Yılmaz Cemalettin, Karaduman Ahmet, Tiryaki Muhammet Mücahit, Güvendi Şengör Büşra, Unkun Tuba, Kültürsay Barkın, Zehir Regayip
Department of Cardiology, Malazgirt State Hospital, Malazgirt, Mus, Turkey.
Department of Cardiology, Kartal Kosuyolu Research and Education Hospital, Kartal, Istanbul, Turkey.
Biomark Med. 2025 Jan;19(1):13-22. doi: 10.1080/17520363.2024.2443383. Epub 2024 Dec 22.
No-reflow is a critical adverse event associated with percutaneous coronary intervention (PCI), particularly during saphenous vein graft (SVG) procedures. The Naples Prognostic Score (NPS) reflects inflammatory status, but its relationship with no-reflow remains unclear. Therefore, we aimed to evaluate the relationship between NPS and no-reflow occurrence following SVG PCI.
We retrospectively analyzed 286 patients who underwent SVG PCI from January 2020 to January 2024, with a median age of 65 years and 85.7% male. Participants were categorized into low NPS (0-2, 48.6%) and high NPS (3-4, 51.4%) groups. Two nested models were developed by adding NPS (continuous and categorical, respectively) to the base model.
Higher no-reflow rates were noted in the high-NPS group (48.5% vs. 9.5%, < 0.05). Multivariable regression revealed that a higher NPS significantly increased no-reflow risk, with odds ratios of 5.966 (95% CI: 3.066-11.611) for continuous NPS and 10.110 (95% CI: 3.194-32.002) for categorical NPS. Adding NPS to the base model significantly improved predictive performance (likelihood-ratio test < 0.001). Model 1 demonstrated the best performance (X : 84.857, R : 0.468) and discriminative ability (AUC: 0.888).
Our findings suggest that NPS is a strong predictor of no-reflow following SVG PCI.
无复流是经皮冠状动脉介入治疗（PCI）相关的严重不良事件，尤其是在大隐静脉桥血管（SVG）手术过程中。那不勒斯预后评分（NPS）反映炎症状态，但其与无复流的关系仍不清楚。因此，我们旨在评估NPS与SVG PCI术后无复流发生之间的关系。
我们回顾性分析了2020年1月至2024年1月期间接受SVG PCI的286例患者，中位年龄为65岁，男性占85.7%。参与者被分为低NPS组（0 - 2分，48.6%）和高NPS组（3 - 4分，51.4%）。通过分别将NPS（连续变量和分类变量）添加到基础模型中，建立了两个嵌套模型。
高NPS组的无复流率更高（48.5%对9.5%，<0.05）。多变量回归显示，较高的NPS显著增加无复流风险，连续NPS的优势比为5.966（95%置信区间：3.066 - 11.611），分类NPS的优势比为10.110（95%置信区间：3.194 - 32.002）。将NPS添加到基础模型中显著提高了预测性能（似然比检验<0.001）。模型1表现出最佳性能（X²：84.857，R²：0.468）和判别能力（AUC：0.888）。
我们的研究结果表明，NPS是SVG PCI术后无复流的有力预测指标。
