Chest Computed Tomography Lung Research Articles

CT lung lesions as predictors of early death or ICU admission in COVID-19 patients

ObjectiveThe main objective of this study was to investigate the prognostic value of early systematic chest computed tomography (CT) with quantification of lung lesions in coronavirus disease 2019 (COVID-19) patients. MethodsWe studied 572 patients diagnosed with COVID-19 (confirmed using polymerase chain reaction) for whom a chest CT was performed at hospital admission. Visual quantification was used to classify patients as per the percentage of lung parenchyma affected by COVID-19 lesions: normal CT, 0–10%, 11–25%, 26–50%, 51–75% and >75%. The primary endpoint was severe disease, defined by death or admission to the intensive care unit in the 7 days following first admission. ResultsThe mean patient age was 66.0 ± 16.0 years, and 343/572 (60.0%) were men. The primary endpoint occurred in 206/572 patients (36.0%). The extent of lesions on initial CT was independently associated with prognosis (odds ratio = 2.35, 95% confidence interval 1.24–4.46; p < 0.01). Most patients with lung involvement >50% (66/95, 69.5%) developed severe disease compared to patients with lung involvement of 26–50% (70/171, 40.9%) and ≤25% (70/306, 22.9%) (p < 0.01 and p < 0.01, respectively). None of the patients with normal CT (0/14) had severe disease. ConclusionChest CT findings at admission are associated with outcome in COVID-19 patients.

Lasting lessons learnt in the radiology department from the battle with COVID-19

Lasting lessons learnt in the radiology department from the battle with COVID-19

Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia.

PurposeTo review the chest computed tomography (CT) findings on the ultra-high-resolution CT (U-HRCT) in patients with the Novel coronavirus disease 2019 (COVID-19).Materials and MethodsIn February 2020, six consecutive patients with COVID-19 pneumonia (median age, 69 years) underwent U-HR CT imaging. U-HR-CT has a larger matrix size of 1024 × 1024 thinner slice thickness of 0.25 mm and can demonstrate terminal bronchioles in the normal lungs; as a result, Reid’s secondary lobules and their abnormalities can be identified. The distribution and hallmarks (ground-glass opacity, consolidation with or without architectural distortion, linear opacity, crazy paving) of the lung opacities on U-HRCT were visually evaluated on a 1 K monitor by two experienced reviewers. The CT lung volume was measured, and the ratio of the measured lung volume to the predicted total lung capacity (predTLC) based on sex, age and height was calculated.ResultsAll cases showed crazy paving pattern in U-HRCT. In these lesions, the secondary lobules were smaller than those in the un-affected lungs. CT lung volume decreased in two cases comparing predTLC.ConclusionU-HRCT can evaluate not only the distribution and hallmarks of COVID-19 pneumonia but also visualize local lung volume loss.

Efficiency and reproducibility in pulmonary nodule detection in simulated dose reduction lung CT images

PurposeTo determine the reproducibility and productivity of reduced dose chest computed tomography (CT) using a nodule detection task. Materials and methodsEighty-eight consecutive non-contrast CT examinations were performed using an automatic exposure system with a reference standard deviation of 8.5. Simulated raw data of a reduced dose scan (standard deviation at 21 and 29) were generated with a dose simulator. Original and simulated raw data were reconstructed to series of 7-mm-thick images (Original, Simulation A, Simulation B). In the first part of the reading experiment, three readers independently interpreted these images (88 cases × 3 series) and recorded the size, type, and location of the pulmonary nodules. The reading time for every case was recorded. In the second part of the experiment, the repeated interpretation of standard dose images was performed by two readers. Concordance or discordance of nodule detection between the first and the repeated reading result was assessed. ResultsA statistically significant difference in the detected nodule counts for lesions less than 5 mm by one reader was observed in simulation B images. Discordance of the interpretation result was found only in ground-glass nodules larger than 5 mm detected by one reader in simulation B images. There was no statistically significant difference in the reading time among the three image types. ConclusionSimulated standard deviation 21 images can reproduce the image interpretation result of original images, whereas simulated standard deviation 29 images may compromise the accuracy of nodule assessment. The effect on the reading time was not observed with dose reduction simulation.

