Supine Chest Radiograph Research Articles

Lung ultrasonography underdiagnoses clinically significant pneumothorax

BackgroundUltrasonography for trauma is an integral part of the Advanced Trauma Life Support algorithm and supported extensively in the literature. The reliability of chest ultrasonography as a screening examination for pneumothorax during initial trauma evaluation is unclear. We performed a prospective study where we hypothesized that chest ultrasonography would have low sensitivity for detecting clinically significant pneumothorax. MethodsA prospective observational analysis of patients with blunt chest trauma at a level 1 trauma center was performed. Patients included had supine chest radiography and chest ultrasonography performed prior to intervention as well as confirmatory computed tomographic imaging. All chest ultrasonography was performed in the trauma bay by a registered sonographer. All imaging was evaluated by an attending trauma surgeon and radiologist in real time. ResultsOf 2,185 patients screened with a diagnosis of blunt thoracic trauma, 1,489 patients had chest radiography, chest ultrasonography, and confirmatory computed tomography and were included for analysis. Patients were 71% male, with median age of 42 years, and mean Injury Severity Score of 6. The sensitivity of chest ultrasonography to detect pneumothorax was low. Chest ultrasonography had a false negative rate of 72% (n = 58), with 22% (n = 13) undergoing tube thoracostomy. Patients with false negative examinations had lower initial O2 saturation and systolic blood pressure and were more likely to have rib fractures compared with true negative chest ultrasonography examinations. ConclusionChest ultrasonography performed on initial trauma evaluation has low sensitivity with a high rate of false negative examinations. Because many of these false negative results are clinically significant requiring thoracostomy, using chest ultrasonography alone to screen for pneumothorax should be done with caution.

Artificial Intelligence to Assess Tracheal Tubes and Central Venous Catheters in Chest Radiographs Using an Algorithmic Approach With Adjustable Positioning Definitions.

To develop and validate an artificial intelligence algorithm for the positioning assessment of tracheal tubes (TTs) and central venous catheters (CVCs) in supine chest radiographs (SCXRs) by using an algorithm approach allowing for adjustable definitions of intended device positioning. Positioning quality of CVCs and TTs is evaluated by spatially correlating the respective tip positions with anatomical structures. For CVC analysis, a configurable region of interest is defined to approximate the expected region of well-positioned CVC tips from segmentations of anatomical landmarks. The CVC/TT information is estimated by introducing a new multitask neural network architecture for jointly performing type/existence classification, course segmentation, and tip detection. Validation data consisted of 589 SCXRs that have been radiologically annotated for inserted TTs/CVCs, including an experts' categorical positioning assessment (reading 1). In-image positions of algorithm-detected TT/CVC tips could be corrected using a validation software tool (reading 2) that finally allowed for localization accuracy quantification. Algorithmic detection of images with misplaced devices (reading 1 as reference standard) was quantified by receiver operating characteristics. Supine chest radiographs were correctly classified according to inserted TTs/CVCs in 100%/98% of the cases, thereby with high accuracy in also spatially localizing the medical device tips: corrections less than 3 mm in >86% (TTs) and 77% (CVCs) of the cases. Chest radiographs with malpositioned devices were detected with area under the curves of >0.98 (TTs), >0.96 (CVCs with accidental vessel turnover), and >0.93 (also suboptimal CVC insertion length considered). The receiver operating characteristics limitations regarding CVC assessment were mainly caused by limitations of the applied CXR position definitions (region of interest derived from anatomical landmarks), not by algorithmic spatial detection inaccuracies. The TT and CVC tips were accurately localized in SCXRs by the presented algorithms, but triaging applications for CVC positioning assessment still suffer from the vague definition of optimal CXR positioning. Our algorithm, however, allows for an adjustment of these criteria, theoretically enabling them to meet user-specific or patient subgroups requirements. Besides CVC tip analysis, future work should also include specific course analysis for accidental vessel turnover detection.

