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Association Between Neutrophil Percentage-to-Albumin Ratio and 2-Year Mortality in Patients Undergoing Transcatheter
Serkan Aslan1, Ali Rıza Demir1, Gökhan Demirci1
1Department of Cardiology, University of Health Sciences İstanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, İstanbul, Türkiye.
Background:
Transcatheter aortic valve replacement (TAVR) is the standard therapy for severe aortic stenosis, particularly in elderly patients with comorbidities. Simple biomarkers to predict mid-term mortality are still needed. This study evaluated the prognostic value of the preprocedural neutrophil percentage-to-albumin ratio (NPAR) for 2-year all-cause mortality after TAVR.
Methods:
A total of 618 patients undergoing TAVR between 2013 and 2023 were retrospectively analyzed. NPAR was calculated as neutrophil percentage × 100 / albumin (g/dL), and patients were classified into tertiles. The prognostic role of NPAR was assessed using Cox regression, Kaplan-Meier survival analysis, and receiver operating characteristic curves.
Results:
Baseline characteristics were similar across tertiles, but higher NPAR was associated with elevated inflammation and lower albumin levels. In multivariable Cox analysis, high NPAR independently predicted 2-year mortality (T3 vs. T1: hazard ratio [HR] 2.75, 95% CI 1.77-4.28; P < .001). In a model including both categorical NPAR and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), tertile 3 of NPAR (HR 2.49, 95% CI 1.61-3.85; P < .001) and STS-PROM (HR 1.31, 95% CI 1.12-1.53; P = .001) remained independent predictors, indicating incremental prognostic value of NPAR beyond established surgical risk scores. Kaplan-Meier curves showed the lowest survival in the highest tertile (35.9% mortality at 2 years). Receiver operating characteristic analysis confirmed NPAR had the best discriminatory ability (area under the curve = 0.703).
Conclusion:
Preprocedural NPAR is an independent, low-cost, and readily available biomarker for predicting mid-term mortality after TAVR. Its integration into risk models may improve prediction accuracy and help guide patient management.
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