Changes in Blood Eosinophil Counts Predict the Death of Patients With Myocardial Infarction After Hospital Discharge

Kaiyang Lin; Manqing Luo; Xia Gu; Jun-Yan Xu; Jinwei Tian; Peter Libby; Guo-Ping Shi; Junli Guo

doi:10.1161/JAHA.124.035383

Changes in Blood Eosinophil Counts Predict the Death of Patients With Myocardial Infarction After Hospital Discharge

J Am Heart Assoc. 2024 Dec 20:e035383. doi: 10.1161/JAHA.124.035383. Online ahead of print.

Authors

Kaiyang Lin¹, Manqing Luo¹, Xia Gu², Jun-Yan Xu^{3

4}, Jinwei Tian², Peter Libby⁵, Guo-Ping Shi⁵, Junli Guo⁴

Affiliations

¹ Department of Cardiology Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Key Laboratory of Cardiovascular Disease Fuzhou China.
² Department of Cardiology The Second Affiliated Hospital of Harbin Medical University Harbin Heilongjiang China.
³ Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences Guangzhou China.
⁴ Key Laboratory of Tropical Translational Medicine of Ministry of Education & Key Laboratory of Tropical Cardiovascular Diseases Research of Hainan Province, School of Public Health Hainan Medical University Haikou China.
⁵ Department of Medicine Brigham and Women's Hospital, and Harvard Medical School Boston MA USA.

PMID: 39704243
DOI: 10.1161/JAHA.124.035383

Abstract

Background: Preclinical studies demonstrate a cardioprotective role of eosinophils in acute myocardial infarction. Yet clinical studies show conflicting correlations between blood eosinophil counts and acute myocardial infarction risk and mortality. This study evaluates blood eosinophil counts of patients with acute myocardial infarction at hospital admission (EOS_baseline) and discharge (EOS_post) on all-cause and cardiac mortalities.

Methods and results: Of 2681 consecutive patients with a median follow-up of 2.55 years, 45 patients died within 30 days, 28 died within 30 to 150 days, and 92 died within 150 days or later postdischarge. Cardiac death occurred in 108 patients. According to the receiver operating characteristic analyses, the best cutoffs of EOS_baseline and EOS_post were 0.02×10⁹/L and 0.03×10⁹/L, respectively, to predict 30-day all-cause death (area under the curve [AUC]_baseline, 0.60; AUC_post, 0.67). The optimal cutoffs of EOS_baseline and EOS_post were 0.20×10⁹/L and 0.14×10⁹/L to predict long-term all-cause mortality (1-year AUC_baseline, 0.59; 1-year AUC_post, 0.61). Multivariate logistic analysis showed that low EOS_baseline (<0.02×10⁹/L) or low EOS_post (<0.03×10⁹/L) predicted the 30-day all-cause (odds ratio [OR]_baseline, 2.56; P=0.005; OR_post, 8.14; P<0.001) and cardiac (OR_baseline, 2.16; P=0.025; OR_post, 7.89; P<0.001) mortalities. Patients with combined low EOS_baseline (<0.02×10⁹/L) and low EOS_post (<0.03×10⁹/L) displayed synergistic risk of 30-day all-cause (OR, 13.93; P<0.001) and cardiac (OR, 11.38; P<0.001) deaths. In contrast, adjusted Cox proportional hazard test indicated that high EOS_post (≥0.14×10⁹/L) was an independent risk for long-term all-cause mortality (hazard ratio, 1.84; P=0.010).

Conclusions: High and low blood eosinophil counts both predict the risk of all-cause and cardiac deaths in patients with acute myocardial infarction depending on the time of data collection. Dynamic changes of blood eosinophil counts offer a more accurate prediction of post-myocardial infarction deaths than a single time point data analysis.

Keywords: acute myocardial infarction; all‐cause mortality; cardiac death; eosinophil; hospital discharge.