ArticleFrontiers in cardiovascular medicine2025
Prognostic value of the neutrophil percentage-to-albumin ratio for mortality in ICU patients with myocardial infarction: a retrospective cohort and machine learning analysis.
Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Albumin-Based Biomarkers and Adverse Outcomes in Coronary Artery Disease: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Association between the albumin-to-lymphocyte ratio and short-term mortality in critically Ill pediatric patients: a retrospective cohort study and machine learning analysis.Frontiers in pediatrics · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Background: Although the neutrophil percentage-to-albumin ratio (NPAR) has shown prognostic value in multiple clinical conditions, its prognostic accuracy for myocardial infarction (MI) patients receiving intensive care has yet to be clearly defined. To our knowledge, this study is the first to comprehensively evaluate the prognostic role of NPAR in ICU-admitted MI patients, integrating both conventional Cox regression and machine learning approaches to address an existing gap between general MI cohorts and critically ill populations. Method: Using data from the MIMIC-IV v3.1 database, we retrospectively included 1,759 ICU-admitted MI patients and calculated NPAR at admission. Primary and secondary outcomes were 30-day and 360-day all-cause mortality, respectively. Kaplan-Meier curves and log-rank tests compared survival across tertiles. Multivariate Cox models assessed associations, with restricted cubic splines evaluating nonlinearity. Machine learning models incorporating NPAR were developed to predict 30-day mortality, and model performance was assessed using the area under the receiver operating characteristic curve (AUC). Result: The 30-day and 360-day all-cause mortality rates were 24% and 38%, respectively. Kaplan-Meier analysis revealed significantly lower survival probabilities in patients with higher NPAR levels. Adjusted Cox regression showed that those in the highest NPAR tertile had an increased risk of 30-day (HR: 2.03, 95% CI: 1.51-2.73, Conclusion: The NPAR serves as an independent predictor of mortality at 30 and 360 days in MI patients admitted to the intensive care unit (ICU). When integrated into machine learning models, NPAR enhances predictive accuracy. These results indicate that NPAR serves as an independent predictor of short- and long-term mortality in ICU-admitted MI patients. Given its simplicity and accessibility from routine laboratory tests, NPAR can be feasibly incorporated into clinical decision-making and risk stratification protocols in critical care settings to facilitate individualized risk assessment and improve outcomes.
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