Occult pneumothorax in patients with blunt chest trauma: key findings on supine chest radiography.

In patients with multiple trauma, a supine chest radiography [chest X-ray (CXR)] is preferred over a erect CXR. However, this method has limitations in detecting post-traumatic pneumothorax. The use of chest computed tomography (CT) to detect traumatic pneumothorax is well known. However, pneumothorax that is not detected before a chest CT scan is known as an occult pneumothorax (OP), and it can cause serious complications in the patient. This study sought to evaluate the frequency and risk factors for OP in trauma patients. Patients who suffered thoracic trauma at the Level 1 Regional Trauma Center of Wonju Severance Christian Hospital between 2015 and 2022 were included in this study. All patients were at least 18 years old. The study reviewed all patients' supine CXR and chest CT images and classified them into five radiographic diagnoses: pneumothorax, rib fracture, subcutaneous emphysema, lung contusion, and pneumomediastinum. The study included 1,284 patients, all with diagnoses of pneumothorax, rib fracture, subcutaneous emphysema, lung contusion, and pneumomediastinum following supine CXR and chest CT. The patient's average age was 58.3±15.2 years. Pneumothorax diagnosis on supine CXR had the lowest accuracy, at 46.7%, and the lowest sensitivity, at 12.7%. In univariate analysis, rib fracture, lung contusion, and subcutaneous emphysema on supine CXR were all found to be statistically significant regarding traumatic OP. In multivariate analysis, the risk factors for OP were lung contusion [odds ratio (OR), 1.440; 95% confidence interval (CI): 1.115-1.860; P=0.005] and subcutaneous emphysema (OR, 25.883; 95% CI: 13.155-50.928; P<0.001) on supine CXR. The lung contusion and subcutaneous emphysema in supine CXR of trauma patients indicate the presence of OP. Therefore, if chest CT cannot be performed immediately due to unstable vital signs or other circumstances, recognizing the above radiological findings of traumatic pneumothorax may be necessary.

Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs.

Background Supine chest radiography for bedridden patients in intensive care units (ICUs) is one of the most frequently ordered imaging studies worldwide. Purpose To evaluate the diagnostic performance of a neural network-based model that is trained on structured semiquantitative radiologic reports of bedside chest radiographs. Materials and Methods For this retrospective single-center study, children and adults in the ICU of a university hospital who had been imaged using bedside chest radiography from January 2009 to December 2020 were reported by using a structured and itemized template. Ninety-eight radiologists rated the radiographs semiquantitatively for the severity of disease patterns. These data were used to train a neural network to identify cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis. A held-out internal test set (100 radiographs from 100 patients) that was assessed independently by an expert panel of six radiologists provided the ground truth. Individual assessments by each of these six radiologists, by two nonradiologist physicians in the ICU, and by the neural network were compared with the ground truth. Separately, the nonradiologist physicians assessed the images without and with preliminary readings provided by the neural network. The weighted Cohen κ coefficient was used to measure agreement between the readers and the ground truth. Results A total of 193 566 radiographs in 45 016 patients (mean age, 66 years ± 16 [SD]; 61% men) were included and divided into training (n = 122 294; 64%), validation (n = 31 243; 16%), and test (n = 40 029; 20%) sets. The neural network exhibited higher agreement with a majority vote of the expert panel (κ = 0.86) than each individual radiologist compared with the majority vote of the expert panel (κ = 0.81 to ≤0.84). When the neural network provided preliminary readings, the reports of the nonradiologist physicians improved considerably (aided vs unaided, κ = 0.87 vs 0.79, respectively; P < .001). Conclusion A neural network trained with structured semiquantitative bedside chest radiography reports allowed nonradiologist physicians improved interpretations compared with the consensus reading of expert radiologists. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Collaborative Artificial Intelligence in Practice: The Next Steps.

Collaborative Artificial Intelligence in Practice: The Next Steps.

