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.

Adherence to PIOPED II investigators' recommendations for computed tomography pulmonary angiography.

BACKGROUND: Computed tomography (CT) pulmonary angiography use has increased dramatically, raising concerns for patient safety. Adherence to recommendations and guidelines may protect patients. We measured adherence to the recommendations of Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED II) investigators for evaluation of suspected pulmonary embolism and the rate of potential false-positive pulmonary embolism diagnoses when recommendations of PIOPED II investigators were not followed. METHODS: We used a structured record review to identify 3500 consecutive CT pulmonary angiograms performed to investigate suspected pulmonary embolism in 2 urban emergency departments, calculating the revised Geneva score (RGS) to classify patients as "pulmonary embolism unlikely" (RGS≤10) or "pulmonary embolism likely" (RGS>10). CT pulmonary angiograms were concordant with PIOPED II investigator recommendations if pulmonary embolism was likely or pulmonary embolism was unlikely and a highly sensitive D-dimer test result was positive. We independently reviewed 482 CT pulmonary angiograms to measure the rate of potential false-positive pulmonary embolism diagnoses. RESULTS: A total of 1592 of 3500 CT pulmonary angiograms (45.5%) followed the recommendations of PIOPED II investigators. The remaining 1908 CT pulmonary angiograms were performed on patients with an RGS≤10 without a D-dimer test (n=1588) or after a negative D-dimer test result (n=320). The overall rate of pulmonary embolism was 9.7%. Potential false-positive diagnoses of pulmonary embolism occurred in 2 of 3 patients with an RGS≤10 and a negative D-dimer test result. CONCLUSIONS: Nonadherence to recommendations for CT pulmonary angiography is common and exposes patients to increased risks, including potential false-positive diagnoses of pulmonary embolism.

Radiation and chest CT scan examinations: what do we know?

In the past 3 decades, the total number of CT scans performed has grown exponentially. In 2007, > 70 million CT scans were performed in the United States. CT scan studies of the chest comprise a large portion of the CT scans performed today because the technology has transformed the management of common chest diseases, including pulmonary embolism and coronary artery disease. As the number of studies performed yearly increases, a growing fraction of the population is exposed to low-dose ionizing radiation from CT scan. Data extrapolated from atomic bomb survivors and other populations exposed to low-dose ionizing radiation suggest that CT scan-associated radiation may increase an individual's lifetime risk of developing cancer. This finding, however, is not incontrovertible. Because this topic has recently attracted the attention of both the scientific community and the general public, it has become increasingly important for physicians to understand the cancer risk associated with CT scan and be capable of engaging in productive dialogue with patients. This article reviews the current literature on the public health debate surrounding CT scan and cancer risk, quantifies radiation doses associated with specific studies, and describes efforts to reduce population-wide CT scan-associated radiation exposure. CT scan examinations of the chest, including CT scan pulmonary and coronary angiography, high-resolution CT scan, low-dose lung cancer screening, and triple rule-out CT scan, are specifically considered.