Bartos, Jason A.

Introduction: Gray-white matter ratio (GWR) on non-contrast head computed tomography (HCT) is a helpful tool in neuro-prognostication in post-cardiac arrest patients. Its usage is unclear for those with angiography due to residual contrast. Hypothesis: GWR remains valid for neuro-prognostication in comatose patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR) followed by coronary angiography (CAG) after out-hospital cardiac arrest (OHCA). Methods: We performed a retrospective study of comatose OHCA adults post ECPR and CAG at University of Minnesota from January to June 2022 with dual energy HCT within 12 hours. GWR was measured at basal ganglia, centrum semiovale, and high convexity levels. Gray matter areas of interest included caudate nucleus, putamen, thalamus, and medial cortex. The white matter areas of interest included the genu of the corpus callosum, posterior limb of the internal capsule, and medial white matter. Circular regions of interest (ROI, 10-15 mm 2 ) were placed bilaterally in corresponding areas to record the average attenuation in Hounsfield unit (HU). The basal ganglia, cerebrum, and overall GWRs were calculated as the ratio of average HU between the gray and white matter ROIs within the area. Primary outcome was inpatient death, further categorized by the primary cause being neurological or not. Logistic regression followed by receiver operating characteristic (ROC) curves was used to validate GWR in predicting death. All data analysis was performed by StataCorp Software (STATA 14). Result: 25 patients were included in the study. 18 (72%) were male with a median age of 60 (interquartile range (IQR): 45-67). The initial HCT was obtained at 2.08 hours (median, IQR: 1.37–2.43) post ECPR cannulation. 7 (28%) patients died from brain death or herniation and 15 (60%) died from non-neurological causes. None had intracranial bleeding. 1 patient had unilateral hypodensity on initial HCT, suggesting an acute embolic infarct. Patients who died had lower GWRs on initial HCT (Figure 1). ROC regression showed all GWRs demonstrated good prognostic performance on inpatient death with area under the curve (AUC) of 0.6970 (basal ganglia), 0.8485 (cerebrum), and 0.7576 (overall) (Figure 2). Conclusions: GWR in early HCT has prognostic value in predicting neurological outcomes despite residual contrast. It may be used as a risk stratification tool for comatose OHCA patients undergoing ECPR and angiography.

OBJECTIVES: Extracorporeal cardiopulmonary resuscitation (ECPR) has been shown to improve neurologically favorable survival in patients with refractory out-of-hospital cardiac arrest (OHCA) caused by shockable rhythms. Further refinement of patient selection is needed to focus this resource-intensive therapy on those patients likely to benefit. This study sought to create a selection model using machine learning (ML) tools for refractory cardiac arrest patients undergoing ECPR. DESIGN: Retrospective cohort study. SETTING: Cardiac ICU in a Quaternary Care Center. PATIENTS: Adults 18–75 years old with refractory OHCA caused by a shockable rhythm. METHODS: Three hundred seventy-six consecutive patients with refractory OHCA and a shockable presenting rhythm were analyzed, of which 301 underwent ECPR and cannulation for venoarterial extracorporeal membrane oxygenation. Clinical variables that were widely available at the time of cannulation were analyzed and ranked on their ability to predict neurologically favorable survival. INTERVENTIONS: ML was used to train supervised models and predict favorable neurologic outcomes of ECPR. The best-performing models were internally validated using a holdout test set. MEASUREMENTS AND MAIN RESULTS: Neurologically favorable survival occurred in 119 of 301 patients (40%) receiving ECPR. Rhythm at the time of cannulation, intermittent or sustained return of spontaneous circulation, arrest to extracorporeal membrane oxygenation perfusion time, and lactic acid levels were the most predictive of the 11 variables analyzed. All variables were integrated into a training model that yielded an in-sample area under the receiver-operating characteristic curve (AUC) of 0.89 and a misclassification rate of 0.19. Out-of-sample validation of the model yielded an AUC of 0.80 and a misclassification rate of 0.23, demonstrating acceptable prediction ability. CONCLUSIONS: ML can develop a tiered risk model to guide ECPR patient selection with tailored arrest profiles.

Cardiogenic shock continues to carry a high mortality rate despite contemporary care, with no breakthrough therapies shown to improve survival over the past few decades. It is a time‐sensitive condition that commonly results in cardiovascular complications and multisystem organ failure, necessitating multidisciplinary expertise. Managing patients with cardiogenic shock remains challenging even in well‐resourced settings, and an important subgroup of patients may require cardiac replacement therapy. As a result, the idea of leveraging the collective cognitive and procedural proficiencies of multiple providers in a collaborative, team‐based approach to care (the “shock team”) has been advocated by professional societies and implemented at select high‐volume clinical centers. A slowly maturing evidence base has suggested that cardiogenic shock teams may improve patient outcomes. Although several registries exist that are beginning to inform care, particularly around therapeutic strategies of pharmacologic and mechanical circulatory support, none of these are currently focused on the shock team approach, multispecialty partnership, education, or process improvement. We propose the creation of a Cardiogenic Shock Team Collaborative—akin to the successful Pulmonary Embolism Response Team Consortium—with a goal to promote sharing of care protocols, education of stakeholders, and discovery of how process and performance may influence patient outcomes, quality, resource consumption, and costs of care.