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A Simple Clinical Risk Score to Predict Post-Discharge Mortality in Chinese Patients Hospitalized with Heart Failure
Lei Wang1, Li-Qin Wang2, Mo-Li Gu1
1Departamento de Medicina Geriátrica, the Fourth Medical Center, Chinese PLA General Hospital, Beijng - China.
Insights
A new risk score using five factors can predict one-year heart failure (HF) mortality in Chinese patients after hospital discharge. This tool helps identify high-risk individuals for better post-discharge care.
Area of Science:
- Cardiology
- Public Health
- Medical Statistics
Background:
- Cardiovascular diseases are the leading cause of death in China.
- Current risk prediction models for heart failure (HF) mortality often focus on in-hospital or short-term outcomes.
- There is a need for a robust model to assess long-term post-discharge mortality risk in HF patients.
Purpose of the Study:
- To identify key predictors of one-year post-discharge mortality in hospitalized Chinese patients with HF.
- To develop a practical risk score for assessing this mortality risk.
Main Methods:
- A Markov Chain Monte Carlo simulation was employed on a dataset of 1,742 Chinese HF patients.
- Variables with >1% frequency and clinical relevance were analyzed for their association with one-year mortality.
- A probability threshold of 0.9 was used to identify robust risk factors.
Main Results:
- Five significant predictors of one-year post-discharge HF mortality were identified: age, female gender, NYHA class >3, left atrial diameter, and BMI.
- The developed risk score demonstrated good predictive performance with an AUC of 0.79 in both derivation and test sets.
- The risk score effectively categorized patients into low, moderate, and high-risk groups, with the high-risk group accounting for 86% of deaths.
Conclusions:
- A simple, 5-variable risk score can reliably predict one-year post-discharge mortality in hospitalized Chinese HF patients.
- This risk score offers a valuable tool for clinical decision-making and patient management.
- The findings highlight the importance of considering specific demographic and clinical factors for long-term HF risk stratification.
Background:
Cardiovascular diseases are the leading causes of death in China. However, present efforts to identify the risk factors for death in patients hospitalized with heart failure (HF) are primarily focused on in-hospital mortality and 30-day mortality in the United States. Thus, a model similar to the model used for predicting the risk in patients considered for cardiovascular surgical procedures is needed to evaluate the risk of the patients admitted with a diagnosis of HF.
Objective:
To identify variables that can predict post-discharge one-year HF mortality and develop a risk score to assess the risk of dying within one year.
Methods:
In the present study, 1,742 Chinese patients with HF were randomly divided into two groups: a derivation sample group and a test sample group. A Markov Chain Monte Carlo simulation method was used to identify variables that can predict the one-year post-discharge mortality. Variables with a frequency of >1% in the bivariate analysis and that were considered clinically meaningful were eligible for further modeling analyses. The posterior probability that a variable was statistically and significantly associated with the outcome was calculated as the total number of times that the variable's 95% CI did not overlap with 1 (i.e., the reference point) divided by the total number of iterations. A variable with a probability of 0.9 or higher was considered a robust risk factor for predicting the outcome, and this was included in the final variable list. The level of statistical significance adopted was 5%.
Results:
Five variables that could robustly predict the one-year post-discharge mortality were identified: age, female gender, New York Heart Association functional classification score >3, left atrial diameter, and body mass index. Both derivation and test models had a receiver operating curve area of 0.79. These selected variables were used to assess the one-year HF mortality risk score, and these were divided into three groups (low, moderate, and high). The high-risk group corresponds to nearly 86% of the deaths, while the moderate group corresponds to 12% of the deaths.
Conclusion:
A simple 5-variable risk score can be used to assess the one-year post-discharge mortality of hospitalized Chinese patients with HF.
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