Dataset Information

OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or ultra-low-dose CT (OPTIMACT)-a randomised controlled trial chest X-ray or ultra-low-dose CT at the ED: design and rationale.

ABSTRACT:

Background

Chest X-ray has been the standard imaging method for patients suspected of non-traumatic pulmonary disease at the emergency department (ED) for years. Recently, ultra-low-dose chest computed tomography (ULD chest CT) has been introduced, which provides substantially more detailed information on pulmonary conditions that may cause pulmonary disease, with a dose in the order of chest X-ray (0.1 vs. 0.05 mSv). The OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or CT (OPTIMACT) study is a randomized trial designed to evaluate the effectiveness of replacing chest X-ray for ULD chest CT in the diagnostic work-up of patients suspected of non-traumatic pulmonary disease at the ED.

Methods

Two thousand four hundred patients presenting at the ED with pulmonary complaints and suspected of non-traumatic pulmonary disease will be enrolled in this multicenter, pragmatic, randomized trial. During randomly assigned periods of one calendar month, either conventional chest X-ray or ULD chest CT scan will be used as the imaging strategy. Randomization will rely on computer-generated blocks of 2 months to control for seasonal effects. Chest X-ray and ULD chest CT will be performed in a standardized way, after obtaining the clinical history and performing physical examination and initial laboratory tests. The primary outcome measure is functional health at 28 days. Secondary outcome measures are mental health, length of hospital stay, mortality within 28 days, quality-adjusted life years (QALYs) during the first 28 days, correct diagnoses at ED discharge as compared to the final post hoc diagnosis, and number of patients in follow-up because of incidental findings on chest X-ray or ULD chest CT. In an economic evaluation, we will estimate total health care costs during the first 28 days.

Discussion

This pragmatic trial will clarify the effects of replacing chest X-ray by ULD chest CT in daily practice, in terms of patient-related health outcomes and costs, in the diagnostic work-up of patients suspected of non-traumatic pulmonary disease at the ED.

Trial registration

The OPTIMACT trial is registered in the Netherlands National Trial Register under number NTR6163. The date of registration is December 6, 2016.

SUBMITTER: van den Berk IAH

PROVIDER: S-EPMC6460797 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or ultra-low-dose CT (OPTIMACT)-a randomised controlled trial chest X-ray or ultra-low-dose CT at the ED: design and rationale.

van den Berk Inge A H IAH Kanglie Maadrika M N P MMNP van Engelen Tjitske S R TSR Bipat Shandra S Dijkgraaf Marcel G W MGW Bossuyt Patrick M M PMM de Monyé Wouter W Prins Jan M JM Stoker Jaap J

Diagnostic and prognostic research 20180808

<h4>Background</h4>Chest X-ray has been the standard imaging method for patients suspected of non-traumatic pulmonary disease at the emergency department (ED) for years. Recently, ultra-low-dose chest computed tomography (ULD chest CT) has been introduced, which provides substantially more detailed information on pulmonary conditions that may cause pulmonary disease, with a dose in the order of chest X-ray (0.1 vs. 0.05 mSv). The OPTimal IMAging strategy in patients suspected of non-traumatic pu ...[more]

PMID: 31093568

Similar Datasets

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.

Project description:ObjectivesThe current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.MethodsIn this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE).ResultsThe radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8.ConclusionsThe results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction.Key points• Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.

Project description:ObjectiveFew studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR).Materials and methodsWe retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC).ResultsThe median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06).ConclusionQLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.

Dataset Information

OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or ultra-low-dose CT (OPTIMACT)-a randomised controlled trial chest X-ray or ultra-low-dose CT at the ED: design and rationale.

Background

Methods

Discussion

Trial registration

Publications

OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or ultra-low-dose CT (OPTIMACT)-a randomised controlled trial chest X-ray or ultra-low-dose CT at the ED: design and rationale.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets