scholarly journals Acute Hemoptysis Redefined: A Deadly Presentation

2018 ◽  
Vol 2018 ◽  
pp. 1-3
Author(s):  
Claudio Galvis ◽  
Juan M. Galvis ◽  
Juan Guardiola ◽  
Adrian P. Umpierrez De Reguero

An aortic aneurysm is a permanent localized arterial dilation with more than 50% of the artery diameter. Among the complications of an aortic aneurysm, one of the rarest is the aorto-bronchial fistula, which presents with massive hemoptysis; this condition is lethal if not treated surgically. We report a 90-year-old man with no significant medical history who presented to the emergency department with abrupt onset of hemoptysis; his chest X-ray displayed left upper lobe opacity with widened mediastinum. CT chest revealed aneurysmatic dilatation of the aorta, left upper lobe opacity suspicious of pulmonary aortic fistula. Thoracic surgery was consulted but due to his poor functional status surgery was deferred. On the second day of hospitalization, the patient developed another episode of massive hemoptysis resulting in hypovolemic shock and expired. This case epitomizes the relevance of broad differential diagnosis for hemoptysis and the prompt assessment and management of the patients with this condition.

2016 ◽  
Vol 4 (2) ◽  
pp. 61-63
Author(s):  
Md Nazmul Hasan ◽  
Md Khorshed Ahmed ◽  
Md Mukhlesur Rahman ◽  
Abu Sadique Abdullah ◽  
Md Abu Siddique ◽  
...  

Symptomatic thoracic aortic aneurysms (TAA) are rare. A 70 year-old man was admitted with hoarseness of voice for the last six months. Postero-anterior chest X-ray showed left hilar enlargement. Computerised thorax tomography (CTT) images were taken and a saccular TAA 8 cm in diameter was found. In this paper, we aimed to show that TAA should be considered as a differential diagnosis of patients with hoarseness of voice and hilar enlargement. Ibrahim Cardiac Med J 2014; 4(2): 61-63


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Cristian Giuseppe Monaco ◽  
Federico Zaottini ◽  
Simone Schiaffino ◽  
Alessandro Villa ◽  
Gianmarco Della Pepa ◽  
...  

An amendment to this paper has been published and can be accessed via the original article.


2021 ◽  
Author(s):  
Anneloes NJ Huijgens ◽  
Laurens J van Baardewijk ◽  
Carolina JPW Keijsers

Abstract BACKGROUND: At the emergency department, there is a need for an instrument which is quick and easy to use to identify geriatric patients with the highest risk of mortality. The so- called ‘hanging chin sign’, meaning that the mandibula is seen to project over one or more ribs on the chest X-ray, could be such an instrument. This study aims to investigate whether the hanging chin sign is a predictor of mortality in geriatric patients admitted through the emergency department. METHODS: We performed an observational retrospective cohort study in a Dutch teaching hospital. Patients of ≥ 65 years who were admitted to the geriatric ward following an emergency department visit were included. The primary outcome of this study was mortality. Secondary outcomes included the length of admission, discharge destination and the reliability compared to patient-related variables and the APOP screener.RESULTS: 396 patients were included in the analysis. Mean follow up was 300 days; 207 patients (52%) died during follow up. The hanging chin sign was present in 85 patients (21%). Patients with the hanging chin sign have a significantly higher mortality risk during admission (OR 2.94 (1.61 to 5.39), p < 0.001), within 30 days (OR 2.49 (1.44 to 4.31), p = 0.001), within 90 days (OR 2.16 (1.31 to 3.56), p = 0.002) and within end of follow up (OR 2.87 (1.70 to 4.84),p < 0.001). A chest X-ray without a PA view or lateral view was also associated with mortality. This technical detail of the chest x-ray and the hanging chin sign both showed a stronger association with mortality than patient-related variables or the APOP screener. CONCLUSIONS: The hanging chin sign and other details of the chest x-ray were strong predictors of mortality in geriatric patients presenting at the emergency department. Compared to other known predictors, they seem to do even better in predicting mortality.


Author(s):  
Petr Arkadievich Ilyin

Blood expectoration or hemoptysis is the coughing up of sputum with blood from the larynx, bronchi or lungs. Hemoptysis is most often caused by diseases of the respiratory tract and lungs — bronchitis or pneumonia, as well as lung cancer, aspergilloma, tuberculosis, bronchiectasis, pulmonary embolism, etc. In the diagnostic investigation of the cause of hemoptysis, it is important to take a detailed history (in the case of an epidemiological history, a laboratory analysis of the secreted sputum for the detection of the causative agent of an infectious disease is necessary), to make the correct interpretation of the patient’s complaints and an assessment of the nature of the sputum (differential diagnosis with bleeding from the upper gastrointestinal tract). A chest X-ray is performed and, then, if indicated, computed tomography, bronchoscopy, and other studies are made. The article presents an algorithm for differential diagnostic investigation of hemoptysis in a patient


Author(s):  
Erin Bell ◽  
Kristen Manto ◽  
Giang Ha ◽  
Nabeal Aljabban ◽  
Lilia Reyes

CJEM ◽  
2004 ◽  
Vol 6 (01) ◽  
pp. 12-21 ◽  
Author(s):  
W.N. Wong ◽  
Antonio C.H. Sek ◽  
Rick F.L. Lau ◽  
K.M. Li ◽  
Joe K.S. Leung ◽  
...  

