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DCM-PROGRESS: predicting end-stage heart failure in non-ischemic dilated cardiomyopathy patients
A F Schmidt1,2,3,4, P Leinveber5, R Panovsky6,7
1Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, the Netherlands.
Insights
We developed a new model to predict the 5-year risk of end-stage heart failure (HF) in dilated cardiomyopathy (DCM) patients. This validated tool can improve patient monitoring and clinical decisions for better outcomes.
Area of Science:
- Cardiology
- Medical Informatics
- Predictive Analytics
Background:
- Non-ischemic dilated cardiomyopathy (DCM) patients face significant risk of end-stage heart failure (HF).
- Accurate risk prediction is crucial for timely intervention and personalized patient management.
- Current risk stratification tools may require enhancement for DCM-specific populations.
Approach:
- A risk-prediction model was developed using lasso and support vector machine (SVM) algorithms.
- Data from Dutch (n=293) and Czech (n=235) DCM cohorts were used for derivation and external validation.
- Predictors included clinical, ECG, echocardiographic, and biochemical parameters.
Key Points:
- The lasso-derived model identified NYHA class, heart rate, systolic blood pressure, height, R-axis, and TAPSE as key predictors.
- The model demonstrated high discriminative performance (c-statistic 0.85 in derivation, 0.75 in validation).
- Performance surpassed the MAGGIC score (c-statistic 0.69), indicating superior predictive accuracy.
Conclusions:
- A validated, highly discriminative risk-prediction model for end-stage HF in DCM patients was successfully developed.
- The DCM-PROGRESS model shows potential to significantly enhance clinical decision-making and patient monitoring strategies.
- International validation suggests broad applicability in diverse clinical settings.
Aims:
Patients with non-ischemic dilated cardiomyopathy (DCM) are at considerable risk for end-stage heart failure (HF), requiring close monitoring to identify early signs of disease. We aimed to develop a model to predict the 5-years risk of end-stage HF, allowing for tailored patient monitoring and management.
Methods And Results:
Derivation data were available from a Dutch cohort of 293 DCM patients, with external validation available from a Czech Republic cohort of 235 DCM patients. Candidate predictors spanned patient and family histories, ECG and echocardiogram measurements, and biochemistry. End-stage HF was defined as a composite of death, heart transplantation, or implantation of a ventricular assist device. Lasso and sigmoid kernel support vector machine (SVM) algorithms were trained using cross-validation. During follow-up 65 (22%) of Dutch DCM patients developed end-stage HF, with 27 (11%) cases in the Czech cohort. Out of the two considered models, the lasso model (retaining NYHA class, heart rate, systolic blood pressure, height, R-axis, and TAPSE as predictors) reached the highest discriminative performance (testing c-statistic of 0.85, 95%CI 0.58; 0.94), which was confirmed in the external validation cohort (c-statistic of 0.75, 95%CI 0.61; 0.82), compared to a c-statistic of 0.69 for the MAGGIC score. Both the MAGGIC score and the DCM-PROGRESS model slightly over-estimated the true risk, but were otherwise appropriately calibrated.
Conclusion:
We developed a highly discriminative risk-prediction model for end-stage HF in DCM patients. The model was validated in two countries, suggesting the model can meaningfully improve clinical decision-making.
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