Abdul Kerim Buğra

Background/Objectives: Reoperative (redo) cardiac surgery carries elevated risk, yet determinants and mechanisms of early death in valve- and endocarditis-predominant cohorts are poorly described. We aimed to identify preoperative determinants of early mortality, derive a clinically usable risk model, examine the mechanisms of death, and evaluate EuroSCORE II. Methods: We analysed all redo cardiac operations through repeat sternotomy at a single tertiary centre (December 2010–December 2025); the cohort comprised 821 patients with verified in-hospital mortality status. Independent preoperative predictors were identified by multivariable logistic regression. Discrimination and calibration of the model and of EuroSCORE II were compared, and procedural complexity, graded low-cardiac-output severity, postoperative morbidity, and failure-to-rescue were examined. Results: The early mortality rate was 22.8% (187/821). The independent preoperative predictors were chronic kidney disease (adjusted odds ratio 2.25), age ≥70 years (2.32), active endocarditis (1.89), advanced functional class (1.78), non-elective status (1.69), and systolic pulmonary artery pressure (1.15 per 10 mmHg); the model discriminated and calibrated well (concordance statistic 0.755). EuroSCORE II discriminated similarly but systematically under-predicted mortality (observed-to-expected ratio 1.46; worst 2.06 at intermediate risk). Mortality rose with procedural complexity (17.3% to 37.3%) and graded haemodynamic support (3.4% to 79.1%). Failure-to-rescue was 38.5%, compared with 5.8% mortality in patients without a major complication. A standard-risk subgroup (n = 209) had 12.4% mortality. Conclusions: Early mortality after redo cardiac surgery is driven by identifiable preoperative comorbidity, procedural complexity, and low-cardiac-output physiology, and is concentrated in failure-to-rescue. A parsimonious preoperative model predicted mortality well; EuroSCORE II, although discriminating, systematically under-predicted risk, supporting redo-specific assessment and recalibration.

Aims: This study aimed to systematically evaluate the clinical accuracy and patient accessibility of ChatGPT-4-generated cardiovascular surgery patient education content across Turkish and English languages, examining language-dependent performance patterns and their implications for clinical practice.Methods: Twelve cardiovascular surgery specialists with minimum 5 years clinical experience conducted blinded evaluation of 52 ChatGPT-4 responses (26 Turkish, 26 English), generating 624 total expert assessments, across six cardiovascular surgery patient education domains using validated 7-point Likert scales. Simultaneously, computational accessibility analysis was performed using language-specific readability indices. Statistical analysis included effect size calculations and correlation analysis.Results: ChatGPT-4 demonstrated high clinical accuracy across both languages (mean satisfaction: 6.40/7.0, 91.4%), with 88.7% of responses receiving high expert ratings. However, profound language-dependent accessibility disparities emerged; Turkish responses achieved near-universal patient accessibility (mean score: 77.7±4.2), while English responses required university-level education (mean score: 30.2±6.1), creating a 47.5-point accessibility gap (Cohen’s d=9.07, p