Dataset Information

Baseline fibroblast growth factor 23 is associated with long-term mortality in ST-elevation myocardial infarction-results from the augsburg myocardial infarction registry.

ABSTRACT:

Background

The aim of this study was to investigate the association between inflammatory plasma protein concentrations and long-term mortality in patients with ST-elevation myocardial infarction (STEMI).

Methods

For 343 STEMI patients recorded between 2009 and 2013 by the population-based Myocardial Infarction Registry Augsburg, 92 inflammatory plasma proteins were measured at the index event using the OLINK inflammation panel. In multivariable-adjusted Cox regression models, the association between each plasma protein and all-cause long-term mortality was investigated. Median follow-up time was 7.6 (IQR: 2.4) years. For plasma protein that showed a strong association with long-term mortality, a 5-year survival ROC analysis was performed.

Results

One plasma protein, namely Fibroblast Growth Factor 23 (FGF-23), was particularly well associated with long-term mortality in the multivariable-adjusted Cox model with an FDR-adjusted p-value of <0.001 and a Hazard Ratio (HR) of 1.57 [95% CI: 1.29-1.91]. In the 5-years ROC analysis, an AUC of 0.6903 [95% CI: 0.594-0.781] was estimated for FGF-23. All other plasma protein didńt show strong associations, each marker with FDR-adjusted p-values >0.05 in the multivariable-adjusted Cox models.

Conclusions

FGF-23 is independently associated with long-term mortality after STEMI and might play an important role in the response to myocardial injury. The results suggest FGF-23 to be a useful marker in the long-term treatment of STEMI patients and a potential target for drug development.

SUBMITTER: Schmitz T

PROVIDER: S-EPMC10436601 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Baseline fibroblast growth factor 23 is associated with long-term mortality in ST-elevation myocardial infarction-results from the augsburg myocardial infarction registry.

Schmitz Timo T Wein Bastian B Heier Margit M Peters Annette A Meisinger Christa C Linseisen Jakob J

Frontiers in cardiovascular medicine 20230804

<h4>Background</h4>The aim of this study was to investigate the association between inflammatory plasma protein concentrations and long-term mortality in patients with ST-elevation myocardial infarction (STEMI).<h4>Methods</h4>For 343 STEMI patients recorded between 2009 and 2013 by the population-based Myocardial Infarction Registry Augsburg, 92 inflammatory plasma proteins were measured at the index event using the OLINK inflammation panel. In multivariable-adjusted Cox regression models, the ...[more]

PMID: 37600039

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Project description:BackgroundMachine learning (ML) risk prediction models, although much more accurate than traditional statistical methods, are inconvenient to use in clinical practice due to their nontransparency and requirement of a large number of input variables.ObjectiveWe aimed to develop a precise, explainable, and flexible ML model to predict the risk of in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI).MethodsThis study recruited 18,744 patients enrolled in the 2013 China Acute Myocardial Infarction (CAMI) registry and 12,018 patients from the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE)-Retrospective Acute Myocardial Infarction Study. The Extreme Gradient Boosting (XGBoost) model was derived from 9616 patients in the CAMI registry (2014, 89 variables) with 5-fold cross-validation and validated on both the 9125 patients in the CAMI registry (89 variables) and the independent China PEACE cohort (10 variables). The Shapley Additive Explanations (SHAP) approach was employed to interpret the complex relationships embedded in the proposed model.ResultsIn the XGBoost model for predicting all-cause in-hospital mortality, the variables with the top 8 most important scores were age, left ventricular ejection fraction, Killip class, heart rate, creatinine, blood glucose, white blood cell count, and use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). The area under the curve (AUC) on the CAMI validation set was 0.896 (95% CI 0.884-0.909), significantly higher than the previous models. The AUC for the Global Registry of Acute Coronary Events (GRACE) model was 0.809 (95% CI 0.790-0.828), and for the TIMI model, it was 0.782 (95% CI 0.763-0.800). Despite the China PEACE validation set only having 10 available variables, the AUC reached 0.840 (0.829-0.852), showing a substantial improvement to the GRACE (0.762, 95% CI 0.748-0.776) and TIMI (0.789, 95% CI 0.776-0.803) scores. Several novel and nonlinear relationships were discovered between patients' characteristics and in-hospital mortality, including a U-shape pattern of high-density lipoprotein cholesterol (HDL-C).ConclusionsThe proposed ML risk prediction model was highly accurate in predicting in-hospital mortality. Its flexible and explainable characteristics make the model convenient to use in clinical practice and could help guide patient management.Trial registrationClinicalTrials.gov NCT01874691; https://clinicaltrials.gov/study/NCT01874691.

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Dataset Information

Baseline fibroblast growth factor 23 is associated with long-term mortality in ST-elevation myocardial infarction-results from the augsburg myocardial infarction registry.

Background

Methods

Results

Conclusions

Publications

Baseline fibroblast growth factor 23 is associated with long-term mortality in ST-elevation myocardial infarction-results from the augsburg myocardial infarction registry.

Similar Datasets

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