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Related Concept Videos

Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Related Experiment Video

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Development and Validation of a Risk Prediction Model for New-Onset Atrial Fibrillation in Sepsis.

Ya-Ge Chai1, Yi Gou1, Yun Cong2

  • 1Emergency Trauma Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People's Republic of China.

International Journal of General Medicine
|December 17, 2025
PubMed
Summary

A new model predicts new-onset atrial fibrillation (NOAF) in sepsis patients using Log IL-6, BUN, and HR. This tool aids early risk stratification for improved sepsis patient outcomes.

Keywords:
new-onset atrial fibrillationprediction modelsepsis

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Area of Science:

  • Critical Care Medicine
  • Cardiology
  • Biostatistics

Background:

  • Sepsis patients have a high risk of new-onset atrial fibrillation (NOAF).
  • NOAF in sepsis is associated with increased mortality.
  • Early risk stratification for NOAF in sepsis is crucial.

Purpose of the Study:

  • To develop and validate a predictive model for NOAF in sepsis patients.
  • Identify key risk factors for NOAF in the sepsis population.

Main Methods:

  • Utilized LASSO regression and multivariate logistic regression on 423 sepsis patients (299 training, 124 validation).
  • Constructed a nomogram incorporating independent risk factors.
  • Assessed model performance using AUC, Hosmer-Lemeshow test, calibration curves, DCA, and CIC.

Main Results:

  • Log interleukin-6 (Log IL-6), blood urea nitrogen (BUN), and heart rate (HR) were identified as independent risk factors.
  • The nomogram achieved high discrimination (AUC 0.925 training, 0.866 validation).
  • Demonstrated good calibration and favorable clinical utility via DCA and CIC.

Conclusions:

  • A risk prediction model with Log IL-6, BUN, and HR effectively predicts NOAF in sepsis.
  • The model shows strong discrimination, calibration, and potential for early risk identification.
  • External validation through multicenter, prospective studies is recommended before widespread clinical application.