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Machine Learning-Driven Prediction of Comorbidities and Mortality in Adults With Type 1 Diabetes.
Andersen, Jonas Dahl; Stoltenberg, Carsten Wridt; Jensen, Morten Hasselstrøm; Vestergaard, Peter; Hejlesen, Ole; Hangaard, Stine.
Afiliación
  • Andersen JD; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Stoltenberg CW; Steno Diabetes Center North Denmark, Aalborg, Denmark.
  • Jensen MH; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Vestergaard P; Steno Diabetes Center North Denmark, Aalborg, Denmark.
  • Hejlesen O; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Hangaard S; Data Science, Novo Nordisk, Søborg, Denmark.
J Diabetes Sci Technol ; : 19322968241267779, 2024 Aug 02.
Article en En | MEDLINE | ID: mdl-39091237
ABSTRACT

BACKGROUND:

Comorbidities such as cardiovascular disease (CVD) and diabetic kidney disease (DKD) are major burdens of type 1 diabetes (T1D). Predicting people at high risk of developing comorbidities would enable early intervention. This study aimed to develop models incorporating socioeconomic status (SES) to predict CVD, DKD, and mortality in adults with T1D to improve early identification of comorbidities.

METHODS:

Nationwide Danish registry data were used. Logistic regression models were developed to predict the development of CVD, DKD, and mortality within five years of T1D diagnosis. Features included age, sex, personal income, and education. Performance was evaluated by five-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and the precision-recall area under the curve (PR-AUC). The importance of SES was assessed from feature importance plots.

RESULTS:

Of the 6572 included adults (≥21 years) with T1D, 379 (6%) developed CVD, 668 (10%) developed DKD, and 921 (14%) died within the five-year follow-up. The AUROC (±SD) was 0.79 (±0.03) for CVD, 0.61 (±0.03) for DKD, and 0.87 (±0.01) for mortality. The PR-AUC was 0.18 (±0.01), 0.15 (±0.03), and 0.49 (±0.02), respectively. Based on feature importance plots, SES was the most important feature in the DKD model but had minimal impact on models for CVD and mortality.

CONCLUSIONS:

The developed models showed good performance for predicting CVD and mortality, suggesting they could help in the early identification of these outcomes in individuals with T1D. The importance of SES in individual prediction within diabetes remains uncertain.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Diabetes Sci Technol Asunto de la revista: ENDOCRINOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Dinamarca Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Diabetes Sci Technol Asunto de la revista: ENDOCRINOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Dinamarca Pais de publicación: Estados Unidos