The triglyceride-glucose index and neutrophil-to-lymphocyte ratio jointly predict the no-reflow phenomenon in T2DM patients with STEMI after primary PCI

TL;DR

This study shows that combining two blood markers helps predict a dangerous heart condition in diabetic patients after a heart attack.

Contribution

A novel predictive model combining TyG index and NLR for no-reflow in T2DM-STEMI patients is developed and validated.

Findings

The combined TyG + NLR model achieved an AUC of 0.785 for predicting no-reflow.

High TyG and NLR levels together predicted a 23.21% no-reflow incidence.

The combined model outperformed individual markers or baseline clinical factors.

Abstract

Patients suffering from ST-segment elevation myocardial infarction (STEMI) and type 2 diabetes mellitus (T2DM) face an elevated risk of the no-reflow phenomenon even after successful primary percutaneous coronary intervention (PPCI). This study aimed to develop an integrated predictive model combining the triglyceride-glucose (TyG) index and the neutrophil-to-lymphocyte ratio (NLR) for no-reflow in this high-risk population.

A retrospective cohort of 524 patients with T2DM and STEMI undergoing PPCI was analyzed. No-reflow was defined as post-procedural TIMI flow grade ≤2. Multivariable logistic regression and receiver operating characteristic (ROC) curve analyses were employed.

The incidence of no-reflow was 8.97% (47/524). Both TyG index (adjusted odds ratio [aOR] 2.98) and NLR (aOR 1.23) were identified as independent predictors. Patients were stratified into four groups based on the optimal cut-offs for NLR (2.831) and TyG (9.347). The group with high levels of both markers had a substantially higher no-reflow incidence (23.21%) compared to the low-risk group (1.49%). The combined model (TyG + NLR) demonstrated superior predictive performance (AUC 0.785) over models containing either marker alone or baseline clinical factors.

The combination of TyG index and NLR effectively stratifies the risk of no-reflow in T2DM-STEMI patients, potentially aiding the early identification of patients in need of targeted management.

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