Use of coronary artery calcium testing to improve coronary heart disease risk assessment in a lung cancer screening population: The Multi-Ethnic Study of Atherosclerosis (MESA)

BackgroundAssessment of coronary artery calcium (CAC) during lung cancer screening chest computed tomography (CT) represents an opportunity to identify asymptomatic individuals at increased coronary heart disease (CHD) risk. We determined the improvement in CHD risk prediction associated with the addition of CAC testing in a population recommended for lung cancer screening. MethodsWe included 484 out of 6814 Multi-Ethnic Study of Atherosclerosis (MESA) participants without baseline cardiovascular disease who met U.S. Preventive Service Task Force CT lung cancer screening criteria and underwent gated CAC testing. 10 year-predicted CHD risks with and without CAC were calculated using a validated MESA-based risk model and categorized into low (<5%), intermediate (5%–10%), and high (≥10%). The net reclassification improvement (NRI) and change in Harrell's C-statistic by adding CAC to the risk model were subsequently determined. ResultsOf 484 included participants (mean age = 65; 39% women; 32% black), 72 (15%) experienced CHD events over the course of follow-up (median = 12.5 years). Adding CAC to the MESA CHD risk model resulted in 17% more participants classified into the highest or lowest risk categories and a NRI of 0.26 (p = 0.001). The C-statistic improved from 0.538 to 0.611 (p = 0.01). ConclusionsCHD event rates were high in this lung cancer screening eligible population. These individuals represent a high-risk population who merit consideration for CHD prevention measures regardless of CAC score. Although overall discrimination remained poor with inclusion of CAC scores, determining whether those reclassified to an even higher risk would benefit from more aggressive preventive measures may be important.

Correlation between heart size and emphysema in patients with chronic obstructive pulmonary disease: CT-based analysis using inspiratory and expiratory scans.

The objective of this study was to investigate the relationship between the extent of emphysema and heart size in patients with chronic obstructive pulmonary disease (COPD) using inspiratory and expiratory chest computed tomography (CT). This retrospective study was approved by the institutional review board and informed consent was waived. We measured lung volume (LV), low attenuation area percent (%LAA; less than or equal to −950 HU), maximum cardiac area, and maximum transverse cardiac diameter on inspiratory/expiratory chest CT in 60 patients with COPD. Spearman rank correlation analysis was used to determine the correlations between the heart and lung CT measurements, and the correlations between these measurements and spirometric values. On inspiratory CT, the maximum transverse cardiac diameter was negatively correlated with LV (ρ = −0.42; p < 0.01) and %LAA (ρ = −0.43; p < 0.001). Furthermore, on expiratory CT, the maximum cardiac area was negatively correlated with LV (ρ = −0.35; p < 0.01) and %LAA (ρ = −0.37; p < 0.01), and there was a negative correlation between transverse cardiac diameter and %LAA (ρ = −0.34; p < 0.01). Although inspiratory cardiac size was not correlated with any of the spirometric values, the maximum cardiac area and transverse diameter on expiratory scans were significantly correlated with the reduced airflow values on spirometry (p < 0.01). In patients with COPD, the transverse cardiac diameter decreased as the emphysema progressed. A smaller cardiac area on expiratory CT suggested the presence of large LVs, emphysema, and airflow limitation in COPD.

MA01.07 Influence of Nodule Morphology on Inter-Reader Variability of Volume and Diameter Measurements in CT Lung Cancer Screening

