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1.
Medicine (Baltimore) ; 100(12): e25307, 2021 Mar 26.
Article in English | MEDLINE | ID: covidwho-1150011

ABSTRACT

ABSTRACT: In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.


Subject(s)
/diagnostic imaging , Image Processing, Computer-Assisted/methods , Machine Learning , Tomography, X-Ray Computed/methods , Adult , Age Factors , Aged , Female , Humans , Lung/diagnostic imaging , Lung/pathology , Male , Middle Aged , Predictive Value of Tests , Severity of Illness Index
2.
BMC Med Imaging ; 21(1): 57, 2021 03 23.
Article in English | MEDLINE | ID: covidwho-1148211

ABSTRACT

BACKGROUND: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. METHODS: A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease. RESULTS: For the performance of infection segmentation, comparing the segmentation results with manually annotated ground-truth, the average Dice is 91.6% ± 10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1% ± 3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations. CONCLUSIONS: By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.


Subject(s)
/diagnostic imaging , Community-Acquired Infections/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Algorithms , Disease Progression , Humans , Pneumonia/diagnostic imaging , Tomography, X-Ray Computed/methods
3.
Radiology ; 299(1): E204-E213, 2021 04.
Article in English | MEDLINE | ID: covidwho-1147215

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.


Subject(s)
/diagnostic imaging , Databases, Factual/statistics & numerical data , Global Health/statistics & numerical data , Lung/diagnostic imaging , Tomography, X-Ray Computed/methods , Humans , Internationality , Radiography, Thoracic , Radiology , Societies, Medical , Tomography, X-Ray Computed/statistics & numerical data
4.
J Thorac Imaging ; 36(2): 65-72, 2021 Mar 01.
Article in English | MEDLINE | ID: covidwho-1138033

ABSTRACT

RATIONALE AND OBJECTIVES: To assess the effect of computed tomography (CT)-based residual lung volume (RLV) on mortality of patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A single-center, retrospective study of a prospectively maintained database was performed. In total, 138 patients with COVID-19 were enrolled. Baseline chest CT scan was performed in all patients. CT-based automated and semi-automated lung segmentation was performed using the Alma Medical workstation to calculate normal lung volume, lung opacities volume, total lung volume, and RLV. The primary end point of the study was mortality. Univariate and multivariate analyses were performed to determine independent predictors of mortality. RESULTS: Overall, 84 men (61%) and 54 women (39%) with a mean age of 47.3 years (±14.3 y) were included in the study. Overall mortality rate was 21% (29 patients) at a median time of 7 days (interquartile range, 4 to 11 d). Univariate analysis demonstrated that age, hypertension, and diabetes were associated with death (P<0.01). Similarly, patients who died had lower normal lung volume and RLV than patients who survived (P<0.01). Multivariate analysis demonstrated that low RLV was the only independent predictor of death (odds ratio, 1.042; 95% confidence interval, 10.2-10.65). Furthermore, receiver operating characteristic curve analysis demonstrated that a RLV ≤64% significantly increased the risk of death (odds ratio, 4.8; 95% confidence interval, 1.9-11.7). CONCLUSION: Overall mortality of patients with COVID-19 may reach 21%. Univariate and multivariate analyses demonstrated that reduced RLV was the principal independent predictor of death. Furthermore, RLV ≤64% is associated with a 4-fold increase on the risk of death.


Subject(s)
/diagnostic imaging , Lung/diagnostic imaging , Lung/pathology , Tomography, X-Ray Computed/methods , /pathology , Female , Humans , Male , Middle Aged , Prospective Studies , Residual Volume , Retrospective Studies , Severity of Illness Index
5.
J Xray Sci Technol ; 29(2): 229-243, 2021.
Article in English | MEDLINE | ID: covidwho-1136443

