Temporal Trends in the Use of Computed Tomographic Pulmonary Angiography for Suspected Pulmonary Embolism in the Emergency Department : A Retrospective Analysis.

BACKGROUND: Recently, validated clinical decision rules have been developed that avoid unnecessary use of computed tomographic pulmonary angiography (CTPA) in patients with suspected pulmonary embolism (PE) in the emergency department (ED). OBJECTIVE: To measure any resulting change in CTPA use for suspected PE. DESIGN: Retrospective analysis. SETTING: 26 European EDs in 6 countries. PATIENTS: Patients with CTPA performed for suspected PE in the ED during the first 7 days of each odd month between January 2015 and December 2019. MEASUREMENTS: The primary end points were the CTPAs done for suspected PE in the ED and the number of PEs diagnosed in the ED each year adjusted to an annual census of 100 000 ED visits. Temporal trends were estimated using generalized linear mixed regression models. RESULTS: 8970 CTPAs were included (median age, 63 years; 56% female). Statistically significant temporal trends for more frequent use of CTPA (836 per 100 000 ED visits in 2015 vs. 1112 in 2019; P < 0.001), more diagnosed PEs (138 per 100 000 in 2015 vs. 164 in 2019; P = 0.028), a higher proportion of low-risk PEs (annual percent change [APC], 13.8% [95% CI, 2.6% to 30.1%]) with more ambulatory management (APC, 19.3% [CI, 4.1% to 45.1%]), and a lower proportion of intensive care unit admissions (APC, -8.9% [CI, -17.1% to -0.3%]) were observed. LIMITATION: Data were limited to 7 days every 2 months. CONCLUSION: Despite the recent validation of clinical decision rules to limit the use of CTPA, an increase in the CTPA rate along with more diagnosed PEs and especially low-risk PEs were instead observed. PRIMARY FUNDING SOURCE: None specific for this study.

Point-of-care ultrasonography for risk stratification of non-critical suspected COVID-19 patients on admission (POCUSCO): A prospective binational study.

BACKGROUND: Lung point-of-care ultrasonography (L-POCUS) is highly effective in detecting pulmonary peripheral patterns and may allow early identification of patients who are likely to develop an acute respiratory distress syndrome (ARDS). We hypothesized that L-POCUS performed within the first 48 hours of non-critical patients with suspected COVID-19 would identify those with a high-risk of worsening. METHODS: POCUSCO was a prospective, multicenter study. Non-critical adult patients who presented to the emergency department (ED) for suspected or confirmed COVID-19 were included and had L-POCUS performed within 48 hours following ED presentation. The lung damage severity was assessed using a previously developed score reflecting both the extension and the intensity of lung damage. The primary outcome was the rate of patients requiring intubation or who died within 14 days following inclusion. RESULTS: Among 296 patients, 8 (2.7%) met the primary outcome. The area under the curve (AUC) of L-POCUS was 0.80 [95%CI:0.60-0.94]. The score values which achieved a sensibility >95% in defining low-risk patients and a specificity >95% in defining high-risk patients were <1 and ≥16, respectively. The rate of patients with an unfavorable outcome was 0/95 (0%[95%CI:0-3.9]) for low-risk patients (score = 0), 4/184 (2.17%[95%CI:0.8-5.5]) for intermediate-risk patients (score 1-15) and 4/17 (23.5%[95%CI:11.4-42.4]) for high-risk patients (score ≥16). In confirmed COVID-19 patients (n = 58), the AUC of L-POCUS was 0.97 [95%CI:0.92-1.00]. CONCLUSION: L-POCUS performed within the first 48 hours following ED presentation allows risk-stratification of patients with non-severe COVID-19.

Building a Semantic Health Data Warehouse in the Context of Clinical Trials: Development and Usability Study.

BACKGROUND: The huge amount of clinical, administrative, and demographic data recorded and maintained by hospitals can be consistently aggregated into health data warehouses with a uniform data model. In 2017, Rouen University Hospital (RUH) initiated the design of a semantic health data warehouse enabling both semantic description and retrieval of health information. OBJECTIVE: This study aimed to present a proof of concept of this semantic health data warehouse, based on the data of 250,000 patients from RUH, and to assess its ability to assist health professionals in prescreening eligible patients in a clinical trials context. METHODS: The semantic health data warehouse relies on 3 distinct semantic layers: (1) a terminology and ontology portal, (2) a semantic annotator, and (3) a semantic search engine and NoSQL (not only structured query language) layer to enhance data access performances. The system adopts an entity-centered vision that provides generic search capabilities able to express data requirements in terms of the whole set of interconnected conceptual entities that compose health information. RESULTS: We assessed the ability of the system to assist the search for 95 inclusion and exclusion criteria originating from 5 randomly chosen clinical trials from RUH. The system succeeded in fully automating 39% (29/74) of the criteria and was efficiently used as a prescreening tool for 73% (54/74) of them. Furthermore, the targeted sources of information and the search engine-related or data-related limitations that could explain the results for each criterion were also observed. CONCLUSIONS: The entity-centered vision contrasts with the usual patient-centered vision adopted by existing systems. It enables more genericity in the information retrieval process. It also allows to fully exploit the semantic description of health information. Despite their semantic annotation, searching within clinical narratives remained the major challenge of the system. A finer annotation of the clinical texts and the addition of specific functionalities would significantly improve the results. The semantic aspect of the system combined with its generic entity-centered vision enables the processing of a large range of clinical questions. However, an important part of health information remains in clinical narratives, and we are currently investigating novel approaches (deep learning) to enhance the semantic annotation of those unstructured data.