February 1, 2022Therapeutics and Clinical Risk ManagementOpen Access

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

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Why the study?

Cardiac microvascular obstruction remains a severe complication in NSTEMI patients receiving reperfusion therapy, driving the need for a clinical and laboratory-based prediction model.

Does a clinical and laboratory-based nomogram accurately predict cardiac microvascular obstruction in NSTEMI patients after primary PCI?

Structured PICO

Does a clinical and laboratory-based nomogram accurately predict cardiac microvascular obstruction in NSTEMI patients after primary PCI?

Population

325 patients undergoing primary PCI for NSTEMI (training cohort n=226, validating cohort n=99)

Intervention

Clinical and laboratory-based nomogram integrating neutrophils, hemoglobin, triglyceride, Killip grade, high thrombus load, no-reflow, and ischemic postconditioning

Outcome

Cardiac microvascular obstruction (CMVO) confirmed by DE-CMRsurrogate

A novel clinical and laboratory-based nomogram accurately predicts coronary microvascular obstruction in NSTEMI patients following primary PCI, potentially aiding in treatment optimization.

Implication

May aid CMVO risk stratification after PCI in NSTEMI; leaves open prospective validation before clinical adoption.

Abstract

Objective: Cardiac microvascular obstruction (CMVO) remains a severe complication in non-ST elevation myocardial infarction (NSTEMI) patients with reperfusion therapy. We aimed at developing and validating the nomogram to predict the possibility of CMVO after primary percutaneous coronary intervention (PCI) by integrating clinical and laboratory-based information. Methods: A total of 325 patients undergoing primary PCI for NSTEMI were recruited and divided into the training cohort (n=226) and the validating cohort (n = 99). The development of the nomogram was based on independent predictors of CMVO, and these variables were selected by multivariable logistic regression analysis. Results: Independent predictors contained in nomogram were identified by multivariable logistic regression analysis, and these independent predictors included neutrophils (OR 1.166, 95% CI 1.044– 1.303, P < 0.01), hemoglobin (OR 1.037, 95% CI 1.013– 1.062, P < 0.01), triglyceride (OR 1.343, 95% CI 1.059; 1.704, P =0.015), Killip grade (OR 2.190, 95% CI 1.065– 4.503, P =0.033), high thrombus load (OR 3.146, 95% CI 1.424– 6.952, P < 0.01), no-reflow (OR 3.142, 95% CI 1.419– 6.955, P < 0.01) and ischemic postconditioning (OR 0.445, 95% CI 0.209– 0.944, P =0.035). The nomogram accurately predicted the presentation of CMVO in both the training set and validating set (AUC, 0.835 and 0.881, respectively). The results predicted by nomogram were confirmed to be highly consistent with the results of DE-CMR, both the training and validating cohorts, by Calibration plot and Hosmer-Lemeshow test. Decision curve analysis (DCA) also suggested that the nomogram was applicable in the clinic. Conclusion: The nomogram showed good performance in predicting CMVO, and it could help clinicians optimize the clinical treatments to improve the prognosis of NSTEMI patients.