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Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis

BACKGROUND: Many prognostic models of diabetic microvascular complications have been developed, but their performances still varies. Therefore, we conducted a systematic review and meta-analysis to summarise the performances of the existing models. METHODS: Prognostic models of diabetic microvascula...

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Autores principales: Saputro, Sigit Ari, Pattanaprateep, Oraluck, Pattanateepapon, Anuchate, Karmacharya, Swekshya, Thakkinstian, Ammarin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8561867/
https://www.ncbi.nlm.nih.gov/pubmed/34724973
http://dx.doi.org/10.1186/s13643-021-01841-z
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author Saputro, Sigit Ari
Pattanaprateep, Oraluck
Pattanateepapon, Anuchate
Karmacharya, Swekshya
Thakkinstian, Ammarin
author_facet Saputro, Sigit Ari
Pattanaprateep, Oraluck
Pattanateepapon, Anuchate
Karmacharya, Swekshya
Thakkinstian, Ammarin
author_sort Saputro, Sigit Ari
collection PubMed
description BACKGROUND: Many prognostic models of diabetic microvascular complications have been developed, but their performances still varies. Therefore, we conducted a systematic review and meta-analysis to summarise the performances of the existing models. METHODS: Prognostic models of diabetic microvascular complications were retrieved from PubMed and Scopus up to 31 December 2020. Studies were selected, if they developed or internally/externally validated models of any microvascular complication in type 2 diabetes (T2D). RESULTS: In total, 71 studies were eligible, of which 32, 30 and 18 studies initially developed prognostic model for diabetic retinopathy (DR), chronic kidney disease (CKD) and end stage renal disease (ESRD) with the number of derived equations of 84, 96 and 51, respectively. Most models were derived-phases, some were internal and external validations. Common predictors were age, sex, HbA1c, diabetic duration, SBP and BMI. Traditional statistical models (i.e. Cox and logit regression) were mostly applied, otherwise machine learning. In cohorts, the discriminative performance in derived-logit was pooled with C statistics of 0.82 (0.73‑0.92) for DR and 0.78 (0.74‑0.83) for CKD. Pooled Cox regression yielded 0.75 (0.74‑0.77), 0.78 (0.74‑0.82) and 0.87 (0.84‑0.89) for DR, CKD and ESRD, respectively. External validation performances were sufficiently pooled with 0.81 (0.78‑0.83), 0.75 (0.67‑0.84) and 0.87 (0.85‑0.88) for DR, CKD and ESRD, respectively. CONCLUSIONS: Several prognostic models were developed, but less were externally validated. A few studies derived the models by using appropriate methods and were satisfactory reported. More external validations and impact analyses are required before applying these models in clinical practice. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018105287 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-021-01841-z.
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spelling pubmed-85618672021-11-03 Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis Saputro, Sigit Ari Pattanaprateep, Oraluck Pattanateepapon, Anuchate Karmacharya, Swekshya Thakkinstian, Ammarin Syst Rev Research BACKGROUND: Many prognostic models of diabetic microvascular complications have been developed, but their performances still varies. Therefore, we conducted a systematic review and meta-analysis to summarise the performances of the existing models. METHODS: Prognostic models of diabetic microvascular complications were retrieved from PubMed and Scopus up to 31 December 2020. Studies were selected, if they developed or internally/externally validated models of any microvascular complication in type 2 diabetes (T2D). RESULTS: In total, 71 studies were eligible, of which 32, 30 and 18 studies initially developed prognostic model for diabetic retinopathy (DR), chronic kidney disease (CKD) and end stage renal disease (ESRD) with the number of derived equations of 84, 96 and 51, respectively. Most models were derived-phases, some were internal and external validations. Common predictors were age, sex, HbA1c, diabetic duration, SBP and BMI. Traditional statistical models (i.e. Cox and logit regression) were mostly applied, otherwise machine learning. In cohorts, the discriminative performance in derived-logit was pooled with C statistics of 0.82 (0.73‑0.92) for DR and 0.78 (0.74‑0.83) for CKD. Pooled Cox regression yielded 0.75 (0.74‑0.77), 0.78 (0.74‑0.82) and 0.87 (0.84‑0.89) for DR, CKD and ESRD, respectively. External validation performances were sufficiently pooled with 0.81 (0.78‑0.83), 0.75 (0.67‑0.84) and 0.87 (0.85‑0.88) for DR, CKD and ESRD, respectively. CONCLUSIONS: Several prognostic models were developed, but less were externally validated. A few studies derived the models by using appropriate methods and were satisfactory reported. More external validations and impact analyses are required before applying these models in clinical practice. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018105287 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-021-01841-z. BioMed Central 2021-11-01 /pmc/articles/PMC8561867/ /pubmed/34724973 http://dx.doi.org/10.1186/s13643-021-01841-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Saputro, Sigit Ari
Pattanaprateep, Oraluck
Pattanateepapon, Anuchate
Karmacharya, Swekshya
Thakkinstian, Ammarin
Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis
title Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis
title_full Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis
title_fullStr Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis
title_full_unstemmed Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis
title_short Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis
title_sort prognostic models of diabetic microvascular complications: a systematic review and meta-analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8561867/
https://www.ncbi.nlm.nih.gov/pubmed/34724973
http://dx.doi.org/10.1186/s13643-021-01841-z
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