MATERIALS AND METHODS: Pro-arrhythmic properties in electrocardiographic and intracellular recordings were compared in young and aged, peroxisome proliferator-activated receptor-γ coactivator-1β knockout (Pgc-1β-/-) and wild type (WT), Langendorff-perfused murine hearts, during regular and programmed stimulation (PES), comparing results by two-way ANOVA.
RESULTS AND DISCUSSION: Young and aged Pgc-1β-/- showed higher frequencies and durations of arrhythmic episodes through wider PES coupling-interval ranges than WT. Both young and old, regularly-paced, Pgc-1β-/- hearts showed slowed maximum action potential (AP) upstrokes, (dV/dt)max (∼157 vs. 120-130 V s-1), prolonged AP latencies (by ∼20%) and shortened refractory periods (∼58 vs. 51 ms) but similar AP durations (∼50 ms at 90% recovery) compared to WT. However, Pgc-1β-/- genotype and age each influenced extrasystolic AP latencies during PES. Young and aged WT ventricles displayed distinct, but Pgc-1β-/- ventricles displayed similar dependences of AP latency upon (dV/dt)max resembling aged WT. They also independently increased myocardial fibrosis. AP wavelengths combining activation and recovery terms paralleled contrasting arrhythmic incidences in Pgc-1β-/- and WT hearts. Mitochondrial dysfunction thus causes pro-arrhythmic Pgc-1β-/- phenotypes by altering AP conduction through reducing (dV/dt)max and causing age-dependent fibrotic change.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
LINKED ARTICLES: This article is part of a themed section on Spotlight on Small Molecules in Cardiovascular Diseases. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.8/issuetoc.
MATERIALS AND METHODS: We tested a hypothesis implicating Na+ current alterations as a mechanism underlying these electrophysiological phenotypes. We applied loose patch-clamp techniques to intact young and aged, WT and Pgc-1β-/-, atrial cardiomyocyte preparations preserving their in vivo extracellular and intracellular conditions.
RESULTS AND DISCUSSION: Depolarising steps activated typical voltage-dependent activating and inactivating inward (Na+) currents whose amplitude increased or decreased with the amplitudes of the activating, or preceding inactivating, steps. Maximum values of peak Na+ current were independently influenced by genotype but not age or interacting effects of genotype and age on two-way ANOVA. Neither genotype, nor age, whether independently or interactively, influenced voltages at half-maximal current, or steepness factors, for current activation and inactivation, or time constants for recovery from inactivation following repolarisation. In contrast, delayed outward (K+) currents showed similar activation and rectification properties through all experimental groups. These findings directly demonstrate and implicate reduced Na+ in contrast to unchanged K+ current, as a mechanism for slowed conduction causing atrial arrhythmogenicity in Pgc-1β-/- hearts.
OBJECTIVE: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores.
METHODS: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined.
RESULTS: Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration.
CONCLUSIONS: In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.