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Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study

Leveraging easily accessible data from hospitals to identify high-risk mortality rates for clinical diabetes care adjustment is a convenient method for the future of precision healthcare. We aimed to develop risk prediction models for all-cause mortality based on 7-year and 10-year follow-ups for ty...

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Autores principales: Chiu, Sherry Yueh-Hsia, Chen, Ying Isabel, Lu, Juifen Rachel, Ng, Soh-Ching, Chen, Chih-Hung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537078/
https://www.ncbi.nlm.nih.gov/pubmed/34682901
http://dx.doi.org/10.3390/jcm10204779
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author Chiu, Sherry Yueh-Hsia
Chen, Ying Isabel
Lu, Juifen Rachel
Ng, Soh-Ching
Chen, Chih-Hung
author_facet Chiu, Sherry Yueh-Hsia
Chen, Ying Isabel
Lu, Juifen Rachel
Ng, Soh-Ching
Chen, Chih-Hung
author_sort Chiu, Sherry Yueh-Hsia
collection PubMed
description Leveraging easily accessible data from hospitals to identify high-risk mortality rates for clinical diabetes care adjustment is a convenient method for the future of precision healthcare. We aimed to develop risk prediction models for all-cause mortality based on 7-year and 10-year follow-ups for type 2 diabetes. A total of Taiwanese subjects aged ≥18 with outpatient data were ascertained during 2007–2013 and followed up to the end of 2016 using a hospital-based prospective cohort. Both traditional model selection with stepwise approach and LASSO method were conducted for parsimonious models’ selection and comparison. Multivariable Cox regression was performed for selected variables, and a time-dependent ROC curve with an integrated AUC and cumulative mortality by risk score levels was employed to evaluate the time-related predictive performance. The prediction model, which was composed of eight influential variables (age, sex, history of cancers, history of hypertension, antihyperlipidemic drug use, HbA1c level, creatinine level, and the LDL /HDL ratio), was the same for the 7-year and 10-year models. Harrell’s C-statistic was 0.7955 and 0.7775, and the integrated AUCs were 0.8136 and 0.8045 for the 7-year and 10-year models, respectively. The predictive performance of the AUCs was consistent with time. Our study developed and validated all-cause mortality prediction models with 7-year and 10-year follow-ups that were composed of the same contributing factors, though the model with 10-year follow-up had slightly greater risk coefficients. Both prediction models were consistent with time.
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spelling pubmed-85370782021-10-24 Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study Chiu, Sherry Yueh-Hsia Chen, Ying Isabel Lu, Juifen Rachel Ng, Soh-Ching Chen, Chih-Hung J Clin Med Article Leveraging easily accessible data from hospitals to identify high-risk mortality rates for clinical diabetes care adjustment is a convenient method for the future of precision healthcare. We aimed to develop risk prediction models for all-cause mortality based on 7-year and 10-year follow-ups for type 2 diabetes. A total of Taiwanese subjects aged ≥18 with outpatient data were ascertained during 2007–2013 and followed up to the end of 2016 using a hospital-based prospective cohort. Both traditional model selection with stepwise approach and LASSO method were conducted for parsimonious models’ selection and comparison. Multivariable Cox regression was performed for selected variables, and a time-dependent ROC curve with an integrated AUC and cumulative mortality by risk score levels was employed to evaluate the time-related predictive performance. The prediction model, which was composed of eight influential variables (age, sex, history of cancers, history of hypertension, antihyperlipidemic drug use, HbA1c level, creatinine level, and the LDL /HDL ratio), was the same for the 7-year and 10-year models. Harrell’s C-statistic was 0.7955 and 0.7775, and the integrated AUCs were 0.8136 and 0.8045 for the 7-year and 10-year models, respectively. The predictive performance of the AUCs was consistent with time. Our study developed and validated all-cause mortality prediction models with 7-year and 10-year follow-ups that were composed of the same contributing factors, though the model with 10-year follow-up had slightly greater risk coefficients. Both prediction models were consistent with time. MDPI 2021-10-18 /pmc/articles/PMC8537078/ /pubmed/34682901 http://dx.doi.org/10.3390/jcm10204779 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chiu, Sherry Yueh-Hsia
Chen, Ying Isabel
Lu, Juifen Rachel
Ng, Soh-Ching
Chen, Chih-Hung
Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study
title Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study
title_full Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study
title_fullStr Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study
title_full_unstemmed Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study
title_short Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study
title_sort developing a prediction model for 7-year and 10-year all-cause mortality risk in type 2 diabetes using a hospital-based prospective cohort study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537078/
https://www.ncbi.nlm.nih.gov/pubmed/34682901
http://dx.doi.org/10.3390/jcm10204779
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