Extracorporeal Cardiopulmonary Resuscitation: A Narrative Review and Establishment of a Sustainable Program.

The rates of survival with functional recovery for out of hospital cardiac arrest remain unacceptably low. Extracorporeal cardiopulmonary resuscitation (ECPR) quickly resolves the low-flow state of conventional cardiopulmonary resuscitation (CCPR) providing valuable perfusion to end organs. Observational studies have shown an association with the use of ECPR and improved survivability. Two recent randomized controlled studies have demonstrated improved survival with functional neurologic recovery when compared to CCPR. Substantial resources and coordination amongst different specialties and departments are crucial for the successful implementation of ECPR. Standardized protocols, simulation based training, and constant communication are invaluable to the sustainability of a program. Currently there is no standardized protocol for the post-cannulation management of these ECPR patients and, ideally, upcoming studies should aim to evaluate these protocols.

Positive oral contrast material for CT evaluation of non-traumatic abdominal pain in the ED: prospective assessment of diagnostic confidence and throughput metrics.

OBJECTIVE: Evaluate the impact of positive oral contrast material (POCM) for non-traumatic abdominal pain on diagnostic confidence, diagnostic rate, and ED throughput. MATERIALS AND METHODS: ED oral contrast guidelines were changed to limit use of POCM. A total of 2,690 abdominopelvic CT exams performed for non-traumatic abdominal pain were prospectively evaluated for diagnostic confidence (5-point scale at 20% increments; 5 = 80-100% confidence) during a 24-month period. Impact on ED metrics including time from CT order to exam, preliminary read, ED length of stay (LOS), and repeat CT scan within 7 days was assessed. A subset of cases (n = 729) was evaluated for diagnostic rate. Data were collected at 2 time points, 6 and 24 months following the change. RESULTS: A total of 38 reviewers were participated (28 trainees, 10 staff). 1238 exams (46%) were done with POCM, 1452 (54%) were performed without POCM. For examinations with POCM, 80% of exams received a diagnostic confidence score of 5 (mean, 4.78 ± 0.43; 99% ≥ 4), whereas 60% of exams without POCM received a score of 5 (mean, 4.51 ± 0.70; 92% ≥ 4; p < .001). Trainees scored 1,523 exams (57%, 722 + POCM, 801 -POCM) and showed even lower diagnostic confidence in cases without PCOM compared with faculty (mean, 4.43 ± 0.68 vs. 4.59 ± 0.71; p < 0.001). Diagnostic rate in a randomly selected subset of exams (n = 729) was 54.2% in the POCM group versus 56.1% without POCM (p < 0.655). CT order to exam time decreased by 31 min, order to preliminary read decreased by 33 min, and ED LOS decreased by 30 min (approximately 8% of total LOS) in the group without POCM compared to those with POCM (p < 0.001 for all). 205 patients had a repeat scan within 7 days, 74 (36%) had IV contrast only, 131 (64%) had both IV and oral contrast on initial exam. Findings were consistent both over a 6-month evaluation period as well as the full 24-month study period. CONCLUSION: Limiting use of POCM in the ED for non-traumatic abdominal pain improved ED throughput but impaired diagnostic confidence, particularly in trainees; however, it did not significantly impact diagnostic rates nor proportion of repeat CT exams.

Postprandial Hypotension in the Intensive Care Unit Due To Portal Hypertension and Coronavirus Disease 2019: A Case Report.

A 49-year-old man with cirrhosis and portal hypertension was admitted for acute respiratory distress syndrome secondary to coronavirus disease 2019 (COVID-19) pneumonia. His course was complicated by postprandial hypotension (PPH)-episodic hemodynamic collapse that occurred minutes after enteral administration of medications or fluids. Octreotide, which reduces splanchnic pooling and can treat PPH, successfully prevented ongoing events. PPH is associated with mortality in the outpatient setting, and at-risk patients include the elderly and those with autonomic dysfunction, including those with COVID-19. Portal hypertension is a likely additional risk factor that has not been previously described. Octreotide is the mainstay of PPH prophylaxis.

Comparison and interpretability of machine learning models to predict severity of chest injury.

OBJECTIVE: Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at nontrauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting. MATERIALS AND METHODS: A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net (EN) regularization, extreme gradient boosted (XGB) machines, and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations. RESULTS: Of 8952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." DISCUSSION: The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. CONCLUSION: NLP and ML methods to populate trauma registries for quality analyses are feasible.

Prediction of severe chest injury using natural language processing from the electronic health record.