ABSTRACT

BACKGROUND AND OBJECTIVE: Radiomics has been widely used in quantitative analysis of medical images for disease diagnosis and prognosis assessment. The objective of this study is to test a machine-learning (ML) method based on radiomics features extracted from chest CT images for screening COVID-19 cases. METHODS: The study is carried out on two groups of patients, including 138 patients with confirmed and 140 patients with suspected COVID-19. We focus on distinguishing pneumonia caused by COVID-19 from the suspected cases by segmentation of whole lung volume and extraction of 86 radiomics features. Followed by feature extraction, nine feature-selection procedures are used to identify valuable features. Then, ten ML classifiers are applied to classify and predict COVID-19 cases. Each ML models is trained and tested using a ten-fold cross-validation method. The predictive performance of each ML model is evaluated using the area under the curve (AUC) and accuracy. RESULTS: The range of accuracy and AUC is from 0.32 (recursive feature elimination [RFE]+Multinomial Naive Bayes [MNB] classifier) to 0.984 (RFE+bagging [BAG], RFE+decision tree [DT] classifiers) and 0.27 (mutual information [MI]+MNB classifier) to 0.997 (RFE+k-nearest neighborhood [KNN] classifier), respectively. There is no direct correlation among the number of the selected features, accuracy, and AUC, however, with changes in the number of the selected features, the accuracy and AUC values will change. Feature selection procedure RFE+BAG classifier and RFE+DT classifier achieve the highest prediction accuracy (accuracy: 0.984), followed by MI+Gaussian Naive Bayes (GNB) and logistic regression (LGR)+DT classifiers (accuracy: 0.976). RFE+KNN classifier as a feature selection procedure achieve the highest AUC (AUC: 0.997), followed by RFE+BAG classifier (AUC: 0.991) and RFE+gradient boosting decision tree (GBDT) classifier (AUC: 0.99). CONCLUSION: This study demonstrates that the ML model based on RFE+KNN classifier achieves the highest performance to differentiate patients with a confirmed infection caused by COVID-19 from the suspected cases.


Subject(s)
/diagnostic imaging , Tomography, X-Ray Computed/methods , Humans , Lung/diagnostic imaging , Machine Learning , Predictive Value of Tests , ROC Curve , Reproducibility of Results
6.
Zhonghua Jie He He Hu Xi Za Zhi ; 44(3): 230-236, 2021 Mar 12.
Article in Chinese | MEDLINE | ID: covidwho-1134266

ABSTRACT

Objective: To explore a modified CT scoring system, its feasibility for disease severity evaluation and its predictive value in coronavirus disease 2019 (COVID-19) patients. Methods: This study was a multi-center retrospective cohort study. Patients confirmed with COVID-19 were recruited in three medical centers located in Beijing, Wuhan and Nanchang from January 27, 2020 to March 8, 2020. Demographics, clinical data, and CT images were collected. CT were analyzed by two emergency physicians of more than ten years' work experience independently through a modified scoring system. Final score was determined by average score from the two reviewers if consensus was not reached. The lung was divided into 6 zones (upper, middle, and lower on both sides) by the level of trachea carina and the level of lower pulmonary veins. The target lesion types included ground-glass opacity (GGO), consolidation, overall lung involvement, and crazy-paving pattern. Bronchiectasis, cavity, pleural effusion, etc., were not included in CT reading and analysis because of low incidence. The reviewers evaluated the extent of the targeted patterns (GGO, consolidation) and overall affected lung parenchyma for each zone, using Likert scale, ranging from 0-4 (0=absent; 1=1%-25%; 2=26%-50%; 3=51%-75%; 4=76%-100%). Thus, GGO score, consolidation score, and overall lung involvement score were sum of 6 zones ranging from 0-24. For crazy-paving pattern, it was only coded as absent or present (0 or 1) for each zone and therefore ranging from 0-6. Results: A total of 197 patients from 3 medical centers and 522 CT scans entered final analysis. The median age of the patients was 64 years, and 54.8% were male. There were 76(38.8%) patients had hypertension and 30(15.3%) patients had diabetes mellitus. There were 75 of the patients classified as moderate cases, as well as 95 severe cases and 27 critical cases. As initial symptom, dry cough occurred in 170 patients, 134 patients had fever, and 125 patients had dyspnea. Reparatory rate, oxygen saturation, lymphocyte count and CURB 65 score on admission day varied among patients with different disease severity scale. There were 50 of the patients suffered from deterioration during hospital stay. The median time consumed for each CT by clinicians was 86.5 seconds. Cronbach's alpha for GGO, consolidation, crazy-paving pattern, and overall lung involvement between two clinicians were 0.809, 0.712, 0.678, and 0.906, respectively, showing good or excellent inter-rater correlation. There were 193 (98.0%) patients had GGO, 147 (74.6%) had consolidation, and 126(64.0%) had crazy-paving pattern throughout clinical course. Bilateral lung involvement was observed in 183(92.9%) patients. Median time of interval for CT scan in our study was 7 days so that the whole clinical course was divided into stages by week for further analysis. From the second week on, the CT scores of various types of lesions in severe or critically patients were higher than those of moderate cases. After the fifth week, the course of disease entered the recovery period. The CT score of the upper lung zones was lower than that of other zones in moderate and severe cases. Similar distribution was not observed in critical patients. For moderate cases, the ground glass opacity score at the second week had predictive value for the escalation of the severity classification during hospitalization. The area under the receiver operating characteristic curve was 0.849, the best cut-off value was 5 points, with sensitivity of 84.2% and specificity of 75.0%. Conclusions: It is feasible for clinicians to use the modified semi-quantitative CT scoring system to evaluate patients with COVID-