Impact of Vaccination and the Omicron Variant on COVID-19-related Chest CT Findings: A Multicenter Study.

Background The SARS-CoV-2 Omicron variant has a higher infection rate than previous variants but results in less severe disease. However, the effects of Omicron and vaccination on chest CT findings are difficult to evaluate. Purpose To investigate the effect of vaccination status and predominant variant on chest CT findings, diagnostic scores, and severity scores in a multicenter sample of consecutive patients referred to emergency departments for proven COVID-19. Materials and Methods This retrospective multicenter study included adults referred to 93 emergency departments with SARS-CoV-2 infection according to a reverse-transcriptase polymerase chain reaction test and known vaccination status between July 2021 and March 2022. Clinical data and structured chest CT reports, including semiquantitative diagnostic and severity scores following the French Society of Radiology-Thoracic Imaging Society guidelines, were extracted from a teleradiology database. Observations were divided into Delta-predominant, transition, and Omicron-predominant periods. Associations between scores and variant and vaccination status were investigated with χ2 tests and ordinal regressions. Multivariable analyses evaluated the influence of Omicron variant and vaccination status on the diagnostic and severity scores. Results Overall, 3876 patients were included (median age, 68 years [quartile 1 to quartile 3 range, 54-80]; 1695 women). Diagnostic and severity scores were associated with the predominant variant (Delta vs Omicron, χ2 = 112.4 and 33.7, respectively; both P < .001) and vaccination status (χ2 = 243.6 and 210.1; both P < .001) and their interaction (χ2 = 4.3 [P = .04] and 28.7 [P < .001], respectively). In multivariable analyses, Omicron variant was associated with lower odds of typical CT findings than was Delta variant (odds ratio [OR], 0.46; P < .001). Two and three vaccine doses were associated with lower odds of demonstrating typical CT findings (OR, 0.32 and 0.20, respectively; both P < .001) and of having high severity score (OR, 0.47 and 0.33, respectively; both P < .001), compared with unvaccinated patients. Conclusion Both the Omicron variant and vaccination were associated with less typical chest CT manifestations of COVID-19 and lesser extent of disease. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Yoon and Goo in this issue.

Emergency whole-body CT scans in pediatric patients with trauma: patterns of injuries, yield of dual-phase scanning, and influence of second read on detection of injuries.

OBJECTIVES: To describe injury patterns in children with multiple trauma (MT), evaluate the yield of dual-phase whole-body CT (WBCT), and quantify missed injuries detected on second reading. METHODS: Remotely analyzed WBCT performed between 2011 and 2020 in 63 emergency departments on children admitted for MT were included. Second reading occurred within 24 h. Collected data included age, sex, mechanism, Injury Severity Score (ISS), radiologists' experience, time and duration of first reading, conclusion of both readings, and dosimetry. Melvin score assessed the clinical impact of missed injuries. RESULTS: Overall, 1114 patients were included, 1982 injuries were described in 662 patients (59.4%), 452/1114 (40.6%) WBCT were negative, and 314 (28.2%) patients had MT (≥ 2 body parts injured). The most frequent injuries were pulmonary contusions (8.3%), costal fractures (6.2%), and Magerl A1 vertebral fractures (4.9%). Overall, 151 injuries were missed in 92 (8.3%) patients. Independent predictors for missed injuries were age ≤ 4 years (p = 0.03), number of injured body parts ≥ 2 (p = 0.01), and number of injuries ≥ 3 (p < 0.001). Melvin score grade 3 lesions were found in 16/92 (17.4%) patients with missed injuries (1.4% of all WBCT), where only prolonged follow-up was necessary. Thirteen active bleeding or pseudoaneurysms were detected (0.7% of injuries). CONCLUSION: Injuries were diagnosed in 59.4% of patients. Double-reading depicted additional injuries in 8.3% of patients, significantly more in children ≤ 4 years, with ≥ 3 injuries or ≥ 2 injured body parts. As 28 % of patients had MT and 1.1% had active extravasation or pseudoaneurysm, indication for WBCT should be carefully weighted. KEY POINTS: • When performed as a first-line imaging evaluation, approximately 41% of WBCT for MT children were considered normal. • The three most common injuries were pulmonary contusions, costal fractures, and Magerl A1 vertebral fractures, but the patterns of traumatic injuries on WBCT depended on the children's age and the trauma mechanism. • The independent predictors of missed injuries were age ≤ 4 years, number of body parts involved ≥ 2, and total number of injuries ≥ 3.

What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing.

