Objective Accurate diagnostic testing to identify SARS–CoV-2 infection is critical. Although highly specific, SARS–CoV-2 reverse transcription polymerase chain reaction (RT-PCR), has shown, in clinical practice, to be affected by a non-insignificant proportion of false negative results. The study sought to explore whether the integration of lung ultrasound (LUS) with clinical evaluation is associated with increased sensitivity for the diagnosis of COVID-19 pneumonia, and therefore may facilitate the identification of false negative SARS-CoV-2 RT-PCR results. Methods This prospective cohort study enrolled consecutive adult patients with symptoms potentially related to SARS-CoV-2 infection admitted to the emergency department (ED) of an Italian academic hospital. Immediately after the initial assessment, a LUS evaluation was performed and the likelihood SARS-CoV-2 infection, based on both clinical and LUS findings (“integrated” assessment), was recorded. RT-PCR SARS-CoV-2 detection was subsequently performed. Results We enrolled 228 patients; 107 patients (46.9%) had SARS-CoV-2 infection. Sensitivity and negative predictive value of the clinical-LUS integrated assessment were higher than first RT-PCR [94.4% (95% CI 88.2-97.9), vs. 80.4% (95% CI 71.6-87.4); 95% (95% CI 89.5-98.2), vs. 85.2% (95% CI 78.3-90.6)]. Among the 142 patients who initially had negative RT-PCR, 21 resulted positive at a subsequent molecular test performed within 72 hours. All these false negative cases were correctly identified by the integrated assessment. Conclusion This study suggests that, in patients presenting to the ED with symptoms commonly associated with SARS-CoV-2 infection, the integration of LUS with clinical evaluation has high sensitivity and specificity for COVID-19 pneumonia and it may help to identify false negative results occurring with RT-PCR.

The first wave (FW) of COVID-19 led to a rapid reduction in total emergency department (ED) visits and hospital admissions for other diseases. Whether this represented a transient “lockdown and fear” phenomenon, or a more persisting trend, is unknown. We divided acute from post-wave changes in ED flows, diagnoses, and hospital admissions, in an Italian city experiencing a FW peak followed by nadir. This multicenter, retrospective, cross-sectional study involved five general EDs of a large Italian city (January–August 2020). Percent changes were calculated versus 2019, using four 14-day periods (FW peak, early/mid/late post-wave). ED visits were 147,446 in 2020, versus 214,868 in 2019. During the FW peak, visits were reduced by 66.4% (P < 0.001). The drop was maximum during daytime (69.8%) and for pediatric patients (89.4%). Critical triage codes were unchanged. Reductions were found for all non-COVID-19 diagnoses. Non-COVID-19 hospital admissions were reduced by 39.5% (P < 0.001), involving all conditions except hematologic, metabolic/endocrine, respiratory diseases, and traumas. In the early, mid, and late post-wave periods, visits were reduced by 25.4%, 25.3% and 23.5% (all P < 0.001) respectively. In the late period, reduction was greater for female (27.9%) and pediatric patients (44.6%). Most critical triage codes were unchanged. Oncological, metabolic/endocrine, and hematological diagnoses were unchanged, while other diagnoses had persistent reductions. Non-COVID-19 hospital admissions were reduced by 12.8% (P = 0.001), 6.3% (P = 0.1) and 12.2% (P = 0.001), respectively. Reductions in ED flows, led by non-critical codes, persisted throughout the summer nadir of COVID-19. Hospital admissions for non-COVID-19 diseases had transient changes.

Objectives: In patients at low clinical probability of acute aortic syndromes (AASs), decision on advanced aortic imaging is cumbersome. Integration of the aortic dissection detection risk score (ADD-RS) with D-dimer (DD) provides a potential pipeline for standardized diagnostic rule-out. We systematically reviewed and summarized supporting data. Methods: Cross-sectional studies assessing integration of ADD-RS with DD for diagnosis of AASs were identified on MEDLINE, EMBASE and Web Of Science databases. Two reviewers independently screened articles, assessed quality, and extracted data. The quality of design and reporting was evaluated with the QUADAS-2 and STARD tools. Individual patient data were obtained, to allow analysis of both conventional (500 ng/mL) and age-adjusted (DD age-adj) DD cutoffs. Data were summarized for four diagnostic strategies combining ADD-RS = 0 or ≤ 1, with DD < 500 ng/mL or < DD age-adj. The statistical heterogeneity of the diagnostic variables was estimated with Higgins' I 2. Pooled values were calculated for variables showing nonsignificant heterogeneity. Results: After screening of 680 studies, four articles (including a total of 3,804 patients) met inclusion criteria. One prospective study provided a low risk of bias/applicability concerns, while methodologic limitations were found in the other three retrospective studies. Statistical heterogeneity was negligible for sensitivity and negative likelihood ratio (LR) values and significant for specificity and positive LR values of all diagnostic strategies. Pooled sensitivity was 99.

Background When acute aortic syndromes (AASs) are suspected, pretest clinical probability assessment and d ‐dimer (DD) testing are diagnostic options allowing standardized care. Guidelines suggest use of a 12‐item/3‐category score (aortic dissection detection) and a DD cutoff of 500 ng/mL. However, a simplified assessment tool and a more specific DD cutoff could be advantageous. Methods and Results In a prospective derivation cohort (n=1848), 6 items identified by logistic regression (thoracic aortic aneurysm, severe pain, sudden pain, pulse deficit, neurologic deficit, hypotension), composed a simplified score (AORTAs) assigning 2 points to hypotension and 1 to the other items. AORTAs≤1 and ≥2 defined low and high clinical probability, respectively. Age‐adjusted DD was calculated as years/age × 10 ng/mL (minimum 500). The AORTAs score and AORTAs≤1/age‐adjusted DD rule were validated in 2 patient cohorts: a high‐prevalence retrospective cohort (n=1035; 22% AASs) and a low‐prevalence prospective cohort (n=447; 11% AASs) subjected to 30‐day follow‐up. The AUC of the AORTAs score was 0.729 versus 0.697 of the aortic dissection detection score ( P =0.005). AORTAs score assessment reclassified 16.6% to 25.1% of patients, with significant net reclassification improvement of 10.3% to 32.7% for AASs and −8.6 to −17% for alternative diagnoses. In both cohorts, AORTAs≥2 had superior sensitivity and slightly lower specificity than aortic dissection detection ≥2. In the prospective validation cohort, AORTAs≤1/age‐adjusted DD had a sensitivity of 100%, a specificity of 48.6%, and an efficiency of 43.3%. Conclusions AORTAs is a simplified score with increased sensitivity, improved AAS classification, and minor trade‐off in specificity, amenable to integration with age‐adjusted DD for diagnostic rule‐out.