repeated measurements of predictors to estimate coefficients that link the longitudinal predictors to a static model (i.e. Cox regression). This study aims to develop and validate a dynamic prediction for incident type 2 diabetes (T2DM) as the outcome | ||
| 520 | |a STUDY DESIGN AND SETTING: Data from the Tehran lipid and glucose study was used to develop (n = 5291 individuals; phases 1 to 3) and validate (n = 3147 individuals; phases 3 to 6) the dynamic prediction model among individuals aged ≥ 20 years. We used repeated measurements of fasting plasma glucose (FPG) or waist circumference (WC) in the framework of the joint modeling (JM) of longitudinal and time-to-event analysis | ||
| 520 | |a RESULTS: Compared with the Cox which used just baseline data, JM showed the same discrimination, better calibration, and higher clinical usefulness (i.e. with a net benefit considering both true and false positive decisions); all were shown with repeated measurements of FPG/WC. Additionally, in our study, the dynamic models improve the risk reclassification (net reclassification index 33% for FPG and 24% for WC model) | ||
| 520 | |a CONCLUSION: Dynamic prediction models, compared with the static one could yield significant improvements in the prediction of T2DM. The complexity of the dynamic models could be addressed by using decision support systems | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Clinical usefulness | |
| 650 | 4 | |a Dynamic prediction models | |
| 650 | 4 | |a Joint modeling | |
| 650 | 4 | |a Risk classification | |
| 650 | 4 | |a Static prediction model | |
| 650 | 4 | |a Type 2 diabetes | |
| 650 | 7 | |a Blood Glucose |2 NLM | |
| 700 | 1 | |a Khalili, Davood |e verfasserin |4 aut | |
| 700 | 1 | |a Zayeri, Farid |e verfasserin |4 aut | |
| 700 | 1 | |a Azizi, Fereidoun |e verfasserin |4 aut | |
| 700 | 1 | |a Hadaegh, Farzad |e verfasserin |4 aut | |
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