1716 Why do emergency department clinicians miss acute aortic syndrome? A case series and descriptive analysis
Abstract
Aims, Objectives and Background Acute aortic syndrome (AAS) is a rare, life-threatening emergency affecting approximately 4000 people per year in the UK, has an ED misdiagnosis rate as high as 38% and around one quarter of cases are not diagnosed until 24 hours after presenting to the ED. It presents in many atypical ways, posing a significant diagnostic conundrum for the emergency department (ED) clinician and risk of litigation. We sought to understand the reasons why AAS was missed in ED by conducting a case series review of patients with misidentified AAS presenting to three UK EDs over a 10-year period. Method and Design We identified missed diagnoses of AAS using searches of ED morbidity and mortality records, post-mortem reports, complaints, Electronic Patient Records (EPRs) and Radiology records from three UK EDs. Results and Conclusion Between 1.1.2011–31.12.2020, 43 cases were identified across three EDs (1.4 cases/yr/dept). The most common diagnosis was Type A aortic dissection (22; 51%). The most common incorrect presumed diagnoses made were acute coronary syndrome (ACS, 12; 28%), pulmonary embolism (PE, 5; 12%) and ‘non-specific chest pain’ (5; 12%). In 31 of the 43 cases (72%) there was no evidence from the notes of consideration of AAS in the differential diagnosis. In 10 of the 43 cases (23%), AAS was clearly considered, but the clinician appears to have been falsely reassured by clinical findings, normal chest x-ray, or atypical or resolved symptoms. Only…
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.