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Introduction <p>Atrial 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.</p> Objective <p>This 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.</p> Methods <p>This 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.</p> Results <p>We 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.</p> Conclusions <p>This 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.</p> Trial registration <p>The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300075727).</p> Graphical abstract <p></p>

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Machine learning-enhanced prediction model for atrial fibrillation development in patients with concurrent type 2 diabetes and obstructive sleep apnea syndrome: a comorbidity perspective

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月21日

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  • Yanan Xu,
  • Shoupeng Duan,
  • Wenyuan Yin,
  • Yi Yang,
  • Xianlin Zhang,
  • Jun Wang

摘要

Introduction

Atrial 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.

Objective

This 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.

Methods

This 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.

Results

We 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.

Conclusions

This 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 registration

The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300075727).

Graphical abstract