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Performance of risk prediction models for diabetic foot ulcer: a meta-analysis.
Guo, Panpan; Tu, Yujie; Liu, Ruiyan; Gao, Zihui; Du, Mengyu; Fu, Yu; Wang, Ying; Yan, Shuxun; Shang, Xin.
Afiliación
  • Guo P; Department of Endocrinology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
  • Tu Y; The 154th Hospital, Xinyang, Henan, China.
  • Liu R; Department of Endocrinology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
  • Gao Z; School of First Clinical, Henan University of Chinese Medicine, Zhengzhou, Henan, China.
  • Du M; School of First Clinical, Henan University of Chinese Medicine, Zhengzhou, Henan, China.
  • Fu Y; School of First Clinical, Henan University of Chinese Medicine, Zhengzhou, Henan, China.
  • Wang Y; Department of Endocrinology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
  • Yan S; Department of Geriatrics, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Shang X; Department of Endocrinology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
PeerJ ; 12: e17770, 2024.
Article en En | MEDLINE | ID: mdl-39035162
ABSTRACT

Background:

The number of prediction models for diabetic foot ulcer (DFU) risk is increasing, but their methodological quality and clinical applicability are uncertain. We conducted a systematic review to assess their performance.

Methods:

We searched PubMed, Cochrane Library, and Embase databases up to 10 February 2024 and extracted relevant information from selected prediction models. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) checklist was used to assess bias risk and applicability. All statistical analyses were conducted in Stata 14.0.

Results:

Initially, 13,562 studies were retrieved, leading to the inclusion of five development and five validation models from eight studies. DFU incidence ranged from 6% to 16.8%, with age and hemoglobin A1C (HbA1c) commonly used as predictive factors. All included studies had a high risk of bias, mainly due to disparities in population characteristics and methodology. In the meta-analysis, we observed area under the curve (AUC) values of 0.78 (95% CI [0.69-0.89]) for development models and 0.84 (95% CI [0.79-0.90]) for validation models.

Conclusion:

DFU risk prediction models show good overall accuracy, but there is a risk of bias. Adherence to the PROBAST checklist is crucial for improving their clinical applicability.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pie Diabético Límite: Humans Idioma: En Revista: PeerJ Año: 2024 Tipo del documento: Article País de afiliación: China 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 Asunto principal: Pie Diabético Límite: Humans Idioma: En Revista: PeerJ Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos