A pragmatic lab-based tool for risk assessment in cardiac critical care: data from the Critical Care Cardiology Trials Network (CCCTN) Registry.

AIMS: Contemporary cardiac intensive care unit (CICU) outcomes remain highly heterogeneous. As such, a risk-stratification tool using readily available lab data at time of CICU admission may help inform clinical decision-making. METHODS AND RESULTS: The primary derivation cohort included 4352 consecutive CICU admissions across 25 tertiary care CICUs included in the Critical Care Cardiology Trials Network (CCCTN) Registry. Candidate lab indicators were assessed using multivariable logistic regression. An integer risk score incorporating the top independent lab indicators associated with in-hospital mortality was developed. External validation was performed in a separate CICU cohort of 9716 patients from the Mayo Clinic (Rochester, MN, USA). On multivariable analysis, lower pH [odds ratio (OR) 1.96, 95% confidence interval (CI) 1.72-2.24], higher lactate (OR 1.40, 95% CI 1.22-1.62), lower estimated glomerular filtration rate (OR 1.26, 95% CI 1.10-1.45), and lower platelets (OR 1.18, 95% CI 1.05-1.32) were the top four independent lab indicators associated with higher in-hospital mortality. Incorporated into the CCCTN Lab-Based Risk Score, these four lab indicators identified a 20-fold gradient in mortality risk with very good discrimination (C-index 0.82, 95% CI 0.80-0.84) in the derivation cohort. Validation of the risk score in a separate cohort of 3888 patients from the Registry demonstrated good performance (C-index of 0.82; 95% CI 0.80-0.84). Performance remained consistent in the external validation cohort (C-index 0.79, 95% CI 0.77-0.80). Calibration was very good in both validation cohorts (r = 0.99). CONCLUSION: A simple integer risk score utilizing readily available lab indicators at time of CICU admission may accurately stratify in-hospital mortality risk.

Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain).

Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains [Formula: see text] and [Formula: see text] directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were performed using Monte Carlo computational model simulations, and in-vivo datasets, and compared to the harmonic phase (HARP) method, a conventional and validated method for tMRI analysis, with six different filter settings. Principal strain maps of Monte Carlo tMRI simulations with various anatomical, functional, and imaging parameters demonstrate artifact-free solid agreements with the corresponding ground-truth maps. Correlations with the ground-truth strain maps were R = 0.90 and 0.92 for the best-proposed cGAN approach compared to R = 0.12 and 0.73 for the best HARP method for [Formula: see text] and [Formula: see text], respectively. The proposed cGAN approach's error was substantially lower than the error in the best HARP method at all strain ranges. In-vivo results are presented for both healthy subjects and patients with cardiac conditions (Pulmonary Hypertension). Strain maps, obtained directly from their corresponding tagged MR images, depict for the first time anatomical, functional, and temporal details at pixelwise native high resolution with unprecedented clarity. This work demonstrates the feasibility of using the deep learning cGAN for direct myocardial and liver Eulerian strain mapping from tMRI at native image resolution with minimal artifacts.

COVID-19 and Disruptive Modifications to Cardiac Critical Care Delivery: JACC Review Topic of the Week.

The COVID-19 pandemic has presented a major unanticipated stress on the workforce, organizational structure, systems of care, and critical resource supplies. To ensure provider safety, to maximize efficiency, and to optimize patient outcomes, health systems need to be agile. Critical care cardiologists may be uniquely positioned to treat the numerous respiratory and cardiovascular complications of the SARS-CoV-2 and support clinicians without critical care training who may be suddenly asked to care for critically ill patients. This review draws upon the experiences of colleagues from heavily impacted regions of the United States and Europe, as well as lessons learned from military mass casualty medicine. This review offers pragmatic suggestions on how to implement scalable models for critical care delivery, cultivate educational tools for team training, and embrace technologies (e.g., telemedicine) to enable effective collaboration despite social distancing imperatives.