ArticleNutrition & metabolism2025
Machine learning-enhanced prediction model for atrial fibrillation development in patients with concurrent type 2 diabetes and obstructive sleep apnea syndrome: a comorbidity perspective.
Article in Nutrition & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Identification of Comorbidities in Obstructive Sleep Apnea Using Diverse Data and a One-Dimensional Convolutional Neural Network.Sensors (Basel, Switzerland) · 2026Observational
- Risk assessment of acute heart failure after endovascular therapy in acute ischemic stroke: a nomogram-based study.Frontiers in cardiovascular medicine · 2026Article
- Risk prediction for long-term cardiovascular events in patients with concurrent hypertension, HFpEF, and unstable angina: a multicenter machine learning-assisted cohort study.Frontiers in endocrinology · 2026Article
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6 authors.
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Abstract
introductionAtrial fibrillation (AF) presents a considerable challenge in patients with Type 2 diabetes and obstructive sleep apnea syndrome (OSAS), as metabolic disturbance plays a role in the pathophysiological mechanisms that underlie arrhythmias.
objectiveThis study aimed to resolve this issue by developing a predictive nomogram using a machine learning algorithm, integrating a comprehensive range of clinical variables, including demographic data, laboratory findings, and sleep monitoring information.
methodsThis multicenter cohort study included patients with Type 2 diabetes who were scheduled for sleep monitoring for OSAS between January 2018 and December 2020. A predictive nomogram was developed using random forests and Cox regression analysis.
resultsWe utilized data from multiple hospitals to construct a development cohort comprising 417 participants and an independent validation cohort consisting of 245 participants. The nomogram was developed using four clinical variables: age, apnea-hypopnea index, triglyceride-glucose (TyG) index, and TyG-body mass index (BMI). The areas under the curve values, derived from 500 bootstrap samples, were 0.862 (95% confidence interval [CI]: 0.813–0.910) for predicting AF in the development group and 0.843 (95% CI: 0.753–0.918) in the independent validation group. The nomogram exhibited excellent calibration, as indicated by the strong concordance between predicted and observed AF incidences at 2-, 3-, and 4-year follow-ups, validated through 500 bootstrap samples. Decision curve analysis further substantiated the clinical utility of the prediction nomogram at these intervals. Furthermore, a user-friendly interface has been developed to enhance usability for clinicians.
conclusionsThis predictive model highlights the critical role of insulin resistance, as evidenced by TyG and TyG-BMI surrogate markers, in the prognostic assessment and early risk stratification of patients with AF over 2-, 3-, and 4-year periods among patients with Type 2 diabetes and OSAS.
trial registrationThe trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300075727).
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