Researchers have developed a simple prediction model that combines a patient’s age with two routine complete blood count measurements to estimate the risk of death within 30 days of an acute myocardial infarction, the medical term for a heart attack. The model, described in BMC Cardiovascular Disorders, relies on data that hospitals already collect at admission, requiring no additional tests or equipment.
The study was a retrospective cohort analysis of 189 patients with acute myocardial infarction treated between January 2020 and December 2024. The patients were divided into two groups according to their outcome at 30 days: 144 survived and 45 did not. The authors compared baseline characteristics and complete blood count parameters between the groups to identify which measurements differed before the outcome was known.
Three variables stood out. Patients who died within 30 days were significantly older than survivors, with the difference reaching statistical significance at p < 0.001. They also had a higher mean platelet volume, a measure of the average size of platelets, at p = 0.012, and a significantly lower hemoglobin-to-red cell distribution width ratio, abbreviated HRR, at p < 0.001.
To test whether these differences were independent of one another, the researchers used multivariate logistic regression, adjusting for other complete blood count parameters. Age remained an independent predictor, with an odds ratio of 1.054 per year (95% confidence interval: 1.020 to 1.088, p = 0.002). Mean platelet volume also remained significant, with an odds ratio of 1.325 (95% CI: 1.000 to 1.755, p = 0.049), a result that sits just at the threshold of conventional statistical significance. The HRR showed an inverse association, with an odds ratio of 0.775 (95% CI: 0.662 to 0.906, p = 0.001), meaning a lower ratio was associated with higher odds of death.
The authors then combined the three factors into a nomogram, a graphical scoring tool that allows clinicians to read off an estimated probability from a patient’s individual values. The nomogram achieved an area under the receiver operating characteristic curve of 0.775 (95% CI: 0.696 to 0.854) and a concordance index of 0.768, with performance assessed by bootstrap validation. According to the authors, the model outperforms any single parameter used alone.
An AUC of 0.775 falls in the range generally described as acceptable rather than excellent discrimination. It indicates that the model ranks patients’ risk better than chance, but with substantial overlap between survivors and non-survivors, so it cannot predict an individual patient’s outcome with certainty. The confidence interval, which extends down to 0.696, reflects the uncertainty inherent in a study of this size.
Several features of the study design shape how the findings should be interpreted. The cohort was relatively small, with 45 deaths, and the analysis was retrospective, meaning the researchers used data already collected during care rather than prospectively registering patients for follow-up. The study covered a single enrollment window from 2020 to 2024, and the published version is an early-release, peer-reviewed accepted manuscript that is subject to further edits before the final version of record appears.
Because the model was developed and internally validated in the same dataset, its performance in other hospitals, other patient populations and other healthcare systems remains to be established. The odds ratios describe associations observed in this cohort and do not by themselves establish that modifying any of the three parameters would change a patient’s risk.
Within those limits, the study offers a practical observation: information already present in a standard admission blood count, together with age, carries measurable information about short-term mortality risk after a heart attack. The authors position the nomogram as a tool for early risk stratification, potentially helping clinicians flag patients who may warrant closer monitoring in the first month after admission.
Subject of Research: Predicting 30-day mortality after acute myocardial infarction using complete blood count parameters
Article Title: Predictive value of complete blood count parameters for 30-day mortality risk in acute myocardial infarction patients
Article References: Wang, J., Yang, M., Zhou, J., Pei, B., & Wu, J. (2026). Predictive value of complete blood count parameters for 30-day mortality risk in acute myocardial infarction patients. BMC Cardiovascular Disorders. https://doi.org/10.1186/s12872-026-06877-4
Image Credits: AI Generated
DOI: 10.1186/s12872-026-06877-4
Keywords: acute myocardial infarction, 30-day mortality, complete blood count, mean platelet volume, hemoglobin-to-red cell distribution width ratio, nomogram, risk prediction, retrospective cohort study, logistic regression, BMC Cardiovascular Disorders, Predictive, value
News Source: Ophelia Keating. (October 28, 2026). Age, Platelet Volume and Hemoglobin Ratio Predict 30-Day Mortality After Heart Attack. Scienmag.