The high number of false positive screen results is a major disadvantage of lung cancer screening by low-dose chest computed tomography (CT). Measurement strategy influences the false-positive rate, and nodule morphology may influence measurement of nodule size. Comparison between inter-reader variation for semi-automatic volume measurements and manual diameter measurements are scarce. Therefore, we aimed to evaluate the influence of nodule morphology on inter-reader variability and assessment of growth for semi-automatic volume measurements and manual diameter measurements, in intermediate-sized solid nodules found in CT lung cancer screening. Twenty-five nodules of each morphological category: smooth, lobulated, spiculated and irregular, were randomly selected from 93 participants of the Dutch-Belgian randomized lung cancer screening trial (NELSON). Semi-automatic volume measurements were performed using Syngo LungCARE® software. Two chest radiologists independently measured maximum and mean diameters manually. The impact of nodule morphology on inter-reader variability was evaluated based on the systematic error and 95% limits of agreement (LoA). Inter-reader variability was compared to volume change cutoff at 3-month follow-up based on NELSON for nodule growth and Lung-RADS diameter cutoff. For manual diameter measurements, a significant systematic deviation was found between readers in smooth, lobulated, and spiculated nodules. The deviation was up to 1.5 mm based on maximum diameter measurements, and 1.2 mm based on mean diameter measurements. For semi-automatic volume measurements, no statistically significant systematic deviation was found. For lobulated, spiculated, and irregular nodules, the 95%-LoA for mean diameter measurements was up to 66% larger than the 1.5 mm cutoff for nodule growth. For volume measurements, the 95%-LoA exceeded the 25% growth cutoff for spiculated and irregular nodules, but only by up to 12%. Nodule morphology has a greater effect on size assessment based on manual diameter measurements than based on volume measurements. The larger inter-reader variability for manual diameter measurement may cause misclassification of spiculated nodules when assessing growth in 24% of cases. Therefore, semi-automatic volume measurement is recommended for nodule size and growth determination in CT lung cancer screening.

Smoking cessation results in a clinical lung cancer screening program.

Lung cancer screening may provide a "teachable moment" for promoting smoking cessation. This study assessed smoking cessation and relapse rates among individuals undergoing follow-up low-dose chest computed tomography (CT) in a clinical CT lung screening program and assessed the influence of initial screening results on smoking behavior. Self-reported smoking status for individuals enrolled in a clinical CT lung screening program undergoing a follow-up CT lung screening exam between 1st February, 2014 and 31st March, 2015 was retrospectively reviewed and compared to self-reported smoking status using a standardized questionnaire at program entry. Point prevalence smoking cessation and relapse rates were calculated across the entire population and compared with exam results. All individuals undergoing screening fulfilled the National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology: Lung Cancer Screening v1.2012(®) high-risk criteria and had an order for CT lung screening. A total of 1,483 individuals underwent a follow-up CT lung screening exam during the study interval. Smoking status at time of follow-up exam was available for 1,461/1,483 (98.5%). A total of 46% (678/1,461) were active smokers at program entry. The overall point prevalence smoking cessation and relapse rates were 20.8% and 9.3%, respectively. Prior positive screening exam results were not predictive of smoking cessation (OR 1.092; 95% CI, 0.715-1.693) but were predictive of reduced relapse among former smokers who had stopped smoking for 2 years or less (OR 0.330; 95% CI, 0.143-0.710). Duration of program enrollment was predictive of smoking cessation (OR 0.647; 95% CI, 0.477-0.877). Smoking cessation and relapse rates in a clinical CT lung screening program rates are more favorable than those observed in the general population. Duration of participation in the screening program correlated with increased smoking cessation rates. A positive exam result correlated with reduced relapse rates among smokers recently quit smoking.

Phenotyping Chronic Lung Allograft Dysfunction Using Body Plethysmography and Computed Tomography.

Restrictive subtype of chronic lung allograft dysfunction (CLAD) was recently described after lung transplantation. This study compares different definitions of a restrictive phenotype in CLAD patients and impact on survival. Eighty-nine CLAD patients out of 1191 screened patients (September 1987 to July 2012) were included as complete longitudinal lung volume measurements and chest computed tomography (CT) after CLAD onset was available. CT findings and lung volumes were quantified and survival was calculated for distinctive groups and predictive factors for worse survival were investigated. Graft survival in patients with total lung capacity (TLC) between 90% and 81% of baseline (BL) (n = 13, 15%) in CLAD course was similar to those with TLC >90% BL (n = 64, 56%; log-rank test p = 0.9). Twelve patients (13%) developed a TLC ≤80% BL and 10 (11%) had significant parenchymal changes on CT, of whom 6 (46%) also had TLC ≤80% BL. CT changes correlated with TLC ≤80% BL (Φ-coefficient = 0.48, p = 0.001). Patients with either TLC ≤80% or significant CT changes (n = 16, 18%) had a significantly reduced survival (log-rank p < 0.001). Forced vital capacity loss at CLAD onset was associated with poorer survival but did not correlate with the TLC or CT changes. A restrictive subtype of CLAD may be defined by either TLC ≤80% BL or severe parenchymal changes on chest CT.

