Clinical cases.

<p><b>Panel 1</b>. A: ECG at admission. B: ECG at 6^th day. C, E: T2-weighted images of basal and mid-short axis views, respectively. The increased myocardial signal intensity (arrows) indicates increased water content, hence tissue edema in the anterior, antero-septal and infero-septal walls. D, F: late gadolinium enhancement (LGE) images (basal and mid-short axis views respectively). Necrosis (arrows) and microvascular obstruction (arrowhead) are shown. The edematous myocardial content was 62gr, corresponding to 56% of total left ventricular mass, and LGE was 59gr, corresponding to 54% of total left ventricular mass. The myocardial salvage index (MSI) was 0.05. ΔQTc-AI-MA (max anterior QTc—min inferior QTc) was 200msec. <b>Panel 2</b>. A: ECG at admission. B: ECG at 6^th day. C, E: T2-weighted images of basal and mid-short axis views, respectively. The increased myocardial signal intensity (arrows) indicates tissue edema in the anterior and antero-septal walls. D, F: LGE images (basal and mid-short axis views, respectively). The edematous myocardial content was 30gr, corresponding to 25% of total left ventricular mass. No LGE was evident. The MSI was 1. No ΔQTc-AI-MA (max anterior QTc—min inferior QTc) was present.</p

QT-interval evaluation in primary percutaneous coronary intervention of ST-segment elevation myocardial infarction for prediction of myocardial salvage index

<div><p>Assessing the efficacy of revascularization therapy in patients with ST-segment elevation myocardial infarction (STEMI) is extremely important in order to guide subsequent management and assess prognosis. We aimed to determine the relationship between corrected QT-interval (QTc) changes on standard sequential ECG and myocardial salvage index in anterior STEMI patients after successful primary percutaneous coronary intervention. Fifty anterior STEMI patients treated by primary percutaneous coronary intervention underwent quantitative ECG analysis and cardiac magnetic resonance. For each patient the difference (ΔQTc) between the QTc of ischemic myocardium (maximum QTc in anterior leads) versus remote myocardium (minimum QTc in inferior leads) during the first six days after STEMI was measured. The QTc in anterior leads was significantly longer than QTc in inferior leads (p<0.0001). At multivariate analysis, ΔQT_C and peak troponin I were the only independent predictors for late gadolium enhancement while ΔQTc and left ventricular ejection fraction were independent predictors of myocardial salvage index <60%. The receiver operative curve of ΔQTc showed an area under the curve of 0.77 to predict a myocardial salvage index <0.6. In conclusion, in a subset of patients with a first occurrence of early revascularized anterior STEMI, ΔQTc is inversely correlated with CMR-derived myocardial salvage index and may represent a useful parameter for assessing efficacy of reperfusion therapy.</p></div

Baseline characteristics of patient population.

<p>Baseline characteristics of patient population.</p

Multivariate logistic regression for MSI <60%.

<p>Multivariate logistic regression for MSI <60%.</p

CMR characteristics.

<p>CMR characteristics.</p

Multivariate linear regression analysis for log-transformed LGE (gr).

<p>Multivariate linear regression analysis for log-transformed LGE (gr).</p

ROC curve for ΔQT AI MA at day six versus MSI <60%.

<p>ΔQTc AI MA: delta QT corrected antero-inferior max; MSI = myocardial salvage index.</p

Multivariate linear regression for log-transformed LGE (%).

<p>Multivariate linear regression for log-transformed LGE (%).</p

Anterior and inferior QTc derivation over time.

<p>Anterior (panel A), inferior (panel B) QTc derivations and ΔQTc-AI-MA (panel C) at different time points expressed as mean and standard error. PCI: percutaneous coronary intervention; ΔQTc-AI-MA: max anterior QTc—min inferior QTc.</p

Data_Sheet_1_Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.CSV

IntroductionElevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance.Materials and methodsPatients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid.ResultsThe cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2.ConclusionThis analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.</p