Although using standardized reports is encouraged, most emergency radiological reports in France remain in free-text format that can be mined with natural language processing for epidemiological purposes, activity monitoring or data collection. These reports are obtained under various on-call conditions by radiologists with various backgrounds. Our aim was to investigate what influences the radiologists' written expressions. To do so, this retrospective multicentric study included 30,227 emergency radiological reports of computed tomography scans and magnetic resonance imaging involving exactly one body region, only with pathological findings, interpreted from 2019-09-01 to 2020-02-28 by 165 radiologists. After text pre-processing, one-word tokenization and use of dictionaries for stop words, polarity, sentiment and uncertainty, 11 variables depicting the structure and content of words and sentences in the reports were extracted and summarized to 3 principal components capturing 93.7% of the dataset variance. In multivariate analysis, the 1st principal component summarized the length and lexical diversity of the reports and was significantly influenced by the weekday, time slot, workload, number of examinations previously interpreted by the radiologist during the on-call period, type of examination, emergency level and radiologists' gender (P value range: < 0.0001-0.0029). The 2nd principal component summarized negative formulations, polarity and sentence length and was correlated with the number of examination previously interpreted by the radiologist, type of examination, emergency level, imaging modality and radiologists' experience (P value range: < 0.0001-0.0032). The last principal component summarized questioning, uncertainty and polarity and was correlated with the type of examination and emergency level (all P values < 0.0001). Thus, the length, structure and content of emergency radiological reports were significantly influenced by organizational, radiologist- and examination-related characteristics, highlighting the subjectivity and variability in the way radiologists express themselves during their clinical activity. These findings advocate for more homogeneous practices in radiological reporting and stress the need to consider these influential features when developing models based on natural language processing.

The accuracy of teleradiologists in diagnosing COVID-19 based on a French multicentric emergency cohort.

OBJECTIVES: To evaluate the accuracy of diagnoses of COVID-19 based on chest CT as well as inter-observer agreement between teleradiologists during on-call duty and senior radiologists in suspected COVID-19 patients. MATERIALS AND METHODS: From March 13, 2020, to April 14, 2020, consecutive suspected COVID-19 adult patients who underwent both an RT-PCR test and chest CT from 15 hospitals were included in this prospective study. Chest CTs were immediately interpreted by the on-call teleradiologist and were systematically blind reviewed by a senior radiologist. Readings were categorised using a five-point scale: (1) normal; (2) non-infectious findings; (3) infectious findings but not consistent with COVID-19 infection; (4) consistent with COVID-19 infection; and (5) typical appearance of COVID-19 infection. The diagnostic accuracy of chest CT and inter-observer agreement using the kappa coefficient were evaluated over the study period. RESULTS: In total, 513 patients were enrolled, of whom 244/513 (47.6%) tested positive for RT-PCR. First readings were scored 4 or 5 in 225/244 (92%) RT-PCR+ patients, and between 1 and 3 in 201/269 (74.7%) RT-PCR- patients. The data were highly consistent (weighted kappa = 0.87) and correlated with RT-PCR (p < 0.001, AUC1st-reading = 0.89, AUC2nd-reading = 0.93). The negative predictive value for scores of 4 or 5 was 0.91-0.92, and the PPV for a score of 5 was 0.89-0.96 at the first and second readings, respectively. Diagnostic accuracy was consistent over the study period, irrespective of a variable prevalence rate. CONCLUSION: Chest CT demonstrated high diagnostic accuracy with strong inter-observer agreement between on-call teleradiologists with varying degrees of experience and senior radiologists over the study period. KEY POINTS: • The accuracy of readings by on-call teleradiologists, relative to second readings by senior radiologists, demonstrated a sensitivity of 0.75-0.79, specificity of 0.92-0.97, NPV of 0.80-0.83, and PPV of 0.89-0.96, based on "typical appearance," as predictive of RT-PCR+. • Inter-observer agreement between the first reading in the emergency setting and the second reading by the senior emergency teleradiologist was excellent (weighted kappa = 0.87).

Whole-Body CT in Patients with Multiple Traumas: Factors Leading to Missed Injury.

Purpose To determine radiologic and clinical markers predictive of missed injuries at early whole-body CT image interpretation. Materials and Methods For this retrospective study, 2354 consecutive whole-body CT examinations were performed in patients with multiple traumas from 26 hospitals interpreted at a teleradiology center study during on-call period from February 2011 to September 2016. All whole-body CT images were interpreted by the on-call radiologist and reviewed within 12-48 hours by another radiologist to detect missed injury as the standard of reference. The first and review reports of all examinations were retrospectively reviewed. Univariable and multivariable logistic regression with a stepwise selection method were performed to identify clinical and radiologic predictors of missed injury. Results This study included 639 women (27.1%) and 1715 men (72.8%). The median age of men, women, and the entire population was 34 years (age range, 1-96 years). On a per-scan basis, there were 304 (12.9%) missed injuries and 59 (2.5%) were clinically significant. On a per-injury basis, the missed injury rate was 530 of 5979 (8.8%). More than two injured body parts (odds ratio, 1.4 [95% confidence interval: 1.1, 1.8]; P = .01), patient age older than 30 years (odds ratio, 2.8 [95% confidence interval: 2.1, 3.8]; P < .001), and an initial clinical severity class of 1 (odds ratio, 1.9 [95% confidence interval: 1.3, 2.8]; P < .001) were independent predictive factors of missed injury. Conclusion Multiple traumas with more than two injured body parts, age older than 30 years, or an initial clinical severity class of 1 were associated with missed injury at whole-body CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Novelline in this issue.

