Background: Chronic low-grade inflammation is a well-known risk factor for coronary heart disease (CHD) and future cardiovascular events. Anti-inflammatory therapy can reduce the risk of ischemic cardiovascular disease (CVD) events following myocardial infarction (MI). However, it remains unknown to what extent inflammation at the time of an acute event predicts long-term outcomes. We explored whether routine blood measurements of inflammatory markers during an acute coronary syndrome (ACS) are predictive of long-term mortality.

Methods: In a cohort of 5292 consecutive patients admitted to a coronary intensive care unit with suspected ACS over a four-year period in the Carlscrona Heart Attack Prognosis Study (CHAPS), 908 patients aged 30–74 years (644 men, 264 women) were diagnosed with MI (527) or unstable angina (UA) (381). A 10-year follow-up study was conducted using Swedish national registries, with total mortality and cardiac mortality as primary outcomes.

Results: Long-term total and cardiac mortality were significantly associated with higher leukocyte counts (e.g., neutrophils, monocytes, p ≤ 0.001), higher levels of inflammatory biomarkers (e.g., C-reactive protein, Serum Amyloid A, fibrinogen, p ≤ 0.001), and elevated neutrophil–lymphocyte ratio (NLR) ( p  < 0.001) and monocyte-lymphocyte ratio (MLR) ( p  = 0.002), all measured at ACS admission. These associations were independent of ACS diagnosis.

Conclusion: Our results suggest that level of inflammation at ACS presentation—beyond its established role as a major CHD risk factor—also predicts long-term mortality following ACS. Notably, inflammation at the time of the event was a stronger predictor of long-term mortality than the acute event outcome itself. However, limitations include the observational study design, moderate sample size, and absence of modern high-sensitivity cardiac biomarkers and contemporary ACS management strategies in this cohort. The results should therefore be interpreted in the context of historical clinical practice. While our model-wise complete-case approach ensured consistency, missing data remains a potential source of bias. Future studies in larger, more contemporary cohorts are needed to validate these findings and refine risk stratification strategies.