Development and validation of a prognostic score integrating remote heart failure symptoms and clinical variables in mortality risk prediction after myocardial infarction: the PragueMi score
Language English Country England, Great Britain Media print
Document type Journal Article, Validation Study
Grant support
NV 19-09-00125
Ministry of Health of the Czech Republic
LX22NPO5104
National Institute for Research of Metabolic and Cardiovascular Diseases
European Union-Next Generation EU
PubMed
38497201
DOI
10.1093/eurjpc/zwae114
PII: 7630700
Knihovny.cz E-resources
- Keywords
- Heart failure, Mortality, Myocardial infarction, Questionnaire, Risk prediction, Symptoms,
- MeSH
- Time Factors MeSH
- Risk Assessment MeSH
- Myocardial Infarction * mortality diagnosis MeSH
- Middle Aged MeSH
- Humans MeSH
- Decision Support Techniques MeSH
- Predictive Value of Tests MeSH
- Prognosis MeSH
- Prospective Studies MeSH
- Reproducibility of Results MeSH
- Risk Factors MeSH
- Aged MeSH
- Heart Failure * mortality diagnosis physiopathology MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Validation Study MeSH
AIMS: While heart failure (HF) symptoms are associated with adverse prognosis after myocardial infarction (MI), they are not routinely used for patients' stratification. The primary objective of this study was to develop and validate a score to predict mortality risk after MI, combining remotely recorded HF symptoms and clinical risk factors, and to compare it against the guideline-recommended Global Registry of Acute Coronary Events (GRACE) score. METHODS AND RESULTS: A cohort study design using prospectively collected data from consecutive patients hospitalized for MI at a large tertiary heart centre between June 2017 and September 2022 was used. Data from 1135 patients (aged 64 ± 12 years, 26.7% women), were split into derivation (70%) and validation cohort (30%). Components of the 23-item Kansas City Cardiomyopathy Questionnaire and clinical variables were used as possible predictors. The best model included the following variables: age, HF history, admission creatinine and heart rate, ejection fraction at hospital discharge, and HF symptoms 1 month after discharge including walking impairment, leg swelling, and change in HF symptoms. Based on these variables, the PragueMi score was developed. In the validation cohort, the PragueMi score showed superior discrimination to the GRACE score for 6 months [the area under the receiver operating curve (AUC) 90.1, 95% confidence interval (CI) 81.8-98.4 vs. 77.4, 95% CI 62.2-92.5, P = 0.04) and 1-year risk prediction (AUC 89.7, 95% CI 83.5-96.0 vs. 76.2, 95% CI 64.7-87.7, P = 0.004). CONCLUSION: The PragueMi score combining HF symptoms and clinical variables performs better than the currently recommended GRACE score.
The prognosis of patients after myocardial infarction is heterogeneous. Thus, risk stratification is needed to identify and intervene patients at increased risk. While heart failure (HF) symptoms are associated with adverse prognosis, they are not used for patients’ stratification. We have developed and internally validated the PragueMi score, which integrates clinical risk factors at the time of hospitalization and HF symptoms determined remotely by a questionnaire 1 month after hospital discharge. PragueMi score was able to better stratify patients’ risk as compared with the currently recommended Global Registry of Acute Coronary Events score.
1st Medical School Charles University Katerinska 1660 32 Prague 120 00 Czech Republic
3rd Medical School Charles University Prague Czech Republic
Department of Cardiology Institute for Clinical and Experimental Medicine Prague Czech Republic
Division of Cardiovascular Medicine University of Utah School of Medicine Salt Lake City UT USA
Experimental Medicine Centre Institute for Clinical and Experimental Medicine Prague Czech Republic
Medical and Dentistry School Palacký University Olomouc Czech Republic
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