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Construction of a nomogram risk prediction model for prolonged mechanical ventilation in patients following surgery for acute type A aortic dissection

  • 0Department of Cardiac Surgery, Nanjing Drum Tower Hospital, Nanjing, China.
Frontiers in Cardiovascular Medicine +

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March 28, 2024

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March 28, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies key risk factors for prolonged mechanical ventilation (PMV) after acute type A aortic dissection (ATAAD) surgery. A validated predictive model aids clinicians in anticipating and managing PMV risks in these complex patients.

Area Of Science

  • Cardiovascular Surgery
  • Thoracic Surgery
  • Critical Care Medicine

Background

  • Acute type A aortic dissection (ATAAD) is a life-threatening condition requiring surgical intervention.
  • Prolonged mechanical ventilation (PMV) is a significant complication following ATAAD surgery, associated with increased morbidity and mortality.
  • Identifying risk factors for PMV is crucial for optimizing patient outcomes.

Purpose Of The Study

  • To analyze risk factors associated with prolonged mechanical ventilation (PMV) in patients undergoing surgical treatment for ATAAD.
  • To construct and validate a predictive model for assessing PMV risk in this patient population.
  • To enhance clinical decision-making for timely interventions.

Main Methods

  • A cohort of 452 ATAAD patients undergoing surgery was analyzed.
  • Patients were divided into PMV (n=132) and non-PMV (n=320) groups.
  • Logistic regression identified risk factors; a predictive model was built and validated using ROC curves, calibration curves, and DCA.

Main Results

  • Significant risk factors for PMV included age, BMI, preoperative WBC, creatinine, cerebral hypoperfusion, and cardiopulmonary bypass time.
  • The predictive model demonstrated good discrimination (AUC=0.856) and strong clinical utility.
  • Model calibration and goodness-of-fit tests confirmed its accuracy.

Conclusions

  • A robust risk prediction model for PMV in ATAAD surgery patients has been developed.
  • The model offers valuable insights for predicting PMV likelihood.
  • It supports personalized intervention strategies to improve patient care and outcomes.

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