Duration of cardiopulmonary resuscitation and phenotype of post-cardiac arrest brain injury.

BACKGROUND: Patients resuscitated from cardiac arrest have variable severity of primary hypoxic ischemic brain injury (HIBI). Signatures of primary HIBI on brain imaging and electroencephalography (EEG) include diffuse cerebral edema and burst suppression with identical bursts (BSIB). We hypothesize distinct phenotypes of primary HIBI are associated with increasing cardiopulmonary resuscitation (CPR) duration. METHODS: We identified from our prospective registry of both in-and out-of-hospital CA patients treated between January 2010 to January 2020 for this cohort study. We abstracted CPR duration, neurological examination, initial brain computed tomography gray to white ratio (GWR), and initial EEG pattern. We considered four phenotypes on presentation: awake; comatose with neither BSIB nor cerebral edema (non-malignant coma); BSIB; and cerebral edema (GWR ≤ 1.20). BSIB and cerebral edema were considered as non-mutually exclusive outcomes. We generated predicted probabilities of brain injury phenotype using localized regression. RESULTS: We included 2,440 patients, of whom 545 (23%) were awake, 1,065 (44%) had non-malignant coma, 548 (23%) had BSIB and 438 (18%) had cerebral edema. Only 92 (4%) had both BSIB and edema. Median CPR duration was 16 [IQR 8-28] minutes. Median CPR duration increased in a stepwise manner across groups: awake 6 [3-13] minutes; non-malignant coma 15 [8-25] minutes; BSIB 21 [13-31] minutes; cerebral edema 32 [22-46] minutes. Predicted probability of phenotype changes over time. CONCLUSIONS: Brain injury phenotype is related to CPR duration, which is a surrogate for severity of HIBI. The sequence of most likely primary HIBI phenotype with progressively longer CPR duration is awake, coma without BSIB or edema, BSIB, and finally cerebral edema.

Association of Initial Illness Severity and Outcomes After Cardiac Arrest With Targeted Temperature Management at 36 °C or 33 °C.

Importance: It is uncertain what the optimal target temperature is for targeted temperature management (TTM) in patients who are comatose following cardiac arrest. Objective: To examine whether illness severity is associated with changes in the association between target temperature and patient outcome. Design, Setting, and Participants: This cohort study compared outcomes for 1319 patients who were comatose after cardiac arrest at a single center in Pittsburgh, Pennsylvania, from January 2010 to December 2018. Initial illness severity was based on coma and organ failure scores, presence of severe cerebral edema, and presence of highly malignant electroencephalogram (EEG) after resuscitation. Exposure: TTM at 36 °C or 33 °C. Main Outcomes and Measures: Primary outcome was survival to hospital discharge, and secondary outcomes were modified Rankin Scale and cerebral performance category. Results: Among 1319 patients, 728 (55.2%) had TTM at 33 °C (451 [62.0%] men; median [interquartile range] age, 61 [50-72] years) and 591 (44.8%) had TTM at 36 °C (353 [59.7%] men; median [interquartile range] age, 59 [48-69] years). Overall, 184 of 187 patients (98.4%) with severe cerebral edema died and 234 of 243 patients (96.3%) with highly malignant EEG died regardless of TTM strategy. Comparing TTM at 33 °C with TTM at 36 °C in 911 patients (69.1%) with neither severe cerebral edema nor highly malignant EEG, survival was lower in patients with mild to moderate coma and no shock (risk difference, -13.8%; 95% CI, -24.4% to -3.2%) but higher in patients with mild to moderate coma and cardiopulmonary failure (risk difference, 21.8%; 95% CI, 5.4% to 38.2%) or with severe coma (risk difference, 9.7%; 95% CI, 4.0% to 15.3%). Interactions were similar for functional outcomes. Most deaths (633 of 968 [65.4%]) resulted after withdrawal of life-sustaining therapies. Conclusions and Relevance: In this study, TTM at 33 °C was associated with better survival than TTM at 36 °C among patients with the most severe post-cardiac arrest illness but without severe cerebral edema or malignant EEG. However, TTM at 36 °C was associated with better survival among patients with mild- to moderate-severity illness.

Factors associated with advanced cardiac care in prehospital chest pain patients.

INTRODUCTION: Many patients transported by emergency medical services (EMS) may require advanced cardiac care but do not have ST-segment elevation (STEMI) on the initial prehospital EKG. We sought to identify factors associated with the need for advanced cardiac care in undifferentiated EMS patients reporting chest pain in the absence of STEMI on EKG. METHODS: We performed a retrospective analysis of all adult patients, reporting atraumatic chest pain from a single EMS agency, presenting to a single, urban hospital over a 10-year period. Patients with STEMI on prehospital electrocardiogram were excluded. Patient demographics, chest pain characteristics and prehospital factors were abstracted for all patients. We identified those patients that required advanced cardiac care and performed regression analysis to determine associated factors. RESULTS: A total of 956 charts were analyzed. Of this total, 193 patients (20.2%) met the primary composite outcome. Of the outcome group, 185 patients (95.9%) had coronary artery disease documented on cardiac catheterization, 22 patients (11.4%) underwent CABG, and seven patients (3.6%) died in the hospital. Most significant variables (multivariable IRR) included age (1.02), male gender (1.65), history of MI (1.47), PCI (1.66), hyperlipidemia (1.40), diaphoresis (1.51), home aspirin (1.53), and improvement with EMS treatment (1.60). CONCLUSION: We have identified several factors that could be considered when risk stratifying prehospital patients reporting chest pain. While potentially predictive, the factors are broad and support the need for other objective factors that could augment prediction of patients who may benefit from early advanced cardiac care.

A Novel Artificial Intelligence System for Endotracheal Intubation.

OBJECTIVE: Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/artificial intelligence has helped to automate the detection of other visual structures but its utility with ETI is unknown. We sought to test the accuracy of various computer algorithms in identifying the glottic opening, creating a tool that could aid successful intubation. METHODS: We collected a convenience sample of providers who each performed ETI 10 times on a mannequin using a video laryngoscope (C-MAC, Karl Storz Corp, Tuttlingen, Germany). We recorded each attempt and reviewed one-second time intervals for the presence or absence of the glottic opening. Four different machine learning/artificial intelligence algorithms analyzed each attempt and time point: k-nearest neighbor (KNN), support vector machine (SVM), decision trees, and neural networks (NN). We used half of the videos to train the algorithms and the second half to test the accuracy, sensitivity, and specificity of each algorithm. RESULTS: We enrolled seven providers, three Emergency Medicine attendings, and four paramedic students. From the 70 total recorded laryngoscopic video attempts, we created 2,465 time intervals. The algorithms had the following sensitivity and specificity for detecting the glottic opening: KNN (70%, 90%), SVM (70%, 90%), decision trees (68%, 80%), and NN (72%, 78%). CONCLUSIONS: Initial efforts at computer algorithms using artificial intelligence are able to identify the glottic opening with over 80% accuracy. With further refinements, video laryngoscopy has the potential to provide real-time, direction feedback to the provider to help guide successful ETI.