Abstract Wed133: Cardiorenal Interaction Assessment via ECG Features: A Study using Dynamic Time Warping and Extracted Feature Clustering

Background: The heart and kidneys have vital functions in the human body that reciprocally influence each physiologically and pathological changes in one organ can damage the other. Epidemiologic studies show that greater than 50% of patients with heart failure (HF) have preserved ejection fraction (HFpEF). Additionally, one in six patients identified as having chronic kidney disease (CKD) also has HF. Thus, it is important to be able to predict and identify the cardiorenal relationship between HFpEF and CKD. We assessed the hypoyhesis that this cardiorenal relationship may be demonstrated using ECG data. Objective: Creating an ECG-enabled model that stratifies HFpEF patients would help identify CKD enriched HFpEF clusters and phenogroups. Simultaneously, a minimal set of significant ECG features derived from the stratification model would aid precision medicine and practical diagnoses due to being more accessible and widely readable than a large set of clinical inputs. Methods: Using unsupervised clustering on all extractable ECG features from FinnGen, HFpEF patients (n=500) were grouped into different phenogroups and analyzed for CKD risk. After isolating significant predictive ECG features, unsupervised clustering and risk analysis were performed again to demonstrate the efficacy of using a minimal set of features for phenogrouping. These clusters were then compared to clusters formed using Dynamic Time Warping (DTW) on raw ECG time series electrical signals. Results: In conclusion, several HFpEF clusters exhibited a deviation of CKD risk from baseline which may allow for further trajectory analysis. The DTW generated clusters were more stable than either sets of clusters formed on the minimal set of extracted ECG features or all extracted ECG features. PR interval and QRS duration stood out as significant features.