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

ABSTRACT: 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.

SUBMITTER: Schuster P

PROVIDER: S-EPMC8076229 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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).

Project description:BackgroundUltra-low-dose CT (ULDCT) examinations of the chest at only twice the radiation dose of a chest X-ray (CXR) now offer a valuable imaging alternative to CXR. This trial prospectively compares ULDCT and CXR for the detection rate of diagnoses and their clinical relevance in a low-prevalence cohort of non-traumatic emergency department patients.MethodsIn this prospective crossover cohort trial, 294 non-traumatic emergency department patients with a clinically indicated CXR were included between May 2nd and November 26th of 2019 (www.clinicaltrials.gov: NCT03922516). All participants received both CXR and ULDCT, and were randomized into two arms with inverse reporting order. The detection rate of CXR was calculated from 'arm CXR' (n = 147; CXR first), and of ULDCT from 'arm ULDCT' (n = 147; ULDCT first). Additional information reported by the second exam in each arm was documented. From all available clinical and imaging data, expert radiologists and emergency physicians built a compound reference standard, including radiologically undetectable diagnoses, and assigned each finding to one of five clinical relevance categories for the respective patient.FindingsDetection rates for main diagnoses by CXR and ULDCT (mean effective dose: 0.22 mSv) were 9.1% (CI [5.2, 15.5]; 11/121) and 20.1% (CI [14.2, 27.7]; 27/134; P = 0.016), respectively. As an additional imaging modality, ULDCT added 9.1% (CI [5.2, 15.5]; 11/121) of main diagnoses to prior CXRs, whereas CXRs did not add a single main diagnosis (0/134; P < 0.001). Notably, ULDCT also offered higher detection rates than CXR for all other clinical relevance categories, including findings clinically irrelevant for the respective emergency department visit with 78.5% (CI [74.0, 82.5]; 278/354) vs. 16.2% (CI [12.7, 20.3]; 58/359) as a primary modality and 68.2% (CI [63.3, 72.8]; 245/359) vs. 2.5% (CI [1.3, 4.7]; 9/354) as an additional imaging modality.InterpretationIn non-traumatic emergency department patients, ULDCT of the chest offered more than twice the detection rate for main diagnoses compared to CXR.FundingThe Department of Biomedical Imaging and Image-guided Therapy of Medical University of Vienna received funding from Siemens Healthineers (Erlangen, Germany) to employ two research assistants for one year.

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

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