Multimodal data-driven prognostic model for predicting new-onset ST-elevation myocardial infarction following emergency percutaneous coronary intervention.

OBJECTIVES: We developed a nomogram model derived from inflammatory indices, clinical data, and imaging data to predict in-hospital major adverse cardiac and cerebrovascular events (MACCEs) following emergency percutaneous coronary intervention (PCI) in patients with new-onset ST-elevation myocardial infarction (STEMI). METHODS: Patients with new-onset STEMI admitted between June 2020 and November 2022 were retrospectively reviewed. Data pertaining to coronary angiograms, clinical data, biochemical indices, and in-hospital clinical outcomes were derived from electronic medical records. Lasso regression model was employed to screen risk factors and construct a prediction model. RESULTS: Overall, 547 patients with new-onset STEMI who underwent PCI were included and assigned to the training cohort (n = 384) and independent verification cohort (n = 163). Six clinical features (age, diabetes mellitus, current smoking, hyperuricemia, neutrophil-to-lymphocyte ratio, and Gensini score) were selected by LASSO regression to construct a nomogram to predict the risk of in-hospital MACCEs. The area-under-the-curve (AUC) values for in-hospital MACCEs risk in the training and independent verification cohorts were 0.921 (95% CI 0.881-0.961) and 0.898 (95% CI 0.821-0.976), respectively. It was adequately calibrated in both training cohort and independent verification cohorts, and predictions were correlated with actual outcomes. Decision curve analysis demonstrated that the nomogram was capable of predicting in-hospital MACCEs with good clinical benefit. CONCLUSIONS: Our prediction nomogram based on multi-modal data (inflammatory indices, clinical and imaging data) reliably predicted in-hospital MACCEs in new-onset STEMI patients with emergency PCI. This prediction nomogram can enable individualized treatment strategies.

PTH Predicts the in-Hospital MACE After Primary Percutaneous Coronary Intervention for Acute ST-Segment Elevation Myocardial Infarction.

Objective: To investigate the correlation between serum parathyroid hormone (PTH) levels and in-hospital major adverse cardiovascular events (MACE) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI), and establish a risk prediction model based on parameters such as PTH for in-hospital MACE. Methods: This observational retrospective study consecutively enrolled 340 patients who underwent primary PCI for STEMI between January 2016 and December 2020, divided into a MACE group (n=92) and a control group (n=248). The least absolute shrinkage and selection operator (LASSO) and logistic regression analyses were used to determine the risk factors for MACE after primary PCI. The rms package in R-studio statistical software was used to construct a nomogram, to detect the line chart C-index, and to draw a calibration curve. The decision curve analysis (DCA) method was used to evaluate the clinical application value and net benefit. Results: Correlation analysis revealed that PTH level positively correlated with the occurrence of in-hospital MACE. Receiver operating characteristic curve analyses revealed that PTH had a good predictive value for in-hospital MACE. Multivariate logistic regression analysis indicated that Killip class II-IV, and FBG were independently associated with in-hospital MACE after primary PCI. A nomogram model was constructed using the above parameters. The model C-index was 0.894 and the calibration curve indicated that the model was well calibrated. The DCA curve suggested that the nomogram model was better than TIMI score model in terms of net clinical benefit. Conclusion: Serum PTH levels in patients with STEMI are associated with in-hospital MACE after primary PCI, and the nomogram risk prediction model based on PTH demonstrated good predictive ability with obvious clinical practical value.