Morphological and quantitative evaluation of emphysema in chronic obstructive pulmonary disease patients: A comparative study of MRI with CT

To further validate the ability of ultrashort echo-time (UTE) magnetic resonance imaging (MRI) in quantifying lung density in patients diagnosed with chronic obstructive pulmonary disease (COPD) and to develop an MRI-based emphysema index (EI). Ten subjects clinically diagnosed with COPD (5M/5F, age 62.6 ± 8.5 years) and ten healthy subjects (2M/8F, age 48.9 ± 19.2 years) were imaged via UTE MRI at 3T (4 mm slices, 1.39 × 1.39 mm2 pixels). Chest computed tomography (CT) images (generally 5 mm slices, ≈0.55 × 0.55 mm2 pixels), acquired retrospectively, were compared to UTE MRI. CT lung densities, MR lung-signal density, and EI were quantified from both CT and UTE MR images via a quantitative automated analysis and compared to the percent predicted forced expiratory volume in 1 second (FEV1 % predicted). EI quantified in controls via CT and UTE MRI was 0.23 ± 0.78% and 2.40 ± 1.50%, respectively; in COPD subjects it was 13.3 ± 14.9% (P = 0.021) and 12.0 ± 9.8% (P = 0.013), respectively. Bland-Altman determined the mean differences and 95% limits of agreement for COPD subjects and healthy controls were 0.06 (12.50 to -12.38). Strong correlation (R2 = 0.79, P < 0.0001) existed between EIs quantified from both CT and UTE MRI. There was a slightly higher correlation between FEV1 % predicted and the UTE MRI EI (R2 = 0.65, P < 0.0001) compared to CT EI (R2 = 0.49, P < 0.0001). Our results demonstrate a significant positive correlation between lung density and EI assessed with CT and MRI. Furthermore, UTE MRI exhibits its potential as a diagnostic alternative to CT for assessing the extent and the severity of emphysema, particularly for longitudinal studies. J. Magn. Reson. Imaging 2016;44:1656-1663.

Lung CT imaging in patients with bronchopulmonary dysplasia: A systematic review.

Bronchopulmonary dysplasia (BPD) is a common respiratory complication of preterm birth and associated with long-term respiratory sequelae. Chest computed tomography (CT) is a sensitive tool to obtain insight in structural lung abnormalities and may be a predictor for later symptoms. To give an overview of chest CT scoring methods that are used to evaluate chest CT scans of BPD patients. To review which structural lung abnormalities are described in children and adults with BPD and whether these are related to clinical outcomes. An extensive literature search was conducted for relevant studies on chest CT imaging in patients born preterm with BPD. We retrieved 316 original papers of which 16 articles and three abstracts fulfilled our inclusion criteria. Overall, we identified nine different semi-quantitative scoring methods. Chest CT scans revealed structural abnormalities in >85% of BPD patients. These abnormalities are decreased pulmonary attenuation, opacities, bronchial wall thickening, and consolidations. Some have been found to be negatively correlated with lung function and respiratory symptoms. None of the currently described scoring systems are appropriately validated or superior over another. Future studies are needed to generate a validated and universal chest CT quantitative scoring method for patients with BPD. Pediatr Pulmonol. 2016; 51:975-986. © 2016 Wiley Periodicals, Inc.

FEMD Algorithm for Effective Segmentation of CT Lung Images

Image segmentation is the most important step in extracting information from medical images. Segmentation of pulmonary Chest Computed Tomography (CT) images is a precursor to most of the pulmonary image analysis schemes. The purpose of lung image segmentation is to separate the voxels corresponding to lung tissue from the anatomy of the surrounding. In this paper, an automated image segmentation method has been proposed inorder to segment the region of interest present in the CT Lung slices. The proposed approach utilizes Fuzzy logic with Earth Mover's Distance (FEMD) based refinement methods. The final segmented output is further refined by morphological based operators. The performance of the proposed method is compared with various segmentation methods such as Canny Sobel and Prewitt and we have obtained an average segmentation accuracy of 79.4091% for segmenting CT lung images.

The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images

Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.

