Objective Our aim was to assess systemic immune-inflammation index (SII) and NT-proBNP value either in singly or in combination to predict acute ST-elevation myocardial infarction (STEMI) patient prognosis. Methods Analyzed retrospectively the clinical features and laboratory data of STEMI confirmed patients in our hospital from January to December 2020. The levels of SII and NT-proBNP were detected. The Kaplan-Meier approach and Spearman’s rank correlation coefficient were used to construct the overall major adverse cardiac event (MACE) curve. Multivariate Cox regression analysis was applied to detect MACE predictors. In addition, the Delong test and receiver operating characteristic (ROC) curve analyzed each factor performance on its own and composite multivariate index to predict MACEs. Results The MACE group showed statistically significant differences in SII, NT- proBNP in comparison to the non-MACE group ( P =0.003, P <0.001). Based on Kaplan-Meier analysis, SII and NT-proBNP showed positive correlation with MACE (log-rank P < 0.001). SII and NT-proBNP were independent predicting factors for long-term MACEs in multivariate Cox regression analysis ( P <0.001, HR : 2.952, 95% CI 1.565–5.566; P <0.001, HR : 2.112, 95% CI 1.662–2.683). SII and NT-proBNP exhibited a positive correlation ( R = 0.187, P < 0.001) in correlation analysis. According to the ROC statistical analysis, the combination exhibited 78.0% sensitivity and 88.0% specificity in the prediction of MACE. According to the results of the AUC and Delong test, the combined SII and NT-proBNP performed better as a prognostic index than each of the individual factor indexes separately ( Z = 2.622, P = 0.009; Z = 3.173, P < 0.001). Conclusion SII and NT-proBNP were independent indicators of clinical prognosis in acute STEMI patients, and they correlated positively. These factors could be combined to improve clinical prognosis.
Objective To investigate the relationship between the incidence of contrast-induced acute kidney injury (CI-AKI) and the levels of the systemic immune-inflammatory index (SII, platelet × neutrophil/lymphocyte ratio) and high-sensitivity C-reactive protein (hsCRP) in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI), to analyze further the predictive value of the combination of SII and hsCRP for CI-AKI. Methods Retrospectively analyze the clinical data of STEMI patients who underwent PCI in our cardiology department from November 2019 to March 2021. Restricted cubic splines were used to determine the correlation between SII and hsCRP and the risk of CI-AKI. Patients were divided into the CI-AKI group (n=71) and the non-CI-AKI group (n=344) according to postoperative creatinine changes. Logistic regression was used to analyze the factors influencing CI-AKI. ROC curves were used to evaluate the predictive value of SII, hsCRP, and their combined levels on CI-AKI. Results Restricted cubic spline analysis showed that when SII>653.73×10 9 /L and hsCRP>5.52mg/dl, there was a positive correlation with the incidence of CI-AKI. And the incidence of CI-AKI rose with the inflammation status. The receiver operating characteristic curve of SII combined with hsCRP was 0.831, which was higher than SII or hsCRP alone. The logistic regression analysis showed that high-risk factors of CI-AKI were diabetes mellitus, platelet count, and highly elevated SII and hsCRP. Conclusion Within a certain range, elevated inflammatory biomarkers SII and hsCRP were risk factors for CI-AKI after PCI in patients with STEMI. This study suggests that the combination of SII and hsCRP predicts the risk of CI-AKI more accurately than either biomarker alone.
Purpose Development and validation of a nomogram model to predict the risk of Contrast-Induced Acute Kidney Injury (CI-AKI) after emergency percutaneous coronary intervention (PCI) in elderly patients with acute ST-segment elevation myocardial infarction (STEMI). Patients and Methods Retrospective analysis of 542 elderly (≥65 years) STEMI patients undergoing emergency PCI in our hospital from January 2019 to June 2022, with all patients randomized to the training cohort (70%; n=380) and the validation cohort (30%; n=162). Univariate analysis, LASSO regression, and multivariate logistic regression analysis were used to determine independent risk factors for developing CI-AKI in elderly STEMI patients. R software is used to generate a nomogram model. The predictive power of the nomogram model was compared with the Mehran score 2. The area under the ROC curve (AUC), calibration curves, and decision curve analysis (DCA) was used to evaluate the prediction model’s discrimination, calibration, and clinical validity, respectively. Results The nomogram model consisted of five variables: diabetes mellitus (DM), left ventricular ejection fraction (LVEF), Systemic immune-inflammatory index (SII), N-terminal pro-brain natriuretic peptide (NT-proBNP), and highly sensitive C-reactive protein(hsCRP). In the training cohort, the AUC is 0.84 (95% CI: 0.790–0.890), and in the validation cohort, it is 0.844 (95% CI: 0.762–0.926). The nomogram model has better predictive ability than Mehran score 2. Based on the calibration curves, the predicted and observed values of the nomogram model were in good agreement between the training and validation cohort. Decision curve analysis (DCA) and clinical impact curve showed that the nomogram prediction model has good clinical utility. Conclusion The established nomogram model can intuitively and specifically screen high-risk groups with a high degree of discrimination and accuracy and has a specific predictive value for CI-AKI occurrence in elderly STEMI patients after PCI.
Aim: To investigate the relationship between the incidence of contrast-induced acute kidney injury (CI-AKI) and the level of small dense low-density lipoprotein (sd-LDL) and systemic immune-inflammation index (SII) in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing emergency percutaneous coronary intervention (PCI), and to further compare the predictive values of SII, sd-LDL and their combination for CI-AKI. Methods: A total of 674 patients were assigned to a training and a validation cohort according to their chronological sequence. The baseline characteristics of the 450 patients in the training cohort were considered as candidate univariate predictors of CI-AKI. Multivariate logistic regression was then used to identify predictors of CI-AKI and develop a prediction model. The predictive values of SII, sd-LDL and their combination for CI-AKI were also evaluated. Results: Multivariate logistic regression analysis showed that age, left ventricular ejection fraction (LVEF), sd-LDL, uric acid, estimated glomerular filtration rate (eGFR) and SII were predictors of CI-AKI. The area under the curve (AUC) of the prediction model based on the above factors was 0.846 [95% confidence interval (CI) 0.808–0.884], and the Hosmer-Lemeshow test (P = 0.587, χ2 = 6.543) proved the goodness of fit of the model. The AUC combining SII with sd-LDL to predict CI-AKI was 0.785 (95% CI 0.735–0.836), with a sensitivity of 72.8% and a specificity of 79.8%, and was statistically significant when compared with SII and sd-LDL, respectively. The predictive efficiency of combining SII with sd-LDL and SII were evaluated by improved net reclassification improvement (NRI, 0.325, P < 0.001) and integrated discrimination improvement (IDI, 0.07, P < 0.001). Conclusions: Both SII and sd-LDL can be used as predictors of CI-AKI in STEMI patients undergoing emergency PCI, and their combination can provide more useful value for early assessment of CI-AKI.
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