scholarly journals Chest x-ray scoring as a predictor of COVID-19 disease; correlation with comorbidities and in-hospital mortality

2021 ◽  
pp. 003693302110274
Author(s):  
Aparajita Singh ◽  
Yoke Hong Lim ◽  
Rajesh Annamalaisamy ◽  
Shyam Sunder Koteyar ◽  
Suresh Chandran ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Ground Glass Opacity ◽  
Tertiary Hospital ◽  
X Rays ◽  
X Ray ◽  
Risk Of Mortality ◽  
Hospitalised Patients ◽  
Hospital Referral ◽  
Number Of Patients ◽  
Chest X Ray

Objectives To devise a novel, simple chest x-ray (CXR) scoring system which would help in prognosticating the disease severity and ability to predict comorbidities and in-hospital mortality. Methods We included a total of 343 consecutive hospitalised patients with COVID-19 in this study. The chest x-rays of these patients were scored retrospectively by three radiologists independently. We divided CXR in to six zones (right upper, mid & lower and left, upper mid & lower zones). We scored each zone as- 0, 1 or 2 as follows- if that zone was clear (0) Ground glass opacity (1) or Consolidation (2). A total of score from 0 to 12 could be obtained. Results A CXR score cut off ≥3 independently predicted mortality. Along with a relatively higher NPV ≥80%, it reinforced the importance of CXR score is a screening tool to triage patients according to risk of mortality. Conclusions We propose that Pennine score is a simple tool which can be adapted by various countries, experiencing a large surge in number of patients, to decide which patient would need a tertiary Hospital referral/admission as opposed to patients that can be managed locally or at basic/primary care hospitals.

scholarly journals Estimation of the Overuse of Preoperative Chest X-rays According to “Choosing Wisely”, “No Hacer”, and “Essencial” Initiatives: Are They Equally Applicable and Comparable?

2020 ◽  
Vol 17 (23) ◽  
pp. 8783
Author(s):  
Jorge Vicente-Guijarro ◽  
José Valencia-Martín ◽  
Paloma Moreno-Nunez ◽  
Pedro Ruiz-López ◽  
José Mira-Solves ◽  
...  
Keyword(s):  
Tertiary Hospital ◽  
Choosing Wisely ◽  
X Rays ◽  
X Ray ◽  
Relative Value ◽  
Wide Variability ◽  
Chest X Ray ◽  
American Society ◽  
Practice Methods ◽  
Preoperative Chest

Background: Overuse reduces the efficiency of healthcare systems and compromises patient safety. Different institutions have issued recommendations on the indication of preoperative chest X-rays, but the degree of compliance with these recommendations is unknown. This study investigates the frequency and characteristics of the inappropriateness of this practice. Methods: This is a descriptive observational study with analytical components, performed in a tertiary hospital in the Community of Madrid (Spain) between July 2018 and June 2019. The inappropriateness of preoperative chest X-ray tests was analyzed according to “Choosing Wisely”, “No Hacer” and “Essencial” initiatives and the cost associated with this practice was estimated in Relative Value and Monetary Units. Results: A total of 3449 preoperative chest X-ray tests were performed during the period of study. In total, 5.4% of them were unjustified according to the “No Hacer” recommendation and 73.3% according to “Choosing Wisely” and “Essencial” criteria, which would be equivalent to 5.6% and 11.8% of the interventions in which this test was unnecessary, respectively. One or more preoperative chest X-ray(s) were indicated in more than 20% of the interventions in which another chest X-ray had already been performed in the previous 3 months. A higher inappropriateness score was also recorded for interventions with an American Society of Anesthesiologists (ASA) grade ≥ III (16.5%). The Anesthesiology service obtained a lower inappropriateness score than other Petitioning Surgical Services (57.5% according to “Choosing Wisely” and “Essencial”; 4.1% according to “No Hacer”). Inappropriate indication of chest X-rays represents an annual cost of EUR 52,122.69 (170.1 Relative Value Units) according to “No Hacer” and EUR 3895.29 (2276.1 Relative Value Units) according to “Choosing Wisely” or “Essencial” criteria. Conclusions: There was wide variability between the recommendations that directly affected the degree of inappropriateness found, with the main reasons for inappropriateness being duplication of preoperative chest X-rays and the lack of consideration of the particularities of thoracic interventions. This inappropriateness implies a significant expense according to the applicable recommendations and therefore a high opportunity cost.

scholarly journals Abnormal Admission Chest X-Ray and MEWS as ICU Outcome Predictors in a Sub-Saharan Tertiary Hospital: A Prospective Observational Study