Preoperative nutritional risk assessment in patients undergoing cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy for colorectal carcinomatosis.

BACKGROUND: Malnutrition is associated with increased postoperative morbidity in colorectal surgery. This study aimed to determine if preoperative nutritional markers could predict postoperative outcomes for patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) for peritoneal metastasis (PM) of colorectal origin. METHODS: All patients who underwent a complete CRS-HIPEC for colorectal PM between January 2009 and December 2014 were evaluated. Preoperative clinical and biological nutritional factors, including Body Mass Index (BMI), preoperative albumin and prealbumin levels were analysed. Preoperative computed tomography was used to measure the cross-sectional surface of the visceral and subcutaneous adipose tissue, at the third lumbar vertebrae, to assess the abdominal fat composition. Skeletal muscle mass was measured to assess for sarcopenia. RESULTS: Among 214 patients, 14 (6.5%) had a BMI ≥ 35 kg/m2, 90 (42%) were sarcopenic, 19 (9%) presented albumin <35 g/L and 2 (1%) had pre-albumin <20 mg/dL. Median values for visceral and subcutaneous fat surfaces were 99.2 cm2 and 198 cm2, respectively. Hypoalbuminemia was associated with worse overall survival (23 vs. 59 months, p = 0.015). The other nutritional factors did not impact overall or progression free survival after CRS-HIPEC for colorectal PM. In multivariate analysis, major post-operative complication and hypoalbuminemia were independently associated with decreased overall survival. CONCLUSIONS: Hypoalbuminemia appears as a strong predictive factor for decreased overall survival in patients presenting PM of colorectal origin undergoing CRS-HIPEC.

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

Changes in the content of radiological reports at population level could detect emerging diseases. Herein, we developed a method to quantify similarities in consecutive temporal groupings of radiological reports using natural language processing, and we investigated whether appearance of dissimilarities between consecutive periods correlated with the beginning of the COVID-19 pandemic in France. CT reports from 67,368 consecutive adults across 62 emergency departments throughout France between October 2019 and March 2020 were collected. Reports were vectorized using time frequency-inverse document frequency (TF-IDF) analysis on one-grams. For each successive 2-week period, we performed unsupervised clustering of the reports based on TF-IDF values and partition-around-medoids. Next, we assessed the similarities between this clustering and a clustering from two weeks before according to the average adjusted Rand index (AARI). Statistical analyses included (1) cross-correlation functions (CCFs) with the number of positive SARS-CoV-2 tests and advanced sanitary index for flu syndromes (ASI-flu, from open-source dataset), and (2) linear regressions of time series at different lags to understand the variations of AARI over time. Overall, 13,235 chest CT reports were analyzed. AARI was correlated with ASI-flu at lag = + 1, + 5, and + 6 weeks (P = 0.0454, 0.0121, and 0.0042, respectively) and with SARS-CoV-2 positive tests at lag = - 1 and 0 week (P = 0.0057 and 0.0001, respectively). In the best fit, AARI correlated with the ASI-flu with a lag of 2 weeks (P = 0.0026), SARS-CoV-2-positive tests in the same week (P < 0.0001) and their interaction (P < 0.0001) (adjusted R2 = 0.921). Thus, our method enables the automatic monitoring of changes in radiological reports and could help capturing disease emergence.

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

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.

Teleradiology as a relevant indicator of the impact of COVID-19 pandemic management on emergency room activities: a nationwide worrisome survey.

OBJECTIVES: To evaluate the impact of COVID-19's lockdown on radiological examinations in emergency services. METHODS: Retrospective, multicentre analysis of radiological examinations requested, via our teleradiology network, from 2017 to 2020 during two timeframes (calendar weeks 5-8 and then 12-15). We included CT scans or MRIs performed for strokes, multiple traumas (Body-CT), cranial traumas (CTr) and acute non-traumatic abdominal pain (ANTAP). We evaluated the number and percentages of examinations performed, of those with a pathological conclusion, and of examinations involving the chest. RESULTS: Our study included 25 centres in 2017, 29 in 2018, 43 in 2019 and 59 in 2020. From 2017 to 2019, the percentages of examinations were constant, which was also true for chest CTs. Each centre's number of examinations, gender distribution and patient ages were unchanged. In 2020, examinations significantly decreased: suspected strokes decreased by 36% (1052 vs 675, p < 0.001), Body-CT by 62% (349 vs 134, p < 0.001), CTr by 52% (1853 vs 895, p < 0.001) and for ANTAP, appendicitis decreased by 38% (45 vs 90, not statistically significant (NS)) sigmoiditis by 44% (98 vs 55, NS), and renal colic by 23% (376 vs 288, NS). The number of examinations per centre decreased by 13% (185.5 vs 162.5, p < 0.001), whereas the number of examinations of the "chest" region increased by 170% (1205 vs 3766, p < 0.001). CONCLUSION: Teleradiology enabled us to monitor the impact of the COVID-19 pandemic management on emergency activities, showing a global decrease in the population's use of care.

Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort.

Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions' semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status.