Development and Validation of a Clinical and Laboratory-Based Nomogram for Predicting Coronary Microvascular Obstruction in NSTEMI Patients After Primary PCI

Development and validation of a predictive model for adverse left ventricular remodeling in NSTEMI patients after primary percutaneous coronary intervention

Introduction

To develop and validate clinical evaluators that predict adverse left ventricular remodeling (ALVR) in non-ST-elevation myocardial infarction (NSTEMI) patients after primary percutaneous coronary intervention (PCI).

Methods

The retrospective study analyzed the clinical data of 507 NSTEMI patients who were treated with primary PCI from the Affiliated Hospital of Xuzhou Medical University and the Second Affiliated Hospital of Xuzhou Medical University, between January 1, 2019 and September 31, 2021. The training cohort consisted of patients admitted before June 2020 (n = 287), and the remaining patients (n = 220) were assigned to an external validation cohort. The endpoint event was the occurrence of ALVR, which was described as an increase ≥ 20% in left ventricular end-diastolic volume (LVEDV) at 3–4 months follow-up CMR compared with baseline measurements. The occurrence probability of ALVR stemmed from the final model, which embodied independent predictors recommended by logistic regression analysis. The area under the receiver operating characteristic curve (AUC), Calibration plot, Hosmer–Lemeshow method, and decision curve analysis (DCA) were applied to quantify the performance.

Results

Independent predictors for ALVR included age (odds ratio (OR): 1.040; 95% confidence interval (CI): 1.009–1.073), the level of neutrophil to lymphocyte ratio (OR: 4.492; 95% CI: 1.906–10.582), the cardiac microvascular obstruction (OR: 3.416; 95% CI: 1.170–9.970), peak global longitudinal strain (OR: 1.131; 95% CI: 1.026–1.246), infarct size (OR: 1.082; 95% CI: 1.042–1.125) and left ventricular ejection fraction (OR: 0.925; 95% CI: 0.872–0.980), which were screened by regression analysis then merged into the nomogram model. Both internal validation (AUC: 0.805) and external validation (AUC: 0.867) revealed that the prediction model was capable of good discrimination. Calibration plot and Hosmer–Lemeshow method showed high consistency between the probabilities predicted by the nomogram (P = 0.514) and the validation set (P = 0.762) and the probabilities of actual occurrence. DCA corroborated the clinical utility of the nomogram.

Conclusions

In this study, the proposed nomogram model enabled individualized prediction of ALVR in NSTEMI patients after reperfusion and conduced to guide clinical therapeutic schedules.