Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France

Abstract

Background COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors. Results A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90–0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29). Conclusion Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators.

Using the Textual Content of Radiological Reports to Detect Emerging Diseases: A Proof-of-Concept Study of COVID-19

“…As the examinations were performed in several centers scattered across France, these data sampled emergency activity and provided an overview of what was occurring in emergency departments. A prior study highlighted that teleradiological monitoring of the SFR-SIT diagnostic score could approximate the course of the COVID-19 pandemic in France [ 23 ]. However, developing such a workflow relying on the SFR-SIT score implies that we already know that a new disease has emerged and its semiology.…”

Emergency department CT examinations demonstrate no evidence of early viral circulation at the start of the COVID-19 pandemic—a multicentre epidemiological study

“…Our study emphasises the value of common information tools as employed in teleradiological structures working for several partners throughout a wide territory because they can rapidly provide databases that can be analysed at different scales: (i) macroscopically, to identify trends and breakdowns in radiological activities and findings [ 8 ], and (ii) microscopically, to identify small groups of patients for specific biological analyses. We believe that this two-steps approach, which combines (i) text mining to automatically annotate radiological reports for pathological radiological features in large databases and (ii) time series analysis to detect abnormal trends in the use of those radiological features over time, could be helpful for detecting and monitoring emerging and recurrent infectious diseases.…”

“…IMADIS is fully dedicated to remote interpretation of emergency imaging throughout France. We showed that the number of chest CTs performed in a workflow devoted to COVID-19 was strongly correlated with the number of patients hospitalised for proven COVID-19 infection, ICU admission and COVID-19-related deaths [ 8 ]. Moreover, several studies have stressed the diagnostic accuracy of chest CT for COVID-19 and its ability to assess disease extension [ 9 – 12 ], which has been formalised into diagnostic and severity scores promoted by radiological societies since April 2020 [ 1 , 13 , 14 ].…”

How artificial intelligence improves radiological interpretation in suspected pulmonary embolism

“…Fourth, we did not observe an increase in the diagnosis of PE following AI clinical implementation, as shown in comparisons between cohort-2018 and cohort-2020. However, cohort-2020 might have been biased by the COVID-19 pandemic, which resulted in more requests for chest CT, overall [ 37 ], and specifically for suspected PE [ 38 ]—though these rises were also due to the increase in partner centers of IMADIS. The injection quality was rated as poor in only 5.6% of examinations, whereas respiratory artefacts were reported in 30%.…”