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Hannington Ssemmanda ◽  
Tonny Stone Luggya ◽  
Clare Lubulwa ◽  
Zeridah Muyinda ◽  
Pascal Kwitonda ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Good Predictor ◽  
Positive Association ◽  
Tertiary Hospital ◽  
Mulago Hospital ◽  
X Rays ◽  
Cross Sectional ◽  
X Ray ◽  
Superior Mediastinum ◽  
Chest X Ray

Background. Critical care in Uganda is a neglected speciality and deemed costly with limited funding/prioritization. We studied admission X-ray and MEWS as mortality predictors of ICU patients requiring mechanical ventilation.Materials and Methods. We did a cross-sectional study in Mulago Hospital ICU and 87 patients for mechanical ventilation were recruited with mortality as the outcome of interest. Chest X-ray results were the main independent variable and MEWS was also gotten for all patients.Results. We recruited 87 patients; most were males (60.92%), aged between 16 and 45 years (59.77%), and most admissions for mechanical ventilation were from the Trauma Unit (30.77%). Forty-one (47.13%) of the 87 patients died and of these 34 (53.13%) had an abnormal CXR with an insignificant IRR = 1.75 (0.90–3.38) (p=0.062). Patients with MEWS ≥ 5 (pvalues = 0.018) and/or having an abnormal superior mediastinum (pvalues = 0.013) showed a positive association with mortality while having a MEWS≥5 had an incidence risk ratio = 3.29 (1.00–12.02) (p=0.018). MEWS was a good predictor of mortality (predictive value = 0.6739).Conclusion. Trauma (31%) caused most ICU admissions, having an abnormal admission chest X-rays positively associated with mortality and a high MEWS was also a good predictor of mortality.

scholarly journals Chest X-ray classification using Deep learning for automated COVID-19 screening

2020 ◽  
Author(s):  
Ankita Shelke ◽  
Madhura Inamdar ◽  
Vruddhi Shah ◽  
Amanshu Tiwari ◽  
Aafiya Hussain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Ground Glass Opacity ◽  
Classification Model ◽  
X Rays ◽  
X Ray ◽  
The People ◽  
History Of ◽  
Chest X Ray

AbstractIn today’s world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

Chest X-Rays Findings in Patients Positive for COVID 19 at Sheikh Zayed Hospital Lahore

2021 ◽  
Vol 15 (5) ◽  
pp. 1196-1199
Author(s):  
A. Z. Sheikh ◽  
Z. Tariq ◽  
S. Noor ◽  
A. Ambreen ◽  
S. Awan ◽  
...  
Keyword(s):  
Ground Glass Opacity ◽  
British Society ◽  
Ground Glass ◽  
Care Hospital ◽  
X Rays ◽  
Middle Zone ◽  
Rt Pcr ◽  
Thoracic Imaging ◽  
X Ray ◽  
Chest X Ray

Aim: To assess the results of chest x ray radiographs of patients positive for Covid-19, presented at the tertiary care hospital according to the classification by the British Society of Thoracic Imaging (BSTI. Place and Duration: In COVID-19 Ward (Department of Medicine) Sheikh Zayed Hospital, Lahore for three months duration from January 2021 to March 2021. Methods: A total of 96 patients were selected. In this observational study, positive COVID-19 patient determined by the reverse transcriptase polymerase chain reaction (RT-PCR) were enrolled for this study above the age of 14 years. CXR results were classified conferring to BSTI documentation and classification in terms of percentage and frequency. Results: Chest rays of 96 patients who tested positive for Covid-19 by RT-PCR over the age of 14 years were examined. Chest X-rays are classified according to the BSTI Covid-19 X-ray classification. Out of 96 patients, 10 patients (10.41%) had normal chest x-rays, 19 (19.80%) patients had classic bilateral, peripheral and basal consolidation / ground glass opacity (GMO), 60 (62.5%) had unspecified group,7(7.29%) patients have poor quality X-ray film. The unilateral involvement was noticed in 15 and bilateral in 49 patients, 12 of the patients had diffuse involvement on chest radiograph and peripheral involvement in 39 patients. According to regional dominance, 41 of the unspecified (42.70%) had middle and lower lung involvement, 7 (7.29%) had only the middle zone, and 8 (8.33%) had involvement of lower zone. Conclusions: In this study, Covid-19 chest X-rays are usually presented as ground glass opacity, mixed consolidation with GGOs in the middle and lower peripheral areas of the bilateral lung. Chest X-ray BSTI classification is used to classify Covid-19 severity in our patients, thus differentiating in the classic Covid-19 of the middle zone versus low zone involvement. Keywords: Consolidation, Covid, Ground Glass Opacity, Chest Image.

