The presentations/physician ratio predicts door-to-physician time but not global length of stay in the emergency department: an Italian multicenter study during the SARS-CoV-2 pandemic.

To investigate the effects of the dramatic reduction in presentations to Italian Emergency Departments (EDs) on the main indicators of ED performance during the SARS-CoV-2 pandemic. From February to June 2020 we retrospectively measured the number of daily presentations normalized for the number of emergency physicians on duty (presentations/physician ratio), door-to-physician and door-to-final disposition (length-of-stay) times of seven EDs in the central area of Tuscany. Using the multivariate regression analysis we investigated the relationship between the aforesaid variables and patient-level (triage codes, age, admissions) or hospital-level factors (number of physician on duty, working surface area, academic vs. community hospital). We analyzed data from 105,271 patients. Over ten consecutive 14-day periods, the number of presentations dropped from 18,239 to 6132 (- 67%) and the proportion of patients visited in less than 60 min rose from 56 to 86%. The proportion of patients with a length-of-stay under 4 h decreased from 59 to 52%. The presentations/physician ratio was inversely related to the proportion of patients with a door-to-physician time under 60 min (slope - 2.91, 95% CI - 4.23 to - 1.59, R2 = 0.39). The proportion of patients with high-priority codes but not the presentations/physician ratio, was inversely related to the proportion of patients with a length-of-stay under 4 h (slope - 0.40, 95% CI - 0.24 to - 0.27, R2 = 0.36). The variability of door-to-physician time and global length-of-stay are predicted by different factors. For appropriate benchmarking among EDs, the use of performance indicators should consider specific, hospital-level and patient-level factors.

The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission.

Triage is crucial for patient's management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient's admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73-0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56-0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp . The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.