Cargando…
A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes
BACKGROUND: Accurate classification of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in the early phase is crucial for individual precision treatment. This study aimed to develop a classification model having fewer and easier to access clinical variables to distinguish T1DM in newly diagnosed di...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8033361/ https://www.ncbi.nlm.nih.gov/pubmed/33842630 http://dx.doi.org/10.21037/atm-20-7115 |
_version_ | 1783676397869334528 |
---|---|
author | Tang, Xiaohan Tang, Rui Sun, Xingzhi Yan, Xiang Huang, Gan Zhou, Houde Xie, Guotong Li, Xia Zhou, Zhiguang |
author_facet | Tang, Xiaohan Tang, Rui Sun, Xingzhi Yan, Xiang Huang, Gan Zhou, Houde Xie, Guotong Li, Xia Zhou, Zhiguang |
author_sort | Tang, Xiaohan |
collection | PubMed |
description | BACKGROUND: Accurate classification of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in the early phase is crucial for individual precision treatment. This study aimed to develop a classification model having fewer and easier to access clinical variables to distinguish T1DM in newly diagnosed diabetes in adults. METHODS: Clinical and laboratory data were collected from 15,206 adults with newly diagnosed diabetes in this cross-sectional study. This cohort represented 20 provinces and 4 municipalities in China. Types of diabetes were determined based on postprandial C-peptide (PCP) level and glutamic acid decarboxylase autoantibody (GADA) titer. We developed multivariable clinical diagnostic models using the eXtreme Gradient Boosting (XGBoost) algorithm. Classification variables included in the final model were based on their scores of importance. Model performance was evaluated by area under the receiver operating characteristic curve (ROC AUC), sensitivity, and specificity. The performance of models with different variable combinations was compared. Calibration intercept and slope were evaluated for the final model. RESULTS: Among the newly diagnosed diabetes cohort, 1,465 (9.63%) persons had T1DM and 13,741 (90.37%) had T2DM. Body mass index (BMI) contributed the most to the model, followed by age of onset and hemoglobin A1c (HbA1c). Compared with models with other clinical variable combinations, a final model that integrated age of onset, BMI and HbA1c had relatively higher performance. The ROC AUC, sensitivity, and specificity for this model were 0.83 (95% CI, 0.80 to 0.85), 0.77, and 0.76, respectively. The calibration intercept and slope were 0.02 (95% CI, –0.03 to 0.06) and 0.90 (95% CI, 0.79 to 1.02), respectively, which suggested a good calibration performance. CONCLUSIONS: Our classification model that integrated age of onset, BMI, and HbA1c could distinguish T1DM from T2DM, which provides a useful tool in assisting physicians in subtyping and precising treatment in diabetes. |
format | Online Article Text |
id | pubmed-8033361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-80333612021-04-09 A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes Tang, Xiaohan Tang, Rui Sun, Xingzhi Yan, Xiang Huang, Gan Zhou, Houde Xie, Guotong Li, Xia Zhou, Zhiguang Ann Transl Med Original Article BACKGROUND: Accurate classification of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in the early phase is crucial for individual precision treatment. This study aimed to develop a classification model having fewer and easier to access clinical variables to distinguish T1DM in newly diagnosed diabetes in adults. METHODS: Clinical and laboratory data were collected from 15,206 adults with newly diagnosed diabetes in this cross-sectional study. This cohort represented 20 provinces and 4 municipalities in China. Types of diabetes were determined based on postprandial C-peptide (PCP) level and glutamic acid decarboxylase autoantibody (GADA) titer. We developed multivariable clinical diagnostic models using the eXtreme Gradient Boosting (XGBoost) algorithm. Classification variables included in the final model were based on their scores of importance. Model performance was evaluated by area under the receiver operating characteristic curve (ROC AUC), sensitivity, and specificity. The performance of models with different variable combinations was compared. Calibration intercept and slope were evaluated for the final model. RESULTS: Among the newly diagnosed diabetes cohort, 1,465 (9.63%) persons had T1DM and 13,741 (90.37%) had T2DM. Body mass index (BMI) contributed the most to the model, followed by age of onset and hemoglobin A1c (HbA1c). Compared with models with other clinical variable combinations, a final model that integrated age of onset, BMI and HbA1c had relatively higher performance. The ROC AUC, sensitivity, and specificity for this model were 0.83 (95% CI, 0.80 to 0.85), 0.77, and 0.76, respectively. The calibration intercept and slope were 0.02 (95% CI, –0.03 to 0.06) and 0.90 (95% CI, 0.79 to 1.02), respectively, which suggested a good calibration performance. CONCLUSIONS: Our classification model that integrated age of onset, BMI, and HbA1c could distinguish T1DM from T2DM, which provides a useful tool in assisting physicians in subtyping and precising treatment in diabetes. AME Publishing Company 2021-03 /pmc/articles/PMC8033361/ /pubmed/33842630 http://dx.doi.org/10.21037/atm-20-7115 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Tang, Xiaohan Tang, Rui Sun, Xingzhi Yan, Xiang Huang, Gan Zhou, Houde Xie, Guotong Li, Xia Zhou, Zhiguang A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes |
title | A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes |
title_full | A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes |
title_fullStr | A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes |
title_full_unstemmed | A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes |
title_short | A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes |
title_sort | clinical diagnostic model based on an extreme gradient boosting algorithm to distinguish type 1 diabetes |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8033361/ https://www.ncbi.nlm.nih.gov/pubmed/33842630 http://dx.doi.org/10.21037/atm-20-7115 |
work_keys_str_mv | AT tangxiaohan aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT tangrui aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT sunxingzhi aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT yanxiang aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT huanggan aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT zhouhoude aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT xieguotong aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT lixia aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT zhouzhiguang aclinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT tangxiaohan clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT tangrui clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT sunxingzhi clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT yanxiang clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT huanggan clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT zhouhoude clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT xieguotong clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT lixia clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes AT zhouzhiguang clinicaldiagnosticmodelbasedonanextremegradientboostingalgorithmtodistinguishtype1diabetes |