This Month in Anesthesiology

This Month in Anesthesiology

Validation of a Deep Learning-based Automatic Detection Algorithm for Measurement of Endotracheal Tube-to-Carina Distance on Chest Radiographs.

Improper endotracheal tube (ETT) positioning is frequently observed and potentially hazardous in the intensive care unit. The authors developed a deep learning-based automatic detection algorithm detecting the ETT tip and carina on portable supine chest radiographs to measure the ETT-carina distance. This study investigated the hypothesis that the algorithm might be more accurate than frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement. A deep learning-based automatic detection algorithm was developed using 1,842 portable supine chest radiographs of 1,842 adult intubated patients, where two board-certified intensivists worked together to annotate the distal ETT end and tracheal bifurcation. The performance of the deep learning-based algorithm was assessed in 4-fold cross-validation (1,842 radiographs), external validation (216 radiographs), and an observer performance test (462 radiographs) involving 11 critical care clinicians. The performance metrics included the errors from the ground truth in ETT tip detection, carina detection, and ETT-carina distance measurement. During 4-fold cross-validation and external validation, the median errors (interquartile range) of the algorithm in ETT-carina distance measurement were 3.9 (1.8 to 7.1) mm and 4.2 (1.7 to 7.8) mm, respectively. During the observer performance test, the median errors (interquartile range) of the algorithm were 2.6 (1.6 to 4.8) mm, 3.6 (2.1 to 5.9) mm, and 4.0 (1.7 to 7.2) mm in ETT tip detection, carina detection, and ETT-carina distance measurement, significantly superior to that of 6, 10, and 7 clinicians (all P < 0.05), respectively. The algorithm outperformed 7, 3, and 0, 9, 6, and 4, and 5, 5, and 3 clinicians (all P < 0.005) regarding the proportions of chest radiographs within 5 mm, 10 mm, and 15 mm error in ETT tip detection, carina detection, and ETT-carina distance measurement, respectively. No clinician was significantly more accurate than the algorithm in any comparison. A deep learning-based algorithm can match or even outperform frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement.

Detecting Endotracheal Tube and Carina on Portable Supine Chest Radiographs Using One-Stage Detector with a Coarse-to-Fine Attention.

In intensive care units (ICUs), after endotracheal intubation, the position of the endotracheal tube (ETT) should be checked to avoid complications. The malposition can be detected by the distance between the ETT tip and the Carina (ETT–Carina distance). However, it struggles with a limited performance for two major problems, i.e., occlusion by external machine, and the posture and machine of taking chest radiographs. While previous studies addressed these problems, they always suffered from the requirements of manual intervention. Therefore, the purpose of this paper is to locate the ETT tip and the Carina more accurately for detecting the malposition without manual intervention. The proposed architecture is composed of FCOS: Fully Convolutional One-Stage Object Detection, an attention mechanism named Coarse-to-Fine Attention (CTFA), and a segmentation branch. Moreover, a post-process algorithm is adopted to select the final location of the ETT tip and the Carina. Three metrics were used to evaluate the performance of the proposed method. With the dataset provided by National Cheng Kung University Hospital, the accuracy of the malposition detected by the proposed method achieves and the ETT–Carina distance errors are less than mm.

Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma.

Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network - Annalise CXR V1.2 (Annalise.ai) - for detection of traumatic injuries on supine chest radiographs. Chest radiographs with a CT performed within 24 h in the setting of trauma were retrospectively identified at a level one adult trauma centre between January 2009 and June 2019. Annalise.ai assessment of the chest radiograph was compared to the radiologist report of the chest radiograph. Contemporaneous CT report was taken as the ground truth. Agreement with CT was measured using Cohen's κ and sensitivity/specificity for both AI and radiologists were calculated. There were 1404 cases identified with a median age of 52 (IQR 33-69) years, 949 males. AI demonstrated superior performance compared to radiologists in identifying pneumothorax (p = 0.007) and segmental collapse (p = 0.012) on chest radiograph. Radiologists performed better than AI for clavicle fracture (p = 0.002), humerus fracture (p < 0.0015) and scapula fracture (p = 0.014). No statistical difference was found for identification of rib fractures and pneumomediastinum. The evaluated AI performed comparably to radiologists in interpreting chest radiographs. Further evaluation of this AI program has the potential to enable it to be safely incorporated in clinical processes. Clinically useful AI programs represent promising decision support tools.

