Erratum to "BME 2.0: Engineering the Future of Medicine".

[This corrects the article DOI: 10.34133/bmef.0001.

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.

A data-driven model for early prediction of need for invasive mechanical ventilation in pediatric intensive care unit patients.

Rationale: Acute respiratory failure is a life-threatening clinical outcome in critically ill pediatric patients. In severe cases, patients can require mechanical ventilation (MV) for survival. Early recognition of these patients can potentially help clinicians alter the clinical course and lead to improved outcomes.

Predicting respiratory decompensation in mechanically ventilated adult ICU patients.

Mechanical ventilation is a life-saving treatment in the Intensive Care Unit (ICU), but often causes patients to be at risk of further respiratory complication. We created a statistical model utilizing electronic health record and physiologic vitals data to predict the Center for Disease Control and Prevention (CDC) defined Ventilator Associated Complications (VACs). Further, we evaluated the effect of data temporal resolution and feature generation method choice on the accuracy of such a constructed model.

Predicting Intensive Care Delirium with Machine Learning: Model Development and External Validation.

Background: Delirium poses significant risks to patients, but countermeasures can be taken to mitigate negative outcomes. Accurately forecasting delirium in intensive care unit (ICU) patients could guide proactive intervention. Our primary objective was to predict ICU delirium by applying machine learning to clinical and physiologic data routinely collected in electronic health records.

Critical Requirements for the Initiation of a Cardiac Arrhythmia in Rat Ventricle: How Many Myocytes?

Cardiovascular disease is the leading cause of death worldwide due in a large part to arrhythmia. In order to understand how calcium dynamics play a role in arrhythmogenesis, normal and dysfunctional Ca signaling in a subcellular, cellular, and tissued level is examined using cardiac ventricular myocytes at a high temporal and spatial resolution using multiscale computational modeling. Ca sparks underlie normal excitation-contraction coupling.

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.

SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction.

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs.

Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.

High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores, such as the respiratory rate-oxygenation (ROX) index, have been described as a means to predict HFNC non-response, but are limited to evaluating for escalations to invasive mechanical ventilation (MV).

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)).

Early Prediction of Multiple Organ Dysfunction in the Pediatric Intensive Care Unit.

The objective of the study is to build models for early prediction of risk for developing multiple organ dysfunction (MOD) in pediatric intensive care unit (PICU) patients. The design of the study is a retrospective observational cohort study. The setting of the study is at a single academic PICU at the Johns Hopkins Hospital, Baltimore, MD.

Prediction of Impending Septic Shock in Children With Sepsis.

Objectives: Sepsis and septic shock are leading causes of in-hospital mortality. Timely treatment is crucial in improving patient outcome, yet treatment delays remain common. Early prediction of those patients with sepsis who will progress to its most severe form, septic shock, can increase the actionable window for interventions.

SWIFT: A Deep Learning Approach to Prediction of Hypoxemic Events in Critically-Ill Patients Using SpO Waveform Prediction.

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO W aveform I CU F orecasting T echnique), a deep learning model that predicts blood oxygen saturation (SpO ) waveforms 5 and 30 minutes in the future using only prior SpO values as inputs.

Spectral clustering of risk score trajectories stratifies sepsis patients by clinical outcome and interventions received.

Sepsis is not a monolithic disease, but a loose collection of symptoms with diverse outcomes. Thus, stratification and subtyping of sepsis patients is of great importance. We examine the temporal evolution of patient state using our previously-published method for computing risk of transition from sepsis into septic shock.

Natural Language Processing of Clinical Notes for Improved Early Prediction of Septic Shock in the ICU.

Sepsis and septic shock are major concerns in public health as the leading contributors to hospital mortality and cost of treatment in the United States. Early treatment is instrumental for improving patient outcome; to this end, algorithmic methods for early prediction of septic shock have been developed using electronic health record data, with the goal of decreasing treatment delay. We extend a previously-developed method, using a gradient boosting algorithm (XG-Boost) to compute a time-evolving risk of impending transition into septic shock, by combining physiological data from the electronic health record with features obtained from natural language processing of clinical note data.

Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU.

Septic shock is a life-threatening condition in which timely treatment substantially reduces mortality. Reliable identification of patients with sepsis who are at elevated risk of developing septic shock therefore has the potential to save lives by opening an early window of intervention. We hypothesize the existence of a novel clinical state of sepsis referred to as the "pre-shock" state, and that patients with sepsis who enter this state are highly likely to develop septic shock at some future time.