scholarly journals Chest X-Ray Findings Among Adult Patients Attending Usmanu Danfodiyo University Teaching Hospital Sokoto Northwestern Nigeria

2020 ◽  
Author(s):  
Mohammed Abacha ◽  
Isma'il Salima ◽  
Sadiq Abubakar Audu ◽  
Abubakar Umar ◽  
Gurama Aminu Dahiru ◽  
...  
Keyword(s):  
Teaching Hospital ◽  
Hypertensive Heart Disease ◽  
Adult Patients ◽  
University Teaching ◽  
Pathologic Finding ◽  
X Rays ◽  
X Ray ◽  
Number Of Patients ◽  
Male Preponderance ◽  
Chest X Ray

Background: Chest x-ray is the most frequently performed diagnostic examination particularly in patients with respiratory and cardiac diseases and for routine medical checkup and planning for surgery. A study on the image quality of chest x-rays had been conducted but the findings on the chest x-rays have not been studied in this tertiary health institution. This study aimed at revealing the most common pathologies and sex distribution of the pathologies on chest x-rays of adult patients attending the hospital. Materials and Methods: A retrospective study of 190 adult (aged 18 and above) patients’ chest x-ray reports was conducted using the existing reported documents of chest x-rays from the archives of Radiology Department of Usmanu Danfodiyo University Teaching Hospital (UDUTH) Sokoto from January 2018 - October 2019 using data capture sheet as instrument for data collection. Data was analyzed using Microsoft excel version 2010. Results: Out of the 190 chest x-rays, 54% were for male while 46% were for female patients with the highest number of patients in the 29-38 years age group. Most of the radiographs studied were normal examinations (38.95%). Moreover, the most common pathology was hypertensive heart failure (26.84%) with male preponderance (13.68%). Conclusion: Hypertensive heart disease is the most common pathologic finding of adult chest x-ray in the study area with elderly male preponderance.

STUDY OF INFLAMMATORY MARKERS IN RELATION TO RADIOLOGICAL FINDINGS IN COVID 19

2021 ◽  
pp. 48-50
Author(s):  
Kalyanisri. Koneru ◽  
V M Kiran Ogirala ◽  
Kommavarapu. Kalyani Madhuri ◽  
Bokam. Bhanu Rekha
Keyword(s):  
Hospital Stay ◽  
Tertiary Care ◽  
P Value ◽  
Care Hospital ◽  
X Rays ◽  
X Ray ◽  
Duration Of Hospital Stay ◽  
Number Of Patients ◽  
Chest X Ray ◽  
Severity Of The Disease

BACKGROUND Currently, the Coronavirus disease 2019 (COVID-19) has become pandemic globally. Elevated inammatory markers are observed and are a common pathophysiological response to acute illness. Chest X-ray changes are also commonly seen in COVID -19 patients. The present study was undertaken to determine the relationship between inammatory markers to chest X-ray ndings in COVID-19 patients. METHODS This is a prospective observational study of COVID-19 patients admitted to tertiary care hospital from may 2020-November 2020. Comorbidities, inammatory markers, and Chest X ray were collected and analyzed. Correlations between radiological and inammatory markers were studied. AIMS & OBJECTIVES: Ÿ Correlation of inammatory markers to radiographic ndings and their outcome in COVID 19 patients Ÿ The outcome was studied in terms of: Ÿ Patients requiring oxygen/ NIVsupport Ÿ duration of hospital stay Ÿ Number of patients Recovered/death RESULTS: Ÿ Out of 500 patients studied, the mean age was 49.41 years, and (295)59% of patients were male,(205)41% were females. (455)91% patients discharged and (43)8.6% died. We found a positive correlation between inammatory markers and Chest X-ray ndings at the time of admission with a signicant statistical P-value. The inammatory markers CRP, ESR, D-Dimer & Sr.ferritin compared with the mode of ventilation(O2 & NIV, duration of hospital stay and outcome also showed signicant statistical P-value. CONCLUSIONS We conclude that in patients with raised inammatory markers there were increased abnormalities on Chest X-rays which required an increase in oxygen or NIVsupport. This can be a useful predictor of the severity of the disease and assessment of outcome.

