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.

D-dimer Thresholds to Exclude Pulmonary Embolism among COVID-19 Patients in the Emergency Department: Derivation with Independent Validation.

OBJECTIVE: To derive and validate a D-dimer cutoff for ruling out pulmonary embolism (PE) in COVID-19 patients presenting to the emergency department (ED). METHODS: A retrospective cohort study was performed in an integrated healthcare system including 22 adult ED's between March 1, 2020, and January 31, 2021. Results were validated among patients enrolled in the RECOVER Registry, representing data from 154 ED's from 26 US states. Consecutive ED patients with laboratory confirmed COVID-19, a D-dimer performed within 48 h of ED arrival, and with objectively confirmed PE were compared to those without PE. After identifying a D-dimer threshold at which the 95% confidence lower bound of the negative predictive value for PE was higher than 98% in the derivation cohort, it was validated using RECOVER registry data. RESULTS: Among 3978 patients with a D-dimer result, 3583 with confirmed COVID-19 infection were included in the derivation cohort. Overall, PE incidence was 4.1% and a D-dimer cutoff of <2 µ/mL (2000 ng/mL) was associated with a NPV of 98.5% (95% CI = 98.0%-98.9%). In the validation cohort of 13,091 patients with a D-dimer, 7748 had confirmed COVID-19 infection, and the PE incidence was 1.14%. A D-dimer cutoff of <2 µ/mL was associated with a NPV of 99.5% (95% CI = 99.3%-99.7%). CONCLUSION: A D-dimer cutoff of <2 µ/ml was associated with a high negative predictive value for PE among patients with COVID-19. However, the resultant sensitivity for PE result at that threshold without pre-test probability assessment would be considered clinically unsafe.

Clinical use of an enterprise data warehouse.

The enormous amount of data being collected by electronic medical records (EMR) has found additional value when integrated and stored in data warehouses. The enterprise data warehouse (EDW) allows all data from an organization with numerous inpatient and outpatient facilities to be integrated and analyzed. We have found the EDW at Intermountain Healthcare to not only be an essential tool for management and strategic decision making, but also for patient specific clinical decision support. This paper presents the structure and two case studies of a framework that has provided us the ability to create a number of decision support applications that are dependent on the integration of previous enterprise-wide data in addition to a patient's current information in the EMR.