Na microdomains and sparks: Role in cardiac excitation-contraction coupling and arrhythmias in ankyrin-B deficiency.

Cardiac sodium (Na) potassium ATPase (NaK) pumps, neuronal sodium channels (I), and sodium calcium (Ca) exchangers (NCX1) may co-localize to form a Na microdomain. It remains controversial as to whether neuronal I contributes to local Na accumulation, resulting in reversal of nearby NCX1 and influx of Ca into the cell. Therefore, there has been great interest in the possible roles of a Na microdomain in cardiac Ca-induced Ca release (CICR).

A bilobal model of Ca-dependent inactivation to probe the physiology of L-type Ca channels.

L-type calcium channels (LTCCs) are critical elements of normal cardiac function, playing a major role in orchestrating cardiac electrical activity and initiating downstream signaling processes. LTCCs thus use feedback mechanisms to precisely control calcium (Ca) entry into cells. Of these, Ca-dependent inactivation (CDI) is significant because it shapes cardiac action potential duration and is essential for normal cardiac rhythm.

Estimating the probabilities of rare arrhythmic events in multiscale computational models of cardiac cells and tissue.

Ectopic heartbeats can trigger reentrant arrhythmias, leading to ventricular fibrillation and sudden cardiac death. Such events have been attributed to perturbed Ca2+ handling in cardiac myocytes leading to spontaneous Ca2+ release and delayed afterdepolarizations (DADs). However, the ways in which perturbation of specific molecular mechanisms alters the probability of ectopic beats is not understood.

WaveformECG: A Platform for Visualizing, Annotating, and Analyzing ECG Data.

The electrocardiogram (ECG) is the most commonly collected data in cardiovascular research because of the ease with which it can be measured and because changes in ECG waveforms reflect underlying aspects of heart disease. Accessed through a browser, WaveformECG is an open source platform supporting interactive analysis, visualization, and annotation of ECGs.

Mechanisms of the cyclic nucleotide cross-talk signaling network in cardiac L-type calcium channel regulation.

Regulation of L-type Calcium (Ca) Channel (LCC) gating is critical to shaping the cardiac action potential (AP) and triggering the initiation of excitation-contraction (EC) coupling in cardiac myocytes. The cyclic nucleotide (cN) cross-talk signaling network, which encompasses the β-adrenergic and the Nitric Oxide (NO)/cGMP/Protein Kinase G (PKG) pathways and their interaction (cross-talk) through distinctively-regulated phosphodiesterase isoenzymes (PDEs), regulates LCC current via Protein Kinase A- (PKA) and PKG-mediated phosphorylation. Due to the tightly-coupled and intertwined biochemical reactions involved, it remains to be clarified how LCC gating is regulated by the signaling network from receptor to end target.

Metadata-driven Clinical Data Loading into i2b2 for Clinical and Translational Science Institutes.

Clinical and Translational Science Award (CTSA) recipients have a need to create research data marts from their clinical data warehouses, through research data networks and the use of i2b2 and SHRINE technologies. These data marts may have different data requirements and representations, thus necessitating separate extract, transform and load (ETL) processes for populating each mart. Maintaining duplicative procedural logic for each ETL process is onerous.

Modeling Na-Ca exchange in the heart: Allosteric activation, spatial localization, sparks and excitation-contraction coupling.

The cardiac sodium (Na)/calcium (Ca) exchanger (NCX1) is an electrogenic membrane transporter that regulates Ca homeostasis in cardiomyocytes, serving mainly to extrude Ca during diastole. The direction of Ca transport reverses at membrane potentials near that of the action potential plateau, generating an influx of Ca into the cell. Therefore, there has been great interest in the possible roles of NCX1 in cardiac Ca-induced Ca release (CICR).

Models and Simulations as a Service: Exploring the Use of Galaxy for Delivering Computational Models.

We describe the ways in which Galaxy, a web-based reproducible research platform, can be used for web-based sharing of complex computational models. Galaxy allows users to seamlessly customize and run simulations on cloud computing resources, a concept we refer to as Models and Simulations as a Service (MaSS). To illustrate this application of Galaxy, we have developed a tool suite for simulating a high spatial-resolution model of the cardiac Ca(2+) spark that requires supercomputing resources for execution.