ArticleAnnals of medicine2023
Novel model predicts diastolic cardiac dysfunction in type 2 diabetes.
Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review.Journal of diabetes · 2025Pooled it
- Age-Related Characteristics of Diastolic Dysfunction in Type 2 Diabetes Patients.Journal of clinical medicine · 2025Article
- T2D-LVDD: neural network-based predictive models for left ventricular diastolic dysfunction in type 2 diabetes.Diabetology & metabolic syndrome · 2025Article
- Novel Model Predicts Type 2 Diabetes Mellitus Patients Complicated With Metabolic Syndrome Using Retrospective Dataset From First Affiliated Hospital of Shenzhen University, China.International journal of endocrinology · 2025Article
- Predicting diabetic cardiomyopathy in type 2 diabetes: development and validation of a nomogram based on clinical and echocardiographic parameters.Frontiers in endocrinology · 2025Article
- Lysophosphatidylcholine trigger myocardial injury in diabetic cardiomyopathy via the TLR4/ZNF480/AP-1/NF-kB pathway.Heliyon · 2024Review
- Development and Validation of a Nomogram for Predicting Suicidal Ideation Among Rural Adolescents in China.Psychology research and behavior management · 2024Article
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Authors and funding
9 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveDiabetes mellitus complicated with heart failure has high mortality and morbidity, but no reliable diagnoses and treatments are available. This study aimed to develop and verify a new model nomogram based on clinical parameters to predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus (T2DM).
methods3030 patients with T2DM underwent Doppler echocardiography at the First Affiliated Hospital of Shenzhen University between January 2014 and December 2021. The patients were divided into the training dataset (
resultsA multivariable model that included age, body mass index (BMI), triglyceride (TG), creatine phosphokinase isoenzyme (CK-MB), serum sodium (Na), and urinary albumin/creatinine ratio (UACR) was presented as the nomogram. We obtained the model for estimating diastolic cardiac dysfunction in patients with T2DM. The AUC-ROC of the training dataset in our model was 0.8307, with 95% CI of 0.8109-0.8505. Similar to the results obtained with the training dataset, the AUC-ROC of the verification dataset in our model was 0.8083, with 95% CI of 0.7843-0.8324, thus demonstrating robust. The function of the predictive model was as follows: Diastolic Dysfunction = -4.41303 + 0.14100*Age(year)+0.10491*BMI (kg/m
conclusionDiastolic cardiac dysfunction in patients with T2DM can be predicted by clinical parameters. Our prediction model may represent an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in T2DM patients and provide a reliable method for early screening of T2DM patients with cardiac complications.KEY MESSAGESThis study used clinical parameters to predict diastolic cardiac dysfunction in patients with T2DM. This study established a nomogram for predicting diastolic cardiac dysfunction by multivariate logical regression analysis. Our predictive model can be used as an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in patients with T2DM and provides a reliable method for early screening of cardiac complications in patients with T2DM.
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