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Technique and Patient Selection Criteria of Right Anterior Mini-Thoracotomy for Minimal Access Aortic Valve Replacement

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Risk Stratification for Postoperative Mortality in Cardiac Surgery: "Quo Vadis"?

Radu-Alexandru Iacobescu^1,2, Tiberiu Lunguleac^1,2, Sabina Antoniu^1,3

¹Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania.

Medicina (Kaunas, Lithuania)

|March 28, 2026

Summary

This summary is machine-generated.

Assessing immediate mortality risk in adult cardiac surgery is crucial. While current tools have limitations, machine learning shows promise for improved prediction, though further validation is needed.

Keywords:

cardiac surgery machine learning mortality risk risk prediction modeling surgery mortality surgical risk

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Area of Science:

Cardiology
Medical Informatics
Surgical Risk Assessment

Background:

Accurate preoperative risk assessment is essential for adult cardiac surgery patients.
Existing risk stratification tools often lack precision and can lead to miscalibration.

Purpose of the Study:

To review current cardiac surgery risk assessment tools.
To explore novel predictors and advanced methodologies like machine learning for improved mortality prediction.

Main Methods:

Narrative review of recent literature on cardiac surgery risk assessment.
Examination of traditional predictors and artificial intelligence-based models.

Main Results:

Common tools (Parsonnet, EuroSCORE II, STS-PROM, ACEF) show moderate accuracy but variable calibration.
Factors like inflammation, frailty, and functional status may enhance traditional models.
AI models demonstrate superior predictive power but require external validation and broader accessibility.

Conclusions:

Current risk assessment tools in cardiac surgery require improvement.
Machine learning offers significant potential but needs further development and validation before clinical implementation.