Postmortem lung features in drowning cases on computed tomography

We sought to compare postmortem chest computed tomography (CT) features of drowning cases with autopsy findings, and to classify these features. We performed a retrospective analysis of high-resolution and multi-planar reconstruction chest CT images of drowning in 92 adults (54 men, 38 women; mean age 65.4 years) scanned before forensic autopsy. The average lung CT number was calculated from whole-lung images reconstructed on a 3D workstation. The statistically significant differences of CT numbers were assessed with an alpha level of 0.05. Postmortem chest CT image patterns were classified into six types: the two main types were ground-glass opacities with thickened pulmonary interstitium (n = 31), and a centrilobular distribution of ill-defined nodules along the airways (n = 38). Some cases were mixed type (n = 10). There were significant differences in CT numbers between each type. The remaining three types were consolidation (n = 5), emphysema and/or fibrosis (n = 4), and unclassifiable (n = 4). Postmortem CT images of drowning cases can be classified into three major types with a few exceptions.

Automated Lung Volumetry from Routine Thoracic CT Scans: How Reliable is the Result?

Today, lung volumes can be easily calculated from chest computed tomography (CT) scans. Modern postprocessing workstations allow automated volume measurement of data sets acquired. However, there are challenges in the use of lung volume as an indicator of pulmonary disease when it is obtained from routine CT. Intra-individual variation and methodologic aspects have to be considered. Our goal was to assess the reliability of volumetric measurements in routine CT lung scans. Forty adult cancer patients whose lungs were unaffected by the disease underwent routine chest CT scans in 3-month intervals, resulting in a total number of 302 chest CT scans. Lung volume was calculated by automatic volumetry software. On average of 7.2 CT scans were successfully evaluable per patient (range 2-15). Intra-individual changes were assessed. In the set of patients investigated, lung volume was approximately normally distributed, with a mean of 5283 cm(3) (standard deviation = 947 cm(3), skewness = -0.34, and curtosis = 0.16). Between different scans in one and the same patient the median intra-individual standard deviation in lung volume was 853 cm(3) (16% of the mean lung volume). Automatic lung segmentation of routine chest CT scans allows a technically stable estimation of lung volume. However, substantial intra-individual variations have to be considered. A median intra-individual deviation of 16% in lung volume between different routine scans was found.

Improved air trapping evaluation in chest computed tomography in children with cystic fibrosis using real-time spirometric monitoring and biofeedback

The quality of chest Computed Tomography (CT) images in children is dependent upon a sufficient breath hold during CT scanning. This study evaluates the influence of spirometric breath hold monitoring with biofeedback software on inspiratory and expiratory chest CT lung density measures, and on trapped air (TA) scoring in children with cystic fibrosis (CF). This is important because TA is an important component of early and progressive CF lung disease. A cross sectional comparison study was completed for chest CT imaging in two cohorts of CF children with comparable disease severity, using spirometric breath hold monitoring and biofeedback software (Copenhagen (COP)) or unmonitored breath hold manoeuvres (Gothenburg (GOT)). Inspiratory-expiratory lung density differences were calculated, and TA was scored to assess the difference between the two cohorts. Eighty-four chest CTs were evaluated. Mean (95%CI) change in inspiratory-expiratory lung density differences was 436 Hounsfield Units (HU) (408 to 464) in the COP cohort with spirometric breath hold monitoring versus 229 HU (188 to 269) in the GOT cohort with unmonitored breath hold manoeuvres (p<0.0001). The Mean TA (95%CI) score was 6.93 (6.05 to 7.82) in COP patients and 3.81 (2.89 to 4.73) in GOT (p<0.0001) patients. In children with comparable CF lung disease, spirometric breath hold monitoring during examination yielded a large difference in lung volume between inhalation and exhalation, and allowed for a significantly greater measured change in lung density and TA score, compared to unmonitored breath hold maneuvers. This has implications to the clinical use of chest CT, especially in children with early CF lung disease.