Congenital Lobar Emphysema in a 17-Day-Old Male Neonate: The Plain Radiographic Features and a Case Report

Congenital lobar emphysema (CLE) is a rare respiratory disorder which allows air to enter the lungs but can’t escape causing over-inflation of the lobes of the lung. This condition (CLE) is commonly detected in the newborn or young infants but some cases are diagnosed in adulthood. Congenital lobar emphysema is a rare congenital malformation with a prevalence of 1 in 20,000 to 1 in 30, 000, and more prevalent in males, with a male to female ratio of about 3:1. This is a 17 day old male neonate that presented with respiratory difficulty and .had a supine chest radiograph that showed an overinflated left lung field more on the upper and middle zones with flattening of the left hemi diaphragm, marked shift of the heart and mediastinum to the right with some degree of loss of volume involving the right lung. We report the radiographic findings of this case due the rare nature of congenital lobar emphysema.

Diagnosis of Traumatic Pneumothorax: A Comparison between Lung Ultrasound and Supine Chest Radiographs.

Background/ObjectiveTraumatic pneumothorax is an ominous condition necessitating urgent appropriate action. It is typically detected on chest X-rays; however, these may not be able to detect the presence of a subtle pneumothorax, especially in supine position. Lung ultrasound is emerging as a promising modality for detecting pneumothorax in trauma patients. The aim of our study was to compare ultrasound with supine chest radiography for the detection of pneumothorax in trauma patients.Materials and MethodsThis was a prospective, single-blinded study carried out on 212 adult thoracoabdominal trauma patients who underwent ultrasound FAST and supine (AP) chest radiography. During the FAST sonography, ultrasound thorax was done to rule out pneumothorax. Only those cases were considered (118) in which the presence or absence of pneumothorax could be confirmed on CT done subsequently or where pneumothorax was confirmed by air escape on chest tube placement, wherever indicated, and the results were compared with sonographic and chest X-ray findings.Observation/ResultsThere were 48 true positives on CT/chest tube insertion. Among these, ultrasound was able to correctly detect pneumothorax in 43 patients, while supine chest X-rays correctly identified 33 cases. Sensitivity of ultrasound was 89.6 vs. 68.8% of supine chest radiography. Lung ultrasound also had a higher negative predictive value as compared to supine chest X-rays.ConclusionsLung ultrasound is more sensitive in detecting traumatic pneumothorax than supine chest X-rays, in addition to having numerous other inherent advantages over chest radiography. It should be incorporated in the emergency assessment of thoracic trauma patients to rule out pneumothorax.Clinical significanceLung sonography is more sensitive in detecting traumatic pneumothorax than supine chest X-rays. No added equipment is required, and the procedure can be carried out at the time of doing ultrasound FAST, thus saving precious time in trauma patients.How to cite this articleBhoil R, Kumar R, Kaur J, Attri PK, Thakur R. Diagnosis of Traumatic Pneumothorax: A Comparison between Lung Ultrasound and Supine Chest Radiographs. Indian J Crit Care Med 2021;25(2):176–180.

Chest ultrasonography by frontline physicians is more sensitive than CXR for diagnosing pneumothorax in trauma patients.

Chan KK, Joo DA, McRae AD, et al. Chest ultrasonography versus supine chest radiography for diagnosis of pneumothorax in trauma patients in the emergency department. Cochrane Database Syst Rev. 2020;7:CD013031. 32702777.

