five

Data Sheet 1_Association and predictability of major perioperative cardiovascular adverse events and elevated neutrophil percentage-to-albumin ratio in patients with stable coronary artery disease undergoing non-cardiac surgery.xlsx

收藏
NIAID Data Ecosystem2026-05-10 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_Association_and_predictability_of_major_perioperative_cardiovascular_adverse_events_and_elevated_neutrophil_percentage-to-albumin_ratio_in_patients_with_stable_coronary_artery_disease_undergoing_non-cardiac_surgery_xlsx/30163276
下载链接
链接失效反馈
官方服务:
资源简介:
ObjectiveTo evaluate the utility of the preoperative neutrophil percentage-to-albumin ratio (NPAR) for predicting perioperative major adverse cardiovascular events (MACE) in patients with stable coronary artery disease (SCAD) undergoing non-cardiac surgery. MethodsIn this retrospective cohort study, we included all adult SCAD patients who underwent non-cardiac surgery at the Fourth Affiliated Hospital of Zhejiang University School of Medicine between October 2020 and October 2024. The primary endpoint was the occurrence of MACE during the perioperative period, defined as a composite of all-cause mortality, cardiac arrest, myocardial infarction, heart failure, or stroke occurring intraoperatively or during the postoperative hospital stay. We used multivariable logistic regression to assess the independent association between NPAR and MACE risk. To explore potential nonlinearity, we fitted smooth curves and performed threshold-effect analysis. Mediation analysis quantified the proportion of the NPAR–MACE relationship explained by estimated glomerular filtration rate (eGFR). Incremental predictive value was evaluated by comparing the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) before and after adding NPAR to established risk models. Feature selection was conducted using the Boruta algorithm, and predictive performance was further validated with an XGBoost model interpreted via Shapley Additive Explanations (SHAP). ResultsOf 1,771 patients, 90 (5.1%) experienced MACE. The MACE subgroup had a higher mean NPAR than those without events (19.4 ± 5.3 vs. 15.9 ± 3.5; P < 0.001). Each 1-unit increase in NPAR was associated with a 20% higher risk of MACE (OR 1.20; 95% CI 1.10–1.30). A J-shaped relationship emerged, with an inflection point at NPAR 13.7 (P_threshold = 0.005). eGFR mediated 8.4% of the NPAR–MACE association. NPAR alone yielded an AUC of 0.721. Adding NPAR to the Revised Cardiac Risk Index raised the AUC from 0.679–0.755 (NRI 0.599; IDI 0.035; all P < 0.01). The XGBoost model achieved an AUC of 0.773, and SHAP analysis identified NPAR as the most influential predictor. ConclusionsPreoperative NPAR is an independent, readily available predictor of perioperative MACE in SCAD patients. Incorporation of NPAR into existing risk models significantly enhances predictive accuracy and may inform targeted perioperative management strategies. ResultsOf 1,771 patients, 90 (5.1%) experienced MACE. The MACE subgroup had a higher mean NPAR than those without events (19.4 ± 5.3 vs. 15.9 ± 3.5; P < 0.001). Each 1-unit increase in NPAR was associated with a 20% higher risk of MACE (OR 1.20; 95% CI 1.10–1.30). A J-shaped relationship emerged, with an inflection point at NPAR 13.7 (P_threshold = 0.005). eGFR mediated 8.4% of the NPAR–MACE association. NPAR alone yielded an AUC of 0.721. Adding NPAR to the Revised Cardiac Risk Index raised the AUC from 0.679–0.755 (NRI 0.599; IDI 0.035; all P < 0.01). The XGBoost model achieved an AUC of 0.773, and SHAP analysis identified NPAR as the most influential predictor. ConclusionsPreoperative NPAR is an independent, readily available predictor of perioperative MACE in SCAD patients. Incorporation of NPAR into existing risk models significantly enhances predictive accuracy and may inform targeted perioperative management strategies.
创建时间:
2025-09-19
二维码
社区交流群
二维码
科研交流群
商业服务