Identifying clinical and biochemical phenotypes in acute respiratory distress syndrome secondary to coronavirus disease-2019.

BACKGROUND: Acute respiratory distress syndrome (ARDS) secondary to coronavirus disease-2019 (COVID-19) is characterized by substantial heterogeneity in clinical, biochemical, and physiological characteristics. However, the pathophysiology of severe COVID-19 infection is poorly understood. Previous studies established clinical and biological phenotypes among classical ARDS cohorts, with important therapeutic implications. The phenotypic profile of COVID-19 associated ARDS remains unknown. METHODS: We used latent class modeling via a multivariate mixture model to identify phenotypes from clinical and biochemical data collected from 263 patients admitted to Massachusetts General Hospital intensive care unit with COVID-19-associated ARDS between March 13 and August 2, 2020. FINDINGS: We identified two distinct phenotypes of COVID-19-associated ARDS, with substantial differences in biochemical profiles despite minimal differences in respiratory dynamics. The minority phenotype (class 2, n = 70, 26·6%) demonstrated increased markers of coagulopathy, with mild relative hyper-inflammation and dramatically increased markers of end-organ dysfunction (e.g., creatinine, troponin). The odds of 28-day mortality among the class 2 phenotype was more than double that of the class 1 phenotype (40·0% vs.· 23·3%, OR = 2·2, 95% CI [1·2, 3·9]). INTERPRETATION: We identified distinct phenotypic profiles in COVID-19 associated ARDS, with little variation according to respiratory physiology but with important variation according to systemic and extra-pulmonary markers. Phenotypic identity was highly associated with short-term mortality. The class 2 phenotype exhibited prominent signatures of coagulopathy, suggesting that vascular dysfunction may play an important role in the clinical progression of severe COVID-19-related disease.

Paramedic Perspectives on Barriers to Prehospital Acute Stroke Recognition.

BACKGROUND: Emergency Medical Service (EMS) providers are tasked with rapid evaluation, stabilization, recognition, and transport of acute stroke patients. Although prehospital stroke scales were developed to assist with stroke recognition, unrecognized challenges exist in the prehospital setting that hinder accurate assessment of stroke. The goal of this qualitative study was to systematically understand the challenges and barriers faced by paramedics in recognizing stroke presentations in the field. METHODS: Paramedics from 12 EMS agencies serving a mix of rural, suburban, and urban communities in the State of California participated in five focus group discussions. Group size ranged from 3-8, with a total of 28 participants. Demographics of the participants were collected and focus group recordings were transcribed verbatim. Transcripts were subjected to deductive and inductive coding, which identified recurrent and divergent themes. RESULTS: Strong consensus existed around constraints to prehospital stroke recognition; participants cited the diversity of stroke presentations, linguistic diversity, and exam confounded by alcohol and or drug use as barriers to initial evaluation. Also, lack of educational feedback from hospital staff and physicians and continuing medical education on stroke were reported as major deterrents to enhancing their diagnostic acumen. Across groups, participants reported attempting to foster relationships with hospital personnel to augment their educational needs, but this was easier for rural than urban providers. CONCLUSIONS: While challenges to stroke recognition in the field were slightly different for rural and urban EMS, participants concurred that timely, systematic feedback on individual patients and case-based training would strengthen early stroke recognition skills.

Pilot study of ultrasound-guided corticosteroid hip injections by emergency physicians.

INTRODUCTION: Our objective was to assess the efficacy of ultrasound-guided hip injections performed by emergency physicians (EPs) for the treatment of chronic hip pain in an outpatient clinic setting. METHODS: Patients were identified on a referral basis from the orthopedic chronic pain clinic. The patient population was either identified as having osteoarthritis of the hip, osteonecrosis of varying etiologies, post-traumatic osteoarthritis of the hip, or other non-infectious causes of chronic hip pain. Patients had an ultrasound-guided hip injection of 4 ml of 0.5% bupivacaine and 1 ml of triamcinolone acetate (40 mg/1 ml). Emergency medicine resident physicians under the supervision of an attending EP performed all injections. Pain scores were collected using a Likert pain scale from patients prior to the procedure, and 10 minutes post procedure and at short-term follow-up of one week and one month. The primary outcome was patient-reported pain score on a Likert pain scale at one week. RESULTS: We performed a total of 47 ultrasound-guided intra-articular hip injections on 44 subjects who met inclusion criteria. Three subjects received bilateral injections. Follow-up data were available for 42/47 (89.4%) hip injections at one week and 40/47 (85.1%) at one month. The greatest improvement was at 10 minutes after injection with a mean decrease in Likert pain score from pre-injection baseline of 5.57 (95% CI, 4.76-6.39). For the primary outcome at one week, we found a mean decrease in Likert pain score from pre-injection baseline of 3.85 (95% CI, 2.94-4.75). At one month we found a mean decrease in Likert pain score of 1.8 (95% CI, 1.12-2.53). There were no significant adverse outcomes reported. CONCLUSION: Under the supervision of an attending EP, junior emergency medicine resident physicians can safely and effectively inject hips for chronic pain relief in an outpatient clinical setting using ultrasound guidance.

Which emergency medical dispatch codes predict high prehospital nontransport rates in an urban community?

BACKGROUND: The Medical Priority Dispatch System (MPDS) is a commonly used computer-based emergency medical dispatch (EMD) system that is widely used to prioritize 9-1-1 calls and optimize resource allocation. There are five major priority classes used to dispatch 9-1-1 calls in the San Francisco System; Alpha codes are the lowest priority (lowest expected acuity) and Echo are the highest priority. OBJECTIVE: We sought to determine which MPDS dispatch codes are associated with high prehospital nontransport rates (NTRs). METHODS: All unique MPDS call categories from 2009 in a highly urbanized, two-tier advanced life support (ALS) system were sorted according to highest NTRs. There are many reasons for nontransport, such as "gone on arrival," and "patient denied transport." Those categories with greater than 100 annual calls were further evaluated. MPDS groups that included multiple categories with NTRs exceeding 25% were then identified and each category was analyzed. Results. EMS responded to a total of 81,437 calls in 2009, of which 18,851 were not transported by EMS. The majority of the NTRs were found among "cardiac/ respiratory arrest/death," "assault/sexual assaults," "unknown problem/man down," "traffic/transportation accidents," and "unconscious/fainting." "Cardiac or respiratory arrest/death -obvious death" (9B1) had the highest overall nontransport rate, 99.25% (1/134), most likely due to declaration of death. "Unknown problem -man down -medical alert notification" had the second highest NTR, 67.22% (138/421). However, Echo priority codes had the highest overall nontransport rates (45.45%) and Charlie had the lowest (13.84%). CONCLUSIONS: The nontransport rates of individual MPDS categories vary considerably and should be considered in any system design. We identified 52 unique call categories to have a 25% or greater NTR, 18 of which exceeded 40%. The majority of NTRs occurred among the "cardiac/respiratory arrest/death," "assault/sexual assaults," "unknown problem/man down," "traffic/transportation accidents," and "unconscious/fainting" categories. The higher the priority code within each subset (AB vs. CDE), the less likely the patient was to be transported. Charlie priority codes had a lower NTR than Delta, and Delta was lower than Echo. Charlie codes were therefore the strongest predictors of hospital transport, while Echo codes (highest priority) were those with the highest nontransport rates and were the worst predictors of hospital transport in the emergent subset.