MDCT OF ABDOMINAL WALL LUMBAR HERNIAS

Purpose:To review the anatomical landmarks of the abdominal wall lumbar region and its normal appearance on multidetector computed tomography (MDCT) and to briefly describe the MDCT features of lumbar hernias.Diagnosis of traumatic diaphragmatic hernia due to blunt abdominal trauma requires a high index of suspicion. This study was conducted to assess the accuracy of multidetector computed tomogram (MDCT) in the diagnosis of traumatic diaphragmatic hernia.Diaphragmatic injuries remain a diagnostic challenge for both radiologists and surgeons. The detection of traumatic diaphragmatic rupture in the acute setting is problematic because specific clinical signs are usually not evident. Furthermore, the high frequency of associated injuries (52–100%) may distract from diaphragmatic injury. In conservatively managed patients, the rate of initially missed diaphragmatic injuries ranges from 12 to 66%, and they may even be overlooked at laparotomy. Diagnosis of a diaphragmatic injury requires a high index of suspicion, as delayed diagnosis increases the chance of visceral herniation and strangulation, which has mortality as high as 60%. Thus, the ability to detect diaphragmatic injuries with noninvasive techniques is increasingly important. Initial reports found CT to have sensitivity equal to that of chest radiography (i.e., 0–50%). Because of a dramatic reduction in motion and beam-hardening artifacts and significant improvement of spatial resolution, especially along the z-axis, helical CT and multisection CT allow better demonstration of most subtle signs of diaphragmatic herniation. In addition, these are also useful tools in the evaluation of patients with multiple traumatic injuries. Traumatic diaphragmatic hernias (TDHs) are sometimes difficult to identify at an early stage and can consequently result in diagnostic delays with life-threatening outcomes. It is the aim of this case study to highlight the difficulties encountered with the earlier detection of traumatic diaphragmatic hernias. Methods: We performed a retrospective search of the imaging report database from November 2007 to October 2011. We retrieved the clinical data and MDCT studies of patients suffering from abdominal wall lumbar hernias. We reviewed the imaging features of abdominal lumbar hernias and compared those with the normal appearance of the lumbar region in asymptomatic individuals.We assessed variables such as age, gender, mechanism of trauma, methods of diagnosis, herniated organs and associated lesions, time of evolution, morbidity, and mortality. Anteroposterior supine chest radiograph, which was performed in all patients, was also analyzed. Computed tomogram (CT) was performed on four-slice MDCT after an IV bolus of iodinated contrast agents. A slice thickness of 4 mm at a pitch of 1.5 was useful to evaluate thorax and abdomen with reconstruction at 1 mm reconstruction increment. An oral contrast agent was given whenever required. Multiplanar reconstruction was done in sagittal and coronal planes. Images were read in lung parenchyma, soft tissues, and bone windows. Findings were analyzed in a prospective manner to evaluate their use as a diagnostic modality as well as to determine their contribution to patient management. Results:We classified lumbar wall hernias as diffuse, superior (or Grynfelt–Lesshaft) and inferior (or Petit) lumbar hernias. We briefly describe the imaging features of each subtype and review the anatomy and MDCT appearance of normal lumbar region.Currently available MDCT provides an excellent opportunity for reviewing the normal anatomy of the wall lumbar region and may be considered a useful modality for evaluating lumbar hernias.Regarding Diaphragmatic hernia following blunt trauma:MDCT is a highly accurate modality for diagnosing traumatic diaphragmatic hernia. In addition, it is fast and compatible with various life-support systems hence, it can be used in acute trauma setting for making a diagnosis and helping in the management.Delayed traumatic diaphragmatic hernias are not common, but can lead to serious consequences once occurred. Early detection of diaphragmatic injuries is crucial to prevent the occurrence of dTDHs. Surgeons should maintain a high suspicion for injuries of the diaphragm in patients who had suffered abdominal or lower chest traumas, especially during the initial surgical explorations. The need for radiographical follow-ups is emphasized to detect diaphragmatic injuries at an earlier stage.

Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists.

Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies. Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients. One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between. Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78-0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist's maximum sensitivity up to 0.87 (95% CI, 0.76-0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659-0.815) versus radiologist's area under the receiver-operating characteristic curve of 0.779 (0.723-0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist's and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662-0.817) versus radiologist's area under the receiver-operating characteristic curve of 0.698 (0.646-0.749) with similar diagnostic metrics for receiver-operating characteristic operating points. Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence-assisted reading setting.

The position of the inferior vena cava-right atrium junction on simple radiography: implication for optimal veno-venous extracorporeal membrane oxygenation cannula tip position.

Extracorporeal membrane oxygenation (ECMO) is increasingly used. For effective oxygenation, position of cannula tip is important. The objective of this study is to identify the position of the inferior vena cava-right atrium junction (IRJ) using the vertebral body unit (VBU) and carina. This may be useful if applied to the assessment of ECMO cannula tip positions. A total of 182 patients who underwent chest computed tomography (CT) and supine chest radiography (CXR) between January 2017 and July 2018 were included, who had not previously received ECMO support. The position of the IRJ was evaluated using VBU and the efficacy of the VBU for locating the IRJ with the carina was analyzed. The mean distance (SD) from carina to IRJ was 79.2 (9.6) mm on chest CT. The mean distance in VBUs (SD) at the level of the carina was 22.3 (1.5) mm on chest CT and 23.3 (2.1) mm on CXR. The mean IRJ position was 3.6 VBUs below the carina on CT and 3.4 VBUs below on CXR with 95% limits of agreement between -0.05 and 0.5. The mean position of the IRJ was estimated to be 3.4 VBUs below the carina on supine CXR. This may be useful if applied to the assessment of ECMO cannula tip positions.

Chest ultrasonography versus supine chest radiography for diagnosis of pneumothorax in trauma patients in the emergency department

Chest ultrasonography versus supine chest radiography for diagnosis of pneumothorax in trauma patients in the emergency department

To reduce routine computed tomographic angiography for thoracic aortic injury assessment in level II blunt trauma patients using three mediastinal signs on the initial chest radiograph: a preliminary report.

CTA is routinely ordered on level II blunt thoraco-abdominally injured patients for assessment of injury to the thoracic aorta. The vast majority of such assessments are negative. The question being asked is, Does the accurate interpretation of the three mediastinal signs permit reliable determination of which patients need CTA for aortic assessment? The purpose of this investigation was to evaluate the role of three specifically selected mediastinal anatomic signs on the initial supine chest radiograph (CXR) of adult level II blunt thoraco-abdominally injured patients for the presence or absence of a mediastinal hematoma. The presence of a mediastinal hematoma is typically used as an indicator for computed tomographic angiography (CTA). The three mediastinal signs are the right para-tracheal stripe (RPTS), left para-spinal line (LPSL), and the left apical extra-pleural area (LAPA). The patient triage designation (level II trauma) was made by the attending physician at the time of admission. The initial CXR image and the CTA report of the 197 adult blunt level II thoraco-abdominally injured patients obtained on the day of admission were compared. The CXR of each of the 197 patients was independently assessed by each of four observers specifically for the status of the three mediastinal signs. Each observer was blinded to the CTA report until after the status of the three mediastinal sign evaluation had been determined. Two or three of the mediastinal signs being positive were required to determine that the CXR was positive for a mediastinal hematoma. Two or three of the selected mediastinal signs were normal in 192 (97.5%) patients. None of these patients had either a mediastinal hematoma or a major aortic injury on CTA. In each of the remaining five (2.5%) patients, two or three of the mediastinal signs were abnormal. Each of these patients had a mediastinal hematoma and a major thoracic aortic injury on CTA. This preliminary study suggests that the accurate interpretation of the three specifically selected mediastinal signs on the initial supine CXR of adult level II blunt thoraco-abdominally injured patients could reduce the need for routine CTA for thoracic aortic injury assessment, and requires verification by an additional study.