ABSTRACT Objectives: To assess the association of diagnostic predictors available in the emergency department (ED) with the outcome diagnosis of severe acute respiratory syndrome (SARS). Methods: This retrospective cohort study describes all patients from the Amoy Garden complex who presented to an ED SARS screening clinic during a 2-month outbreak. Clinical and diagnostic predictors were recorded, along with ED diagnoses. Final diagnoses were established independently based on diagnostic tests performed after the ED visit. Associations of key predictors with the final diagnosis of SARS were described. Results: Of 821 patients, 205 had confirmed SARS, 35 undetermined SARS and 581 non-SARS. Multivariable logistic regression showed that the strongest predictors of SARS were abnormal chest x-ray (odds ratio [OR] = 17.4), subjective fever (OR = 9.7), temperature &gt;38°C (OR = 6.4), myalgias (OR = 5.5), chills and rigors (OR = 4.0) and contact exposure (OR = 2.6). In a subset of 176 patients who had a complete blood cell count performed, the strongest predictors were temperature ≥38ºC (OR = 15.5), lymphocyte count &lt;1000 (OR = 9.3) and abnormal chest x-ray (OR = 5.7). Diarrhea was a powerful negative predictor (OR = 0.03) of SARS. Conclusions: Two components of the World Health Organization case definition — fever and contact exposure — are helpful for ED decision-making, but respiratory symptoms do not discriminate well between SARS and non-SARS. Emergency physicians should consider the presence of diarrhea, chest x-ray findings, the absolute lymphocyte count and the platelet count as significant modifiers of disease likelihood. Prospective validation of these findings in other clinical settings is desirable.


CJEM ◽  
2015 ◽  
Vol 18 (5) ◽  
pp. 391-394
Author(s):  
Michael Romano ◽  
Tomislav Jelic ◽  
Jordan Chenkin

AbstractThere is evidence to suggest that point-of-care ultrasound assessment of the lungs has a higher sensitivity and specificity than chest radiography for the diagnosis of pneumonia. It is unknown if the same is true for pneumonia complications. We present and discuss the case of a 61-year-old woman who presented to the emergency department with confusion, decreased level of consciousness, and signs of sepsis. A chest x-ray revealed a right sided infiltrate. An ultrasound of the patient’s lungs was performed, and revealed a complex loculated fluid collection consistent with an empyema. A chest CT confirmed the diagnosis, and immediate percutaneous drainage was performed.


2020 ◽  
Vol 14 (3) ◽  
pp. 179-183
Author(s):  
Lucio Brugioni ◽  
Francesca De Niederhausern ◽  
Chiara Gozzi ◽  
Pietro Martella ◽  
Elisa Romagnoli ◽  
...  

Pericarditis and spontaneous pneumomediastinum are among the pathologies that are in differential diagnoses when a patient describes dorsal irradiated chest pain: if the patient is young, male, and long-limbed, it is necessary to exclude an acute aortic syndrome firstly. We present the case of a young man who arrived at the Emergency Department for chest pain: an echocardiogram performed an immediate diagnosis of pericarditis. However, if the patient had performed a chest X-ray, this would have enabled the observation of pneumomediastinum, allowing a correct diagnosis of pneumomediastinum and treatment. The purpose of this report is to highlight the importance of the diagnostic process.


2020 ◽  
pp. 102490792094899
Author(s):  
Kwok Hung Alastair Lai ◽  
Shu Kai Ma

Background: Artificial intelligence is becoming an increasingly important tool in different medical fields. This article aims to evaluate the sensitivity and specificity of artificial intelligence trained with Microsoft Azure in detecting pneumothorax. Methods: A supervised learning artificial intelligence is trained with a collection of X-ray images of pneumothorax from National Institutes of Health chest X-ray dataset online. A subset of the image dataset focused on pneumothorax is used in training. Two artificial intelligence programs are trained with different numbers of training images. After the training, a collection of pneumothorax X-ray images from patient attending emergency department is retrieved through the Clinical Data Analysis & Reporting System. In total, 115 pneumothorax patients and 60 normal inpatients are recruited. The pneumothorax chest X-ray and the resolution chest X-ray of the above patient group and a collection of normal chest X-ray from inpatients without pneumothorax will be retrieved, and these three sets of images will then undergo testing by artificial intelligence programs to give a probability of being a pneumothorax X-ray. Results: The sensitivity of artificial intelligence-one is 33.04%, and the specificity is at least 61.74%. The sensitivity of artificial intelligence-two is 46.09%, and the specificity is at least 71.30%. The dramatic improvement of 46.09% in sensitivity and improvement of 15.48% in specificity by addition of around 1000 X-ray images is encouraging. The mean improvement of AI-two over AI-one is 19.7% increase in probability difference. Conclusions: We should not rely on artificial intelligence in diagnosing pneumothorax X-ray solely by our models and more training should be expected to explore its full function.


Sign in / Sign up

Export Citation Format

Share Document