The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model’s performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis.

Background and importanceCurrent guidelines for patients presenting to the emergency department with chest pain without ST-segment elevation myocardial infarction (non-STEMI) on electrocardiogram are based on troponin measurement. The HEART score is reportedly a reliable work-up strategy that combines clinical evaluation with troponin value. A clinical rule that could select very low-risk patients without the need for a blood test (HEAR score, being the HEART score without the troponin item) would be of great interest.Objectives To prospectively assess the safety of a HEAR score <2 to rule-out non-STEMI without troponin measurement. Secondary objective was to assess the safety of a sequential strategy that combines HEAR score and HEART (defined as two-step HEART strategy). Design, settings and participantsProspective observational study in six emergency departments. Patients with nontraumatic chest pain and no alternative diagnosis were included and followed up for 45 day. Patients were considered at low-risk if the HEAR score was <2 or, for the two-step HEART strategy, if the HEART score was <4. Outcomes measure and analysisThe primary endpoint was the 45-day rate of major adverse cardiac events (MACE) in patients with a HEAR score <2. A HEAR score based strategy was consider safe if the rate of the primary endpoint was below 1%, with an upper margin of the 95% confidence interval (CI) below 3%. ResultsAmong 1452 patients included, 1402 were analyzed and 97 (7%) had a MACE during the follow-up period. The HEAR score was <2 in 279 (20%) patients and one presented a MACE [0.4% (95% CI: 0.01-1.98)]. The two-step HEART strategy classified low-risk an additional 476 patients (34%) and one of these 476 patients had a MACE [0.3% (95% CI: 0.03-0.95)]. The two-step HEART strategy would have theoretically avoided 360 troponin measurements (19%). ConclusionsIn our prospective multicenter study, a HEAR based work-up strategy was safe, with a very low risk of MACE at 45 day. We also report that a twostep HEART-based strategy may safely allow significant reduction of troponin measurements in patients presenting to the emergency department with chest pain. European

Acute traumatic coagulopathy (ATC) is an acute and endogenous mechanism triggered by the association of trauma and hemorrhage. Several animal models have been developed, but some major biases have not yet been identified. Our aim was to develop a robust and clinically relevant murine model to study this condition. Anesthetized adult Sprague Dawley rats were randomized into 4 groups: C, control; T, trauma; H, hemorrhage; TH, trauma and hemorrhage (n = 7 each). Trauma consisted of laparotomy associated with four-limb and splenic fractures. Clinical variables, ionograms, arterial and hemostasis blood tests were compared at 0 and 90 min. ATC and un-compensated shock were observed in group TH. In this group, the rise in prothrombin time and activated partial thromboplastin was 29 and 40%, respectively. Shock markers, compensation mechanisms and coagulation pathways were all consistent with human pathophysiology. The absence of confounding factors, such as trauma-related bleeding or dilution due to trans-capillary refill was verified. This ethic, cost effective and bias-controlled model reproduced the specific and endogenous mechanism of ATC and will allow to identify potential targets for therapeutics in case of trauma-related hemorrhage.