scholarly journals HRCT findings of pulmonary sarcoidosis; relation to pulmonary function tests

2013 ◽  
Vol 8 ◽  
Author(s):  
Fatih Ors ◽  
Seyfettin Gumus ◽  
Mehmet Aydogan ◽  
Sebahattin Sari ◽  
Samet Verim ◽  
...  
Keyword(s):  
Pulmonary Function ◽  
Pulmonary Function Tests ◽  
Ground Glass Opacity ◽  
Pulmonary Involvement ◽  
Pulmonary Sarcoidosis ◽  
X Rays ◽  
Type Distribution ◽  
X Ray ◽  
Chest X Ray ◽  
Parenchymal Abnormalities

Background: Chest-X-ray has several limitations in detecting the extent of pulmonary disease in sarcoidosis. It might not reflect the degree of pulmonary involvement in patients with sarcoidosis when compared to computed tomography of the thorax. We aimed to investigate the HRCT findings of pulmonary sarcoidosis and to find out the existence of possible relations between HRCT findings and PFTs. In addition, we aimed to investigate the accordance between HRCT findings and conventional chest-X-ray staging of pulmonary sarcoidosis. Method: 45 patients with sarcoidosis with a mean age 29.7+/− 8.4 years were evaluated. Six of them were female and 39 were male. The type, distribution and extent of the parameters on HRCT/CTs were evaluated and scored. Chest-X-rays were evaluated for the stage of pulmonary sarcoidosis. Correlations were investigated between HRCT/ CT parameter scores, Chest X-Ray stages and pulmonary function parameters. Results: Nodule, micronodule, ground glass opacity and consolidation were the most common HRCT findings. There were significant correlations between pulmonary function parameters, HRCT pattern scores, and chest-X-ray stages. A significant correlation between chest-x-ray score and total HRCT score was found. Conclusions: Pulmonary sarcoidosis patients might have various pulmonary parenchymal changes on HRCT. Thorax HRCT was superior to chest-X-ray in detecting pulmonary parenchymal abnormalities. The degree of pulmonary involvement might be closely related to the loss of pulmonary function measured by PFTs. Chest-X-ray is considered to have a role in the evaluation of pulmonary sarcoidosis.

Spontaneous Pneumomediastinum: Uncommon Cause of Air Leak in Late Preterm Neonate

2021 ◽  
Vol 35 (2) ◽  
pp. 93-94
Author(s):  
Jyotsna Bhushan ◽  
Shagufta Iqbal ◽  
Abhishek Chopra
Keyword(s):  
Respiratory Distress ◽  
Conservative Management ◽  
Preterm Neonate ◽  
Late Preterm ◽  
Intravenous Fluids ◽  
X Rays ◽  
Blood Gas ◽  
X Ray ◽  
Spontaneous Pneumomediastinum ◽  
Chest X Ray

A clinical case report of spontaneous pneumomediastinum in a late-preterm neonate, chest x-ray showing classical “spinnaker sail sign,” which was managed conservatively and had excellent prognosis on conservative management. Respiratory distress in a preterm neonate is a common clinical finding. Common causes include respiratory distress syndrome, transient tachypnea of the newborn, pneumonia, and pneumothorax. Pneumomediastinum is not very common cause of respiratory distress and more so spontaneous pneumomediastinum. We report here a preterm neonate with spontaneous pneumomediastinum who had excellent clinical recovery with conservative management. A male baby was delivered to G3P1A1 mother at 34 + 6 weeks through caesarean section done due to abruptio placenta. Apgar scores were 8 and 9. Maternal antenatal history was uneventful and there were no risk factors for early onset sepsis. Baby had respiratory distress soon after birth with Silverman score being 2/10. Baby was started on oxygen (O2) by nasal prongs through blender 0.5 l/min, FiO2 25%, and intravenous fluids. Blood gas done was normal. Possibility of transient tachypnea of newborn or mild hyaline membrane disease was kept. Respiratory distress increased at 20 h of life (Silverman score: 5), urgent chest x-ray done revealed “spinnaker sign” suggestive of pneumomediastinum, so baby was shifted to O2 by hood with FiO2 being 70%. Blood gas repeated was normal. Baby was managed conservatively on intravenous fluids and O2 by hood. Baby was gradually weaned off from O2 over next 5 days. As respiratory distress decreased, baby was started on orogastric feed, which baby tolerated well and then was switched to oral feeds. Serial x-rays showed resolution of pneumomediastinum. Baby was discharged on day 7 of life in stable condition on breast feeds and room air.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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