INTRODUCTION: Trauma injury severity scores are currently calculated retrospectively from the electronic health record (EHR) using manual annotation by certified trauma coders. Natural language processing (NLP) of clinical documents in the EHR may enable automated injury scoring. We hypothesize that NLP with machine learning can discriminate between cases of severe and non-severe injury to the thorax after trauma. METHODS: Clinical documents from a trauma center were examined between 2014 and 2018. Severe chest injury was defined as a thorax abbreviated injury score (AIS) >2 and served as the reference standard for supervised learning. Free text unigrams and concept unique identifiers (CUIs) from the Unified Medical Language Systems (UMLS) were extracted from clinical documents collected at one hour, four hours, and eight hours after patient arrival to the emergency department. Logistic regression models with elastic net regularization were tuned to maximize area under the receiver operating characteristic curve (AUROC) using 10-fold cross-validation on the training dataset (80%) and tested on a hold-out 20% dataset. RESULTS: There were 6,891 traumas that met inclusion criteria. The complete data corpus consisted of 473,694 documents. Models trained using the first hour of data had a mean AUROC of 0.88 (95%CI [0.86, 0.89]); model discrimination and reclassification from the first hour significantly improved after eight hours with a mean AUROC of 0.94 (95%CI [0.93, 0.95]). Performance of models using CUIs were similar to unigrams (p>0.05). Models demonstrated excellent clinical face validity. CONCLUSIONS: Both CUIs and unigrams demonstrated excellent discrimination in predicting severity of chest injury using the first eight hours of clinical documents. Our model demonstrates that automated anatomical injury scoring is feasible and may be used for aggregation of data for trauma research and quality programs.

Dead in the air- The need to adapt to CoVID adaptations.

During the recent CoVID-19 pandemic, airway management recommendations have been provided to decrease aerosolization and risk of viral spread to healthcare providers. High efficiency particulate air (HEPA) viral filters and adaptors are one way to decrease the risk of aerosolization during intubation. When placed proximal to the ventilator circuit, these viral filters and adaptors can create a significant amount of dead space, which in our smallest patients can significantly impact effective ventilation. We report a case of hypoventilation in a pediatric patient due to lack of provider team appreciation or ventilator sensing of additional dead space due to HEPA viral filter and adaptor.

Electronic Health Record-Based Surveillance for Community Transmitted COVID-19 in the Emergency Department.

INTRODUCTION: SARS-CoV-2, a novel coronavirus, manifests as a respiratory syndrome (COVID-19) and is the cause of an ongoing pandemic. The response to COVID-19 in the United States has been hampered by an overall lack of diagnostic testing capacity. To address uncertainty about ongoing levels of SARS-CoV-2 community transmission early in the pandemic, we aimed to develop a surveillance tool using readily available emergency department (ED) operations data extracted from the electronic health record (EHR). This involved optimizing the identification of acute respiratory infection (ARI)-related encounters and then comparing metrics for these encounters before and after the confirmation of SARS-CoV-2 community transmission. METHODS: We performed an observational study using operational EHR data from two Midwest EDs with a combined annual census of over 80,000. Data were collected three weeks before and after the first confirmed case of local SARS-CoV-2 community transmission. To optimize capture of ARI cases, we compared various metrics including chief complaint, discharge diagnoses, and ARI-related orders. Operational metrics for ARI cases, including volume, pathogen identification, and illness severity, were compared between the preand post-community transmission timeframes using chi-square tests of independence. RESULTS: Compared to our combined definition of ARI, chief complaint, discharge diagnoses, and isolation orders individually identified less than half of the cases. Respiratory pathogen testing was the top performing individual ARI definition but still only identified 72.2% of cases. From the pre to post periods, we observed significant increases in ED volumes due to ARI and ARI cases without identified pathogen. CONCLUSION: Certain methods for identifying ARI cases in the ED may be inadequate and multiple criteria should be used to optimize capture. In the absence of widely available SARS-CoV-2 testing, operational metrics for ARI-related encounters, especially the proportion of cases involving negative pathogen testing, are useful indicators for active surveillance of potential COVID-19 related ED visits.

Morphomic Malnutrition Score: A Standardized Screening Tool for Severe Malnutrition in Adults.

