Related Experiment Video
Updated: Sep 16, 2025
Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
Published on: September 22, 2020
Development and validation of an AMR-based predictive model for post-PCI contrast-induced nephropathy in patients
Zhaokai Wang1, Shuping Yang2, Cheng Li1
1Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Insights
This study developed a nomogram using angiography-derived microcirculatory resistance index (AMR) to predict contrast-induced nephropathy (CIN) in ST-segment elevation myocardial infarction (STEMI) patients undergoing PCI. The nomogram demonstrated good predictive accuracy, aiding clinical decision-making.
Area of Science:
- Cardiology
- Nephrology
- Medical Imaging
Background:
- Contrast-induced nephropathy (CIN) is a risk following percutaneous coronary intervention (PCI).
- Predictive tools for CIN in ST-segment elevation myocardial infarction (STEMI) patients are crucial for optimizing treatment.
- Angiography-derived microcirculatory resistance index (AMR) has potential as a predictive biomarker.
Purpose of the Study:
- To develop and validate a nomogram for predicting CIN probability after PCI in STEMI patients.
- To utilize AMR as a key component in the predictive model.
- To assess the clinical utility of the developed nomogram.
Main Methods:
- Development and validation of a nomogram using logistic regression and LASSO analysis.
- Inclusion of 595 STEMI patients in the training cohort and 256 in the validation cohort.
- Performance evaluation using AUC-ROC, calibration plots, and decision curve analysis (DCA).
Main Results:
- Multifactorial logistic regression identified eGFR, AMR, UHR, TyG index, and contrast agent dosage as independent predictors of CIN.
- The nomogram achieved good predictive performance in both training (AUC: 0.881) and validation (AUC: 0.841) cohorts.
- Calibration plots and DCA confirmed the nomogram's accuracy and clinical feasibility.
Conclusions:
- The developed AMR-based nomogram accurately predicts CIN risk in STEMI patients undergoing PCI.
- This tool can assist clinicians in optimizing treatment strategies and improving patient prognosis.
- The nomogram offers a valuable approach for personalized risk assessment and management of CIN.
Background:
This study aimed to develop and validate an angiography-derived microcirculatory resistance index (AMR)- based nomogram to predict the probability of contrast-induced nephropathy (CIN) following percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI).
Method:
In this study, 595 STEMI patients from the Affiliated Hospital of Xuzhou Medical University from January 1, 2022 to December 31, 2023 were included as the training cohort, and 256 patients from the East Hospital of Xuzhou Medical University were included as the validation cohort. Independent risk factors for the development of nomogram were identified using univariate logistic regression, randomized forest regression, multifactorial logistic regression, and LASSO regression analyses. The study evaluated performance by creating calibration curves, analyzing the area under the curve (AUC-ROC) of subjects' work characteristics, examining calibration plots, and conducting decision curve analysis (DCA).
Result:
Multifactorial logistic regression analysis identified five independent predictors, including eGFR (OR:0.975; 95% CI: 0.970-0.983; P < 0.001), AMR (OR: 2.505; 95% CI: 1.756-3.656; P < 0.001), Serum blood uric acid to high-density lipoprotein cholesterol ratio (UHR) (OR: 1.006; 95% CI: 1.003-1.007; P < 0.001), The triglyceride and glucose index (TyG) (OR: 1.829; 95% CI: 1.346-2.502; P < 0.001), Contrast agent dosage (OR: 1.022; 95% CI: 1.016-1.028; P < 0.001), The nomogram accurately predicted the probability of CIN after PCI. Both the training cohort (AUC: 0.881) and validation cohort (AUC: 0.841) demonstrated good predictive ability of the model. Calibration plots confirmed the agreement between the predictions of the training and validation cohorts. DCA analysis also demonstrated the feasibility of the nomogram in clinical patient management.
Conclusion:
The nomogram showed good performance in predicting CIN, and it could help clinicians optimize the clinical treatments to improve the prognosis of STEMI patients.
More Related Videos
18:11A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
Published on: December 28, 2012
04:41Digital PCR for Quantifying Circulating MicroRNAs in Acute Myocardial Infarction and Cardiovascular Disease
Published on: July 3, 2018
Related Concept Videos
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:19Acute Coronary Syndrome II: Pathophysiology and Clinical Manifestations
01:30Acute Coronary Syndrome I: Introduction
01:28Acute Coronary Syndrome IV: Interprofessional Care