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.

Predictive models in emergency medicine and their missing data strategies: a systematic review.

In the field of emergency medicine (EM), the use of decision support tools based on artificial intelligence has increased markedly in recent years. In some cases, data are omitted deliberately and thus constitute "data not purposely collected" (DNPC). This accepted information bias can be managed in various ways: dropping patients with missing data, imputing with the mean, or using automatic techniques (e.g., machine learning) to handle or impute the data. Here, we systematically reviewed the methods used to handle missing data in EM research. A systematic review was performed after searching PubMed with the query "(emergency medicine OR emergency service) AND (artificial intelligence OR machine learning)". Seventy-two studies were included in the review. The trained models variously predicted diagnosis in 25 (35%) publications, mortality in 21 (29%) publications, and probability of admission in 21 (29%) publications. Eight publications (11%) predicted two outcomes. Only 15 (21%) publications described their missing data. DNPC constitute the "missing data" in EM machine learning studies. Although DNPC have been described more rigorously since 2020, the descriptions in the literature are not exhaustive, systematic or homogeneous. Imputation appears to be the best strategy but requires more time and computational resources. To increase the quality and the comparability of studies, we recommend inclusion of the TRIPOD checklist in each new publication, summarizing the machine learning process in an explicit methodological diagram, and always publishing the area under the receiver operating characteristics curve-even when it is not the primary outcome.

Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy University Hospital (APUH) developed an Artificial Intelligence (AI) project called "Prediction of the Patient Pathway in the Emergency Department" (3P-U) to predict patient outcomes. MATERIALS: Using the 3P-U model, we performed a prospective, single-center study of patients attending APUH's ED in 2020 and 2021. The objective was to determine the minimum and maximum numbers of beds required in real-time, according to the 3P-U model. Results A total of 105,457 patients were included. The area under the receiver operating characteristic curve (AUROC) for the 3P-U was 0.82 for all of the patients and 0.90 for the unambiguous cases. Specifically, 38,353 (36.4%) patients were flagged as "likely to be discharged", 18,815 (17.8%) were flagged as "likely to be admitted", and 48,297 (45.8%) patients could not be flagged. Based on the predicted minimum number of beds (for unambiguous cases only) and the maximum number of beds (all patients), the hospital management coordinated the conversion of wards into dedicated COVID-19 units. DISCUSSION AND CONCLUSIONS: The 3P-U model's AUROC is in the middle of range reported in the literature for similar classifiers. By considering the range of required bed numbers, the waste of resources (e.g., time and beds) could be reduced. The study concludes that the application of AI could help considerably improve the management of hospital resources during global pandemics, such as COVID-19.