Prospective Validation of a Prediction Model for the Diagnosis of Acute Pancreatitis.

Importance: While most patients with acute pancreatitis (AP) fulfill diagnostic criteria with characteristic abdominal pain and serum lipase levels of at least 3 times the upper limit of normal (reference range) at presentation, early imaging is often used for confirmation. A prior prediction model and corresponding point-based score were developed using nonimaging parameters to diagnose AP in patients presenting to the emergency department (ED). Objective: To evaluate the performance of the prediction model to diagnose AP in a prospective patient cohort. Design, Setting, and Participants: This prospective diagnostic study included consecutive adult patients presenting to the ED between January 1, 2020, and March 9, 2021, at 2 large academic medical centers in the northeastern US with serum lipase levels at least 3 times the upper limit of normal. Patients transferred from outside institutions or with malignant disease and established intra-abdominal metastases, acute trauma, or altered mentation were excluded. Data were analyzed from October 15 to October 23, 2023. Exposures: Participants were assigned scores for initial serum lipase level, number of prior AP episodes, prior cholelithiasis, abdominal surgery within 2 months, presence of epigastric pain, pain of worsening severity, duration from pain onset to presentation, and pain level at ED presentation. Main Outcome and Measures: A final diagnosis of AP, established by expert review of hospitalization records. Results: Prospective scores in 349 participants (mean [SD] age, 53.0 [18.8] years; 184 women [52.7%]; 66 Black [18.9%]; 199 White [57.0%]) demonstrated an area under the receiver operating characteristics curve of 0.91. A score of at least 6 points achieved highest accuracy (F score, 82.0), corresponding to a sensitivity of 81.5%, specificity of 85.9%, positive predictive value of 82.6%, and negative predictive value of 85.1% for AP diagnosis. Early computed tomography or magnetic resonance imaging was performed more often in participants predicted to have AP (116 of 155 [74.8%] with a score ≥6 vs 111 of 194 [57.2%] with a score <6; P < .001). Early imaging revealed an alternative diagnosis in 8 of 116 participants (6.9%) with scores of at least 6 points, 1 of 93 (1.1%) with scores of at least 7 points, and 1 of 73 (1.4%) with scores of at least 8 points. Conclusions and Relevance: In this multicenter diagnostic study, the prediction model demonstrated excellent AP diagnostic accuracy. Its application may be used to avoid unnecessary confirmatory imaging.

Humanitarian Training With Virtual Simulation During a Pandemic.

There is an ongoing and established need for humanitarian training and professionalization. The coronavirus disease 2019 (COVID-19) pandemic disrupted training programs designed to accomplish this goal, including the Humanitarian Response Intensive Course, which includes a 3-d immersive simulation to prepare humanitarian workers for future field work. To provide program continuity, the 3-d simulation was quickly adapted to a virtual format using a combination of video conferencing, short messaging service, and cloud-based file storage software. Participants were geographically dispersed and participated virtually. Learning objectives were preserved, while some components not amenable to a virtual format were removed.A virtual humanitarian training simulation is a feasible, acceptable, and affordable alternative to an in-person simulation. Participants were engaged and experienced minimal technological disruptions. The majority of students believed the format met or exceeded expectations. However, feedback also emphasized the importance of providing sufficient time for team collaboration and deliverable preparation in the simulation schedule. The virtual format was more affordable than the traditional in-person simulation, and diverse expert faculty who could not have attended in-person were able to participate. This format could be used to overcome other barriers to in-person simulation training, including geographic, financial, time, or security.

Emergency care in 59 low- and middle-income countries: a systematic review.

OBJECTIVE: To conduct a systematic review of emergency care in low- and middle-income countries (LMICs). METHODS: We searched PubMed, CINAHL and World Health Organization (WHO) databases for reports describing facility-based emergency care and obtained unpublished data from a network of clinicians and researchers. We screened articles for inclusion based on their titles and abstracts in English or French. We extracted data on patient outcomes and demographics as well as facility and provider characteristics. Analyses were restricted to reports published from 1990 onwards. FINDINGS: We identified 195 reports concerning 192 facilities in 59 countries. Most were academically-affiliated hospitals in urban areas. The median mortality within emergency departments was 1.8% (interquartile range, IQR: 0.2-5.1%). Mortality was relatively high in paediatric facilities (median: 4.8%; IQR: 2.3-8.4%) and in sub-Saharan Africa (median: 3.4%; IQR: 0.5-6.3%). The median number of patients was 30 000 per year (IQR: 10 296-60 000), most of whom were young (median age: 35 years; IQR: 6.9-41.0) and male (median: 55.7%; IQR: 50.0-59.2%). Most facilities were staffed either by physicians-in-training or by physicians whose level of training was unspecified. Very few of these providers had specialist training in emergency care. CONCLUSION: Available data on emergency care in LMICs indicate high patient loads and mortality, particularly in sub-Saharan Africa, where a substantial proportion of all deaths may occur in emergency departments. The combination of high volume and the urgency of treatment make emergency care an important area of focus for interventions aimed at reducing mortality in these settings.