Estimating the probability of early afterdepolarizations and predicting arrhythmic risk associated with long QT syndrome type 1 mutations.

Early afterdepolarizations (EADs) are action potential (AP) repolarization abnormalities that can trigger lethal arrhythmias. Simulations using biophysically detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias; however, such analyses can pose a huge computational burden. We have previously developed a highly simplified approach in which logistic regression models (LRMs) map parameters of complex cell models to the probability of ectopic beats. Here, we extend this approach to predict the probability of EADs (P(EAD)) as a mechanistic metric of arrhythmic risk. We use the LRM to investigate how changes in parameters of the slow-activating delayed rectifier current (IKs) affect P(EAD) for 17 different long QT syndrome type 1 (LQTS1) mutations. In this LQTS1 clinical arrhythmic risk prediction task, we compared P(EAD) for these 17 mutations with two other recently published model-based arrhythmia risk metrics (AP morphology metric across populations of myocyte models and transmural repolarization prolongation based on a one-dimensional [1D] tissue-level model). These model-based risk metrics yield similar prediction performance; however, each fails to stratify clinical risk for a significant number of the 17 studied LQTS1 mutations. Nevertheless, an interpretable ensemble model using multivariate linear regression built by combining all of these model-based risk metrics successfully predicts the clinical risk of 17 mutations. These results illustrate the potential of computational approaches in arrhythmia risk prediction.

Applications of AI in multi-modal imaging for cardiovascular disease.

Data for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic health records. In this paper, we review research from the last 5 years in applications of AI to multi-modal imaging. There have been many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed. Only a few papers have addressed modalities such as x-ray, echocardiography, or non-imaging modalities. As for prediction or classification tasks, there have only been a couple of papers that use multiple modalities in the cardiovascular domain. Furthermore, no models have been implemented or tested in real world cardiovascular clinical settings.

Computational signatures for post-cardiac arrest trajectory prediction: Importance of early physiological time series.

BACKGROUND: There is an unmet need for timely and reliable prediction of post-cardiac arrest (CA) clinical trajectories. We hypothesized that physiological time series (PTS) data recorded on the first day of intensive care would contribute significantly to discrimination of outcomes at discharge. PATIENTS AND METHODS: Adult patients in the multicenter eICU database who were mechanically ventilated after resuscitation from out-of-hospital CA were included. Outcomes of interest were survival, neurological status based on Glasgow motor subscore (mGCS) and surrogate functional status based on discharge location (DL), at hospital discharge. Three machine learning predictive models were trained, one with features from the electronic health records (EHR), the second using features derived from PTS collected in the first 24 h after ICU admission (PTS24), and the third combining PTS24 and EHR. Model performances were compared, and the best performing model was externally validated in the MIMIC-III dataset. RESULTS: Data from 2216 admissions were included in the analysis. Discrimination of prediction models combining EHR and PTS24 features was higher than models using either EHR or PTS24 for prediction of survival (AUROC 0.83, 0.82 and 0.79 respectively), neurological outcome (0.87, 0.86 and 0.79 respectively), and DL (0.80, 0.78 and 0.76 respectively). External validation in MIMIC-III (n = 86) produced similar model performance. Feature analysis suggested prognostic significance of previously unknown EHR and PTS24 variables. CONCLUSION: These results indicate that physiological data recorded in the early phase after CA resuscitation contain signatures that are linked to post-CA outcome. Additionally, they attest to the effectiveness of ML for post-CA predictive modeling.

Estimating ectopic beat probability with simplified statistical models that account for experimental uncertainty.

Ectopic beats (EBs) are cellular arrhythmias that can trigger lethal arrhythmias. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses can pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between the parameters of complex cell models and the probability of EBs (P(EB)). As an example, in this study, we build an LRM for P(EB) as a function of the initial value of diastolic cytosolic Ca2+ concentration ([Ca2+]iini), the initial state of sarcoplasmic reticulum (SR) Ca2+ load ([Ca2+]SRini), and kinetic parameters of the inward rectifier K+ current (IK1) and ryanodine receptor (RyR). This approach, which we refer to as arrhythmia sensitivity analysis, allows for evaluation of the relationship between these arrhythmic event probabilities and their associated parameters. This LRM is also used to demonstrate how uncertainties in experimentally measured values determine the uncertainty in P(EB). In a study of the role of [Ca2+]SRini uncertainty, we show a special property of the uncertainty in P(EB), where with increasing [Ca2+]SRini uncertainty, P(EB) uncertainty first increases and then decreases. Lastly, we demonstrate that IK1 suppression, at the level that occurs in heart failure myocytes, increases P(EB).