Forecasting mortality: dynamic assessment of risk in ST-segment elevation acute myocardial infarction
Wei-Ching Chang1, Padma Kaul, Yuling Fu
1Department of Medicine, University of Alberta, 2-51 Medical Sciences Building, Edmonton, Alberta, Canada T6G 2H7.
Insights
Dynamic risk assessment models for ST-segment elevation myocardial infarction (STEMI) patients show excellent predictive ability. These models enhance patient stratification and provide ongoing guidance for clinical management.
Area of Science:
- Cardiology
- Medical Informatics
Background:
- ST-segment elevation myocardial infarction (STEMI) requires accurate and timely risk assessment.
- Existing risk models may not fully capture the dynamic nature of patient prognosis post-STEMI.
Purpose of the Study:
- To establish the feasibility and clinical value of dynamic risk assessment models for STEMI patients.
- To develop predictive models for 30-day mortality in STEMI.
Main Methods:
- Utilized multiple-logistic regression on data from 6066 STEMI patients in the ASSENT-3 trial.
- Validated models in independent cohorts (ASSENT-3 PLUS and COMMA trials).
- Assessed mortality risk at multiple time points (baseline, 3h, day 2, day 5).
Main Results:
- Models demonstrated excellent discriminatory power (c-statistics 0.80-0.87) across forecasting periods.
- Strong gradients in mortality observed with increasing risk scores.
- Dynamic modeling provided insights into prognosis changes over time.
Conclusions:
- Dynamic risk modeling significantly enhances risk assessment and stratification for STEMI patients.
- This approach offers valuable, ongoing guidance for patient management decisions.
- Identifies high-risk patients for potential co-interventions.
Aims:
To demonstrate the feasibility and clinical utility of developing dynamic risk assessment models for ST-segment elevation myocardial infarction (STEMI) patients.
Methods And Results:
In 6066 STEMI patients enrolled in the Assessment of the Safety and Efficacy of a New Thrombolytic-3 (ASSENT-3) trial with complete electrocardiographic data, we assessed the probability of 30-day mortality over the following forecasting periods beginning at day 0 (baseline), 3 h, day 2, and day 5 using multiple-logistic regression. These models were validated and simplified in independent samples of 1622 similar fibrinolytic-treated patients from the ASSENT-3 PLUS trial and in 814 STEMI patients undergoing primary percutaneous coronary intervention in the COMplement inhibition in Myocardial infarction treated with Angioplasty (COMMA) trial. The discriminatory power of these predictive models, from baseline to day 5, was excellent (c-statistics 0.80 to 0.87); and their predictive ability was supported by strong gradients in mortality outcomes as the risk score increased. Dynamic modelling also provided information on the change in prognosis over time which may be used to advise more appropriate therapeutic decisions, e.g. the identification of high-risk patients for possible co-interventions.
Conclusion:
Dynamic modelling for STEMI patients enhances the risk assessment and stratification and should provide valuable ongoing guidance for their management.
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