Markov model for detection of ECG instability prior to cardiac arrest in single-ventricle patients.

OBJECTIVES: Assess the degree of instability in the electrocardiogram (ECG) waveform in patients with single-ventricle physiology before a cardiac arrest and compare them with similar patients who did not experience a cardiac arrest. METHODS: Retrospective control study in patients with single-ventricle physiology who underwent Norwood, Blalock-Taussig shunt, pulmonary artery band, and aortic arch repair from 2013 to 2018. Electronic medical records were obtained for all included patients. For each subject, 6 h of ECG data were analyzed. In the arrest group, the end of the sixth hour coincides with the cardiac arrest. In the control group, the 6-h windows were randomly selected. We used a Markov chain framework and the likelihood ratio test to measure the degree of ECG instability and to classify the arrest and control groups. RESULTS: The study dataset consists of 38 cardiac arrest events and 67 control events. Our Markov model was able to classify the arrest and control groups based on the ECG instability with an ROC AUC of 82% at the hour preceding the cardiac arrests. CONCLUSION: We designed a method using the Markov chain framework to measure the level of instability in the beat-to-beat ECG morphology. Furthermore, we were able to show that the Markov model performed well to distinguish patients in the arrest group compared to the control group.

Quantification of electrocardiogram instability prior to cardiac arrest in patients with single-ventricle physiology.

OBJECTIVE: To quantify the instability measured in the electrocardiogram (ECG) waveform in patients with single-ventricle physiology before a cardiac arrest and compare with similar patients who did not have a cardiac arrest. METHODS: We measure the instability in the ECG morphology using variance, entropy, and decorrelation of polynomial fit coefficients of the beat-to-beat segmented data. These three metrics quantify the spread of the ECG morphology, the lack of beat-to-beat periodicity and the lack of predictability, respectively. For each subject, 3 h of ECG data were analyzed. In the arrest group, the end of the third hour coincides with the cardiac arrest. In the control group, the 3-h windows were randomly selected. RESULTS: The study dataset consists of 38 cardiac arrest events and 67 control events. In the hour prior to the cardiac arrest, the variance, entropy, and decorrelation of the polynomial fit coefficients were higher in the arrest group than in the control group (p = 0.003, p = 0.009, and p = 0.035, respectively). For the second and third hours prior to the arrests, the differences in variance, entropy, and decorrelation between the arrest and control groups lost statistical significance. Using these metrics of instability as predictive features in a support vector machine algorithm, we found an area under the receiver operating characteristic curve of 0.8 to distinguish the arrest event from the control events. CONCLUSION: By taking a holistic assessment of the ECG waveform in patients with single-ventricle physiology to measure the instability in its beat-to-beat morphology, the ECG waveform variance, entropy, and decorrelation are found to be statistically different in the patients who arrested compared with patients in the control group.

Development of a Perfusion Reactor for Intervertebral Disk Regeneration.

A tissue-engineered biological disk replacement has been proposed as a promising approach for the treatment of degenerative disk disease. A perfusion bioreactor would be a logical consideration to facilitate this scale-up as such reactors have been shown to improve nutrient delivery and provide beneficial mechanical forces that support the cultivation of large three-dimensional constructs. It was hypothesized that perfusion culture of tissue-engineered intervertebral disk (IVD) tissues would be capable of generating outer annulus fibrosus (oAF) and nucleus pulposus (NP) tissues comparable with established spinner reactor or static cultures, respectively, without compromising cellular viability, nutrient delivery, and tissue formation. In this study, the perfusion grown oAF and NP tissues did not show a significant difference in extracellular matrix (ECM) quantity or cellular phenotype when compared with their control conditions. In addition, they maintained cellular viability at the center core of the tissues and received enhanced diffusion of medium throughout the tissue when compared with static conditions. This study lays the groundwork for future studies to grow an entire IVD tissue to a physiologically relevant size.

PATRIC as a unique resource for studying antimicrobial resistance.

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other 'omic' data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Antimicrobial Resistance Prediction in PATRIC and RAST.

The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88-99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71-88%. This set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.