Objective
To establish a nomogram model to predict the risk of contrast-induced acute kidney injury (CI-AKI) by analyzing the risk factors of CI-AKI and to evaluate its effectiveness.
Methods
Retrospectively analyze the clinical data of non-ST-elevation acute coronary syndrome (NSTE-ACS) patients who underwent percutaneous coronary intervention (PCI) in our cardiology department from September 2018 to June 2021. Of these, patients who underwent PCI in an earlier period formed the training cohort (70%; n = 809) for nomogram development, and those who underwent PCI thereafter formed the validation cohort (30%; n = 347) to confirm the model’s performance. The independent risk factors of CI-AKI were determined by LASSO regression and multivariable logistic regression analysis. By using R software from which nomogram models were subsequently generated. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot, and decision curve analysis (DCA), respectively.
Results
The nomogram consisted of six variables: age >75, left ventricular ejection fraction, diabetes mellitus, fibrinogen-to-albumin ratio, high-sensitive C-reactive protein, and lymphocyte count. The C-index of the nomogram is 0.835 (95%
CI
: 0.800–0.871) in the training cohort and 0.767 (95%
CI
: 0.711–0.824) in the validation cohort, respectively. The calibration plots exhibited that the nomogram was in good agreement between prediction and observation in the training and validation cohorts. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility.
Conclusion
The nomogram model established has a good degree of differentiation and accuracy, which is intuitively and individually to screen high-risk groups and has a certain predictive value for the occurrence of CI-AKI in NSTE-ACS patients after PCI.
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.
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.
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