Predictive value of complete blood count parameters for 30-day mortality risk in acute myocardial infarction patients

Q: What are the key findings of this study?

Higher hemoglobin-to-red cell distribution width ratio independently reduced 30-day mortality risk in acute myocardial infarction patients (OR 0.775, p=0.001), while a nomogram with age and MPV achieved an AUC of 0.775.

Q: What is the clinical evidence from this study?

Study design: Cohort. Population: Acute myocardial infarction (AMI) (n=189). Intervention: Hemoglobin-to-red cell distribution width ratio (HRR) and complete blood count parameters (MPV, age). Primary outcome: 30-day all-cause mortality (OR 0.775, 95% CI 0.662-0.906, p=0.001).

Q: What does this research mean for the field?

A higher hemoglobin-to-red cell distribution width ratio independently reduces 30-day mortality risk in acute myocardial infarction patients, and a nomogram combining age, mean platelet volume, and this ratio achieves acceptable predictive discrimination. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

Jin Wang; Miaomiao Yang; Jun Zhou; Bing Pei; Juan Wu

doi:10.1186/s12872-026-06877-4

October 28, 2026BMC Cardiovascular DisordersOpen Access

Predictive value of complete blood count parameters for 30-day mortality risk in acute myocardial infarction patients

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Key result

Higher hemoglobin-to-RDW ratio is linked to ~22% lower 30-day mortality in AMI.

OR 0.775
95% CI 0.662-0.906
P=0.001
n=189

Why the study?

This study aimed to investigate the predictive value of complete blood count parameters for 30-day mortality risk in patients with acute myocardial infarction and to develop a clinical prediction model.

Does a nomogram combining age, mean platelet volume (MPV), and hemoglobin-to-red cell distribution width ratio (HRR) predict 30-day all-cause mortality in hospitalized patients with acute myocardial infarction?

Key Points

To investigate the predictive utility of complete blood count parameters for 30-day mortality risk in patients with acute myocardial infarction and construct a clinical prediction model.
Retrospective cohort study analyzing 189 acute myocardial infarction patients hospitalized between January 2020 and December 2024, divided into survivor (n = 144) and non-survivor (n = 45) groups.
Univariate and multivariate logistic regression were performed on baseline variables and complete blood count parameters to determine independent prognostic indicators.
A clinical nomogram was established and validated using receiver operating characteristic curve analysis and bootstrap validation.
Multivariate analysis identified age (OR = 1.054, 95% CI: 1.020–1.088, P = 0.002) and mean platelet volume (OR = 1.325, 95% CI: 1.000–1.755, P = 0.049) as independent risk factors for 30-day mortality.
Hemoglobin-to-red cell distribution width ratio independently protected against mortality (OR = 0.775, 95% CI: 0.662–0.906, P = 0.001), corresponding to a 22.5% risk reduction per unit increase.
The nomogram combining age, mean platelet volume, and hemoglobin-to-red cell distribution width ratio demonstrated an AUC of 0.775 (95% CI: 0.696–0.854) and a C-index of 0.768, outperforming each individual parameter.

Study Design

Type

Cohort (n=189)

Multicenter

No

Structured PICO

Does a nomogram combining age, mean platelet volume (MPV), and hemoglobin-to-red cell distribution width ratio (HRR) predict 30-day all-cause mortality in hospitalized patients with acute myocardial infarction?

P

Population

189 adult patients with acute myocardial infarction (median age 65 years, 29.1% female) admitted within 24 hours of symptom onset, free of malignancies or hematological disorders, followed for 30 days.

E

Exposure

Nomogram clinical prediction model incorporating age, mean platelet volume (MPV), and hemoglobin-to-red cell distribution width ratio (HRR) derived from routine baseline complete blood count (CBC) parameters

C

Comparator

Single predictor parameters alone (age, MPV, HRR) and sensitivity models (age + MPV excluding HRR; age + MPV + HRR + Killip class; age + MPV + hemoglobin replacing HRR)

O

Outcome

30-day all-cause mortalityhard clinical

Main Result

Odds Ratio: 0.775 (95% CI 0.662–0.906)

p-value: p=0.001

Limitations

Single-center retrospective design introducing potential selection bias
Relatively small sample size (189 patients, 45 deaths) limiting statistical power and generalizability
Lack of head-to-head comparison with established risk scores such as GRACE and TIMI due to incomplete recording of required variables
Intentional exclusion of clinically significant variables like hypertension, creatinine, and Killip class from the primary multivariate model for parsimony
Single-center, retrospective design introduces inherent selection bias
Relatively small sample size (189 patients, including 45 deaths) reduces statistical power and limits generalizability
No head-to-head comparisons with established risk scores (e.g., GRACE and TIMI) due to incompletely recorded variables
Intentional exclusion of significant clinical variables (hypertension, creatinine, Killip class) from the multivariate model to maintain parsimony and avoid overfitting

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Implication

A simple bedside nomogram integrating age, mean platelet volume (MPV), and hemoglobin-to-red cell distribution width ratio (HRR) from routine blood counts effectively predicts 30-day mortality in acute myocardial infarction (AUC = 0.775), aiding rapid risk stratification.

Abstract

To investigate the predictive value of complete blood count (CBC) parameters for 30-day mortality risk in patients with acute myocardial infarction (AMI) and to develop a clinical prediction model. This retrospective cohort study enrolled 189 AMI patients between January 2020 and December 2024, categorizing them into survival ( n = 144) and non-survival ( n = 45) groups based on 30-day outcomes. Baseline data and CBC parameters were analyzed. Univariate and multivariate logistic regression identified independent risk factors, followed by the construction of a nomogram model. Model performance was evaluated using receiver operating characteristic (ROC) curves and bootstrap validation. The results showed that non-survivors were significantly older ( p < 0.001), higher mean platelet volume (MPV) ( p = 0.012) and had a significantly lower hemoglobin-to-red cell distribution width ratio (HRR) ( p < 0.001). Multivariate analysis confirmed age (Odds Ratio (OR) = 1.054, 95% CI: 1.020–1.088, p = 0.002), MPV (OR = 1.325, 95% CI: 1.000–1.755, P = 0.049), and HRR (OR = 0.775, 95% CI: 0.662–0.906, P = 0.001) as independent risk factors after adjusting for other CBC parameters. The nomogram model achieved an area under the curve (AUC) of 0.775 (95% CI: 0.696–0.854) and a concordance index (C-index) of 0.768. In conclusion, age, MPV, and HRR are independent predictors of 30-day mortality in AMI. The nomogram integrating these three factors demonstrates acceptable predictive performance and outperforms any single parameter, offering a practical tool for early risk stratification.

Discussion

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Cite This Study

Wang et al. (2026) conducted a cohort in Acute myocardial infarction (AMI) (n=189). Hemoglobin-to-red cell distribution width ratio (HRR) and complete blood count parameters (MPV, age) was evaluated on 30-day all-cause mortality (OR 0.775, 95% CI 0.662-0.906, p=0.001). Higher hemoglobin-to-red cell distribution width ratio independently reduced 30-day mortality risk in acute myocardial infarction patients (OR 0.775, p=0.001), while a nomogram with age and MPV achieved an AUC of 0.775.

synapsesocial.com/papers/6ae4342cd9418cd7c8f47d50 https://doi.org/10.1186/s12872-026-06877-4

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Predictive value of complete blood count parameters for 30-day mortality risk in acute myocardial infarction patients

Key Points

Study Design

Structured PICO

Main Result

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Predictive value of complete blood count parameters for 30-day mortality risk in acute myocardial infarction patients

Key Points

Study Design

Structured PICO

Main Result

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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