BACKGROUND: Granular diagnostic criteria for adult malnutrition are lacking. OBJECTIVE: This study uses analytic morphomics to define the Morphomic Malnutrition Score (MMS), a robust screening tool for severe malnutrition. METHODS: The study population (n = 643) consisted of 2 cohorts: 1) 124 emergency department patients diagnosed with severe malnutrition by a registered dietitian (RD) and an available computed tomography (CT) scan within 2 days of RD evaluation, and 2) 519 adult kidney donor candidates to represent a healthy cohort. Body composition markers of muscle area and abdominal adiposity were measured from patient CT scans using analytic morphomic assessment, and then converted to sex- and age-adjusted percentiles using the Reference Analytic Morphomics Population (RAMP). RAMP consists of 6000 patients chosen to be representative of the general population. The combined cohort was then randomly divided into training (n = 453) and validation (n = 190) sets. MMS was derived using logistic regression. The model coefficients were transformed into a score, normalized from 0 to 10 (10 = most severe). RESULTS: Severely malnourished patients had lower amounts of muscle and fat than kidney donors, specifically for dorsal muscle group area at the twelfth thoracic vertebral level (P < 0.001), psoas muscle area at the fourth lumbar vertebral level (P < 0.001), and subcutaneous fat area at the third lumbar vertebral level (P < 0.001)-all parameters in MMS. MMS for severely malnourished patients was higher than kidney donors (7.7 ± 2.2 vs 3.8 ± 2.0, respectively; P-value < 0.001). An MMS > 6.1 was accurate in determining nutrition diagnosis (82.1% sensitivity; 88.3% specificity; 85.2% balanced accuracy). CONCLUSIONS: MMS provides an evidence-based, granular assessment to distinguish severely malnourished adults from a healthy population.

TRPA1 modulates mechanotransduction in cutaneous sensory neurons.

Transient receptor potential ankyrin 1 (TRPA1) is expressed by nociceptive neurons of the dorsal root ganglia (DRGs) and trigeminal ganglia, but its roles in cold and mechanotransduction are controversial. To determine the contribution of TRPA1 to cold and mechanotransduction in cutaneous primary afferent terminals, we used the ex vivo skin-nerve preparation from Trpa1(+/+), Trpa1(+/-), and Trpa1(-/-) adult mouse littermates. Cutaneous fibers from TRPA1-deficient mice showed no deficits in acute cold sensitivity, but they displayed striking deficits in mechanical response properties. C-fiber nociceptors from Trpa1(-/-) mice exhibited action potential firing rates 50% lower than those in wild-type C-fibers across a wide range of force intensities. Adelta-fiber mechanonociceptors also had reduced firing, but only at high intensity forces (>100 mN). Surprisingly, the firing rates of low-threshold Abeta and D-hair mechanoreceptive fibers were also altered. TRPA1 protein and mRNA expression was assessed in DRG neurons and cutaneous innervation by using Trpa1 in situ hybridization, an antibody for TRPA1, and an antibody for placental alkaline phosphatase (PLAP) in mice in which PLAP was substituted for Trpa1. DRG neurons of all sizes expressed Trpa1 mRNA or PLAP immunoreactivity. TRPA1 or PLAP immunolabeling was detected not only on many thin-caliber axons and intraepidermal endings but also on many large-caliber axons as well as lanceolate and Meissner endings. Epidermal and hair follicle keratinocytes also express TRPA1 message and protein. We propose that TRPA1 modulates mechanotransduction via a cell-autonomous mechanism in nociceptor terminals and possibly through a modulatory role in keratinocytes, which may interact with sensory terminals to modify their mechanical firing properties.

The menthol receptor TRPM8 is the principal detector of environmental cold.

Sensory nerve fibres can detect changes in temperature over a remarkably wide range, a process that has been proposed to involve direct activation of thermosensitive excitatory transient receptor potential (TRP) ion channels. One such channel--TRP melastatin 8 (TRPM8) or cold and menthol receptor 1 (CMR1)--is activated by chemical cooling agents (such as menthol) or when ambient temperatures drop below approximately 26 degrees C, suggesting that it mediates the detection of cold thermal stimuli by primary afferent sensory neurons. However, some studies have questioned the contribution of TRPM8 to cold detection or proposed that other excitatory or inhibitory channels are more critical to this sensory modality in vivo. Here we show that cultured sensory neurons and intact sensory nerve fibres from TRPM8-deficient mice exhibit profoundly diminished responses to cold. These animals also show clear behavioural deficits in their ability to discriminate between cold and warm surfaces, or to respond to evaporative cooling. At the same time, TRPM8 mutant mice are not completely insensitive to cold as they avoid contact with surfaces below 10 degrees C, albeit with reduced efficiency. Thus, our findings demonstrate an essential and predominant role for TRPM8 in thermosensation over a wide range of cold temperatures, validating the hypothesis that TRP channels are the principal sensors of thermal stimuli in the peripheral nervous system.