Synoptic reporting accuracy for computed tomography pulmonary arteriography among patients suspected of pulmonary embolism.

Background: Structured reporting is an efficient and replicable method of presenting diagnostic results that eliminates variability inherent in narrative descriptive reporting and may improve clinical decisions. Synoptic element reporting can generate discrete coded data that then may inform clinical decision support and trigger downstream actions in computerized electronic health records. Objective: Limited evidence exists for use of synoptic reporting for computed tomography pulmonary arteriography (CTPA) among patients suspected of pulmonary embolism. We reported the accuracy of synoptic reporting for the outcome of pulmonary embolism among patients who presented to an integrated health care system with CTPA performed for suspected pulmonary embolism. Methods: Structured radiology reports with embedded synoptic elements were implemented for all CTPA examinations on March 1, 2018. Four hundred CTPA reports between January 4, 2019 and July 30, 2020 (200 reports each for which synoptic reporting recorded the presence or absence of pulmonary embolism [PE]) were selected at random. One non-diagnostic study was excluded from analysis. We then assessed the accuracy of synoptic reporting compared with the gold standard of manual chart review. Results: Synoptic reporting and manual review agreed in 99.2% of patients undergoing CTPA for suspected PE, agreed on the presence of PE in 196 of 199 (98.5%) cases, the absence of PE in 200 of 200 (100%) cases with a sensitivity of 87.6% (76.1-96.1) a specificity of 99.9% (99.7%-100%), a positive predictive value of 99.5% (98.1-100), and a negative predictive value of 98% (95.7%-99.5%). Conclusion: The overall rate of agreement was 99.2%, but we observed an unacceptable false-negative rate for clinical reliance on synoptic element reporting in isolation from dictated reports.

CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

PURPOSE: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs. MATERIALS AND METHODS: In this retrospective cohort study, a dataset of 7434 prior chest radiographic studies from 6551 ED patients was used to develop and validate a deep learning model to identify radiographic pneumonia, pleural effusions, and evidence of multilobar pneumonia. Model performance was evaluated against 3 radiologists' adjudicated interpretation and compared with performance of the natural language processing of radiology reports used by ePNa. RESULTS: The deep learning model achieved an area under the receiver operating characteristic curve of 0.833 (95% confidence interval [CI]: 0.795, 0.868) for detecting radiographic pneumonia, 0.939 (95% CI: 0.911, 0.962) for detecting pleural effusions and 0.847 (95% CI: 0.800, 0.890) for identifying multilobar pneumonia. On all 3 tasks, the model achieved higher agreement with the adjudicated radiologist interpretation compared with ePNa. CONCLUSIONS: A deep learning model demonstrated higher agreement with radiologists than the ePNa CDSS in detecting radiographic pneumonia and related findings. Incorporating deep learning models into pneumonia CDSS could enhance diagnostic performance and improve pneumonia management.

Isolated acromioclavicular joint pathology in the symptomatic shoulder on magnetic resonance imaging: a pictorial essay.

The acromioclavicular (AC) joint is a synovial joint that is predisposed to painful syndromes because of mechanical stress or developmental variation. It is often overlooked in the evaluation of patients with shoulder pain, however. Isolated AC joint pathology was studied on magnetic resonance imaging scans of patients with symptoms suggesting rotator cuff pathology. The conditions identified included osteoarthritis, distal clavicle osteolysis, and os acromiale syndrome.