Identification of pulmonary fissures using a piecewise plane fitting algorithm

We describe an automated computerized scheme to identify pulmonary fissures depicted in chest computed tomography (CT) examinations from a novel perspective. Whereas CT images can be regarded as a cloud of points, the underlying idea is to search for surface-like structures in the three-dimensional (3D) Euclidean space by using an efficient plane fitting algorithm. The proposed plane fitting operation is performed in a number of small spherical lung sub-volumes to detect small planar patches. Using a simple clustering criterion based on their spatial coherence and surface area, the identified planar patches, assumed to represent fissures, are classified into different types of fissures, namely left oblique, right oblique and right horizontal fissures. The performance of the developed scheme was assessed by comparing with a manually created “reference standard” and the results obtained by a previously developed approach on a dataset of 30 lung CT examinations. The experiments show that the average discrepancy is around 1.0mm in comparison with the reference standard, while the corresponding maximum discrepancy is 20.5mm. In addition, 94% of the fissure voxels identified by the computerized scheme are within 3mm of the fissures in the reference standard. As compared to a previously developed approach, we also found that the newly developed scheme had a smaller discrepancy with the standard reference. In efficiency, it takes approximately 8min to identify the fissures in a chest CT examination on a typical PC. The developed scheme demonstrates a reasonable performance in terms of accuracy, robustness, and computational efficiency.

Parathyroid Carcinoma Presenting as a Hyperparathyroid Crisis

Parathyroid Carcinoma Presenting as a Hyperparathyroid Crisis

Pulmonary nodule registration: Rigid or nonrigid?

The primary aim of this study is to investigate the performance difference of rigid and nonrigid registration schemes in matching corresponding pulmonary nodules depicted on sequential chest computed tomography (CT) examinations. A gradient descent based rigid registration algorithm with scaling was developed and it handled the involved geometric transformations (i.e., translation, rescaling, shearing, and rotation) separately instead of optimizing them in a single pass. Given two lung CT examinations, the scaling and translation parameters were simply estimated from the lung volume dimensions (e.g., size and mass center), while the rotation parameters were optimized progressively using gradient descent. To investigate the performance difference of rigid and nonrigid schemes in pulmonary nodule registration, the well-known nonrigid Demons algorithm was implemented and tested along with the developed schemes against 60 diverse low-dose clinical lung CT examinations with average 2-yr follow-up scans. A verified cancer and its correspondence in the follow-up scan as well as their spatial locations (mass center) were identified in each examination. In addition to the computational efficiency, the accuracy of these registration procedures was assessed by computing the Euclidean distances between the corresponding nodules after the registration. To demonstrate the advantage of the developed algorithm, the authors also implemented a fast iterative closest point (ICP) based rigid algorithm and compared their performance. Our experiments on the collected chest CT examinations showed that the nodule registration errors in 3D Euclidean distance for the developed rigid affine approach, the traditional ICP algorithm, and the refining nonrigid Demons algorithm were 9.6, 9.8, and 10.0 mm, respectively, and the corresponding computational costs in time were 5, 300, and 55 s, respectively. A rigid solution may be preferred in practice for the pulmonary nodule registration in longitudinal studies because of its relatively high efficiency and sufficient accuracy for the clinical need.

Normal d-dimer levels in emergency department patients suspected of acute pulmonary embolism

ObjectivesWe sought to determine: 1) whether normal d-dimer enzyme-linked immunosorbent assay (ELISA) assays predicted the absence of pulmonary embolism (PE) in the high-volume emergency department (ED) of the Brigham and Women’s Hospital, and 2) whether ED physicians accepted normal d-dimer levels as confirmation of no PE without further diagnostic testing such as lung scanning, chest computed tomography (CT) scanning, or pulmonary angiography. BackgroundAlthough the plasma d-dimer ELISA is a sensitive screening test for excluding acute PE, this laboratory marker has not been widely integrated into clinical algorithms such as creatine kinase-MB fraction or troponin testing for acute myocardial infarction. MethodsWe mandated that ED physicians order d-dimer ELISA tests on all patients suspected of acute PE. We reviewed the clinical record of each ED patient initially evaluated for suspected PE during the year 2000. We determined whether additional imaging tests for PE were obtained and whether the final diagnosis was PE. ResultsOf 1,106 d-dimer assays, 559 were elevated and 547 were normal. Only 2 of 547 had PE despite a normal d-dimer. The sensitivity of the d-dimer ELISA for acute PE was 96.4% (95% confidence interval [CI]: 87.5% to 99.6%), and the negative predictive value was 99.6% (95% CI: 98.7% to >99.9%). Nevertheless, 24% of patients with normal d-dimers had additional imaging tests for PE. ConclusionsThe d-dimer ELISA has a high negative predictive value for excluding PE. By paying more attention to normal d-dimer results, fewer chest CT scans and lung scans will be required, and improvements may be realized in diagnostic efficiency and cost reduction.