Point-of-Care Diagnostic Device for Traumatic Pneumothorax: Low Sensitivity of the Unblinded PneumoScan™

Background Traumatic Pneumothorax (PTX) is a potentially life-threatening injury. It requires a fast and accurate diagnosis and treatment, but diagnostic tools are limited. A new point-of-care device (PneumoScan) based on micropower impulse radar (MIR) promises to diagnose a PTX within seconds. In this study, we compare standard diagnostics with PneumoScan during shock-trauma-room management. Patients and Methods Patients with blunt or penetrating chest trauma were consecutively included in the study. All patients were examined including clinical examination with auscultation (CE) and supine chest radiography (CXR). In addition, PneumoScan-readings and thoracic ultrasound scan (US) were performed. Computed tomography (CT) served as gold standard. Results CT scan revealed PTX in 11 patients. PneumoScan detected two PTX correctly but missed nine. 15 false-positive results were found by PneumoScan, leading to a sensitivity of 20% and specificity of 80%. Six PTX were detected through CE (sensitivity: 54,5%). CXR detected four (sensitivity: 27,3%) and thoracic US two PTX correctly (sensitivity: 25%). Conclusion The unblinded PneumoScan prototype did not confirm the promising results of previous studies. The examined standard diagnostics and thoracic US showed rather weak sensitivity as well. Until now, there is no appropriate point-of-care tool to rule out PTX.

Assessment of Cardiac Device Position on Supine Chest Radiograph in the ICU: Introduction and Applicability of the Aortic Valve Location Ratio.

The use of intracardiac assist devices is expanding, and correct position of these devices is required for optimal functioning. The aortic valve is an important landmark for positioning of those devices. It would be of great value if the device position could be easily monitored on plain supine chest radiograph in the ICU. We introduce a ratio-based tool for determination of the aortic valve location on plain supine chest radiograph images, which can be used to evaluate intracardiac device position. Retrospective observational study. Large academic medical center. Patients admitted to the ICU and supported by an intracardiac assist device. We developed a ratio to determine the aortic valve location on supine chest radiograph images. This ratio is used to assess the position of a cardiac assist device and is compared with echocardiographic findings. Supine anterior-posterior chest radiographs of patients with an aortic valve prosthesis (n = 473) were analyzed to determine the location of the aortic valve. We calculated several ratios with the potential to determine the position of the aortic valve. The aortic valve location ratio, defined as the distance between the carina and the aortic valve, divided by the thoracic width, was found to be the best performing ratio. The aortic valve location ratio determines the location of the aortic valve caudal to the carina, at a distance of 0.25 ± 0.05 times the thoracic width for male patients and 0.28 ± 0.05 times the thoracic width for female patients. The aortic valve location ratio was validated using CT images of patients with angina pectoris without known valvular disease (n = 95). There was a good correlation between cardiac device position (Impella) assessed with the aortic valve location ratio and with echocardiography (n = 53). The aortic valve location ratio enables accurate and reproducible localization of the aortic valve on supine chest radiograph. This tool is easily applicable and can be used for assessment of cardiac device position in patients on the ICU.

Pneumothorax in the Supine Patient: Subtle Radiographic Signs.

Routine posteroanterior chest radiographs and computed tomography scans are more sensitive for detecting pneumothoraces than anteroposterior chest radiographs. However, supine chest radiographs are commonly performed as part of the initial and routine assessment of trauma and critically ill patients. Rates of occult pneumothorax can be as high as 50% and have a significant impact in the mortality of these patients; thus, a prompt diagnosis of this entity is important. This pictorial essay will illustrate the pleural anatomy, explain the distribution of air within the pleural space in the supine position, and review the radiologic findings that characterize this entity.