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A Novel Risk Score to Predict In-Hospital Mortality in Patients With Acute Myocardial Infarction: Results From a
Lulu Li1,2, Xiling Zhang2, Yini Wang2
1Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Insights
A new HAMIOT risk score effectively predicts in-hospital mortality in acute myocardial infarction (AMI) patients. This validated tool offers improved risk stratification for AMI care.
Area of Science:
- Cardiology
- Clinical Risk Prediction
- Public Health
Background:
- Acute myocardial infarction (AMI) poses a significant mortality risk.
- Accurate prediction of in-hospital mortality is crucial for timely intervention.
- Existing risk scores may require refinement for specific populations.
Purpose of the Study:
- To develop and validate a novel, simplified risk score for predicting in-hospital mortality in Chinese AMI patients.
- To assess the performance of the new score against existing tools like the Global Registry of Acute Coronary Events (GRACE) score.
- To establish a tool for prospective risk stratification in clinical practice.
Main Methods:
- Utilized the multicenter, prospective Heart Failure after Acute Myocardial Infarction with Optimal Treatment (HAMIOT) cohort in China.
- Employed logistic regression to develop the risk score using a training cohort (70%) and validated it in a separate validation cohort (30%) and an external cohort.
- Evaluated model performance using Harrell's c-statistic for discrimination and the Hosmer-Lemeshow test for calibration.
Main Results:
- Identified ten independent predictors of in-hospital mortality, including age, systolic blood pressure, and serum creatinine.
- The novel HAMIOT risk score demonstrated strong discrimination (c-statistic: 0.88 in training, 0.82 in validation) and good calibration.
- The HAMIOT score outperformed the GRACE score in both training and validation cohorts.
Conclusions:
- The developed HAMIOT risk score is a valid and effective tool for predicting in-hospital mortality in patients with AMI.
- The score facilitates prospective risk stratification, aiding clinical decision-making.
- This novel score offers enhanced predictive accuracy compared to the GRACE score.
Objectives:
The aim of this study was to develop and validate a novel risk score to predict in-hospital mortality in patients with acute myocardial infarction (AMI) using the Heart Failure after Acute Myocardial Infarction with Optimal Treatment (HAMIOT) cohort in China.
Methods:
The HAMIOT cohort was a multicenter, prospective, observational cohort of consecutive patients with AMI in China. All participants were enrolled between December 2017 and December 2019. The cohort was randomly assigned (at a proportion of 7:3) to the training and validation cohorts. Logistic regression model was used to develop and validate a predictive model of in-hospital mortality. The performance of discrimination and calibration was evaluated using the Harrell's c-statistic and the Hosmer-Lemeshow goodness-of-fit test, respectively. The new simplified risk score was validated in an external cohort that included independent patients with AMI between October 2019 and March 2021.
Results:
A total of 12,179 patients with AMI participated in the HAMIOT cohort, and 136 patients were excluded. In-hospital mortality was 166 (1.38%). Ten predictors were found to be independently associated with in-hospital mortality: age, sex, history of percutaneous coronary intervention (PCI), history of stroke, presentation with ST-segment elevation, heart rate, systolic blood pressure, initial serum creatinine level, initial N-terminal pro-B-type natriuretic peptide level, and PCI treatment. The c-statistic of the novel simplified HAMIOT risk score was 0.88, with good calibration (Hosmer-Lemeshow test: P = 0.35). Compared with the Global Registry of Acute Coronary Events risk score, the HAMIOT score had better discrimination ability in the training (0.88 vs. 0.81) and validation (0.82 vs. 0.72) cohorts. The total simplified HAMIOT risk score ranged from 0 to 121. The observed mortality in the HAMIOT cohort increased across different risk groups, with 0.35% in the low risk group (score ≤ 50), 3.09% in the intermediate risk group (50 < score ≤ 74), and 14.29% in the high risk group (score > 74).
Conclusion:
The novel HAMIOT risk score could predict in-hospital mortality and be a valid tool for prospective risk stratification of patients with AMI.
Clinical Trial Registration:
[https://clinicaltrials.gov], Identifier: [NCT03297164].
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