QT variability improves risk stratification in patients with dilated cardiomyopathy
C Fischer1, A Seeck, R Schroeder
1Department of Medical Engineering and Biotechnology, University of Applied Sciences Jena, Germany.
Insights
QT variability (QTV) analysis improves risk stratification in dilated cardiomyopathy (DCM) patients. Combining QTV with blood pressure variability (BPV) and clinical data offers superior prediction of sudden cardiac death risk.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Systolic and diastolic blood pressure variability (BPV) and segmented Poincare plot analysis (SPPA) aid risk stratification in dilated cardiomyopathy (DCM).
- Traditional heart rate variability analysis is insufficient for risk stratification in DCM patients.
- There is a need for improved risk stratification methods in DCM.
Purpose of the Study:
- To enhance risk stratification in DCM patients using a multivariate technique incorporating QT variability (QTV).
- To evaluate the effectiveness of combining BPV, SPPA, and QTV for predicting adverse outcomes in DCM.
Main Methods:
- Recruited 56 low-risk and 13 high-risk DCM patients.
- Applied various BPV and QTV methods, including symbolic dynamics and segmented Poincare plot analysis.
- Utilized multivariate discriminate analysis (DA) with different combinations of indices.
Main Results:
- A multivariate DA model using one BPV index (DBP(Shannon)) and one QTV index (QTV(log)) achieved an AUC of 92%, sensitivity of 92.3%, and specificity of 89.3%.
- An electrocardiogram-only analysis combining SPPA and QTV yielded an AUC of 88.3%.
- A three-index model (clinical, BPV, QTV) reached an AUC of 95.7%, with 100% sensitivity and 85.7% specificity.
Conclusions:
- QT variability (QTV) analysis in a multivariate approach significantly improves risk stratification for DCM patients.
- The combination of QTV with BPV and clinical data enhances the prediction of sudden cardiac death risk in DCM.
- Multivariate analysis incorporating QTV offers a promising tool for identifying high-risk DCM patients.
Abstract:
Recently it could be demonstrated that systolic and diastolic blood pressure variability (BPV) as well as segmented Poincare plot analysis (SPPA) contribute to risk stratification in patients suffering from dilated cardiomyopathy (DCM). The aim of this study was to improve the risk stratification applying a multivariate technique including QT variability (QTV). We enrolled and significantly separated 56 low risk and 13 high risk DCM patients by nearly all applied BPV and QTV methods, but not with traditional heart rate variability analysis. The optimum set of two indices calculating the multivariate discriminate analysis (DA) included one BPV index calculated by symbolic dynamics method (DBP(Shannon)) and one index calculated from QTV (QTV(log)) achieving an area under the receiver operating characteristics curve (AUC) of 92%, sensitivity of 92.3% and specificity of 89.3%. Performing only electrocardiogram analysis, the optimum multivariate approach including indices from segmented Poincaré plot analysis and QTV still achieved a remarkable AUC of 88.3%. Increasing the number of indices for multivariate DA up to three, we achieved an AUC of 95.7%, sensitivity of 100% and specificity of 85.7% including one clinical, one BPV and one QTV index. Summarizing, we identified DCM patients with an increased risk of sudden cardiac death applying QTV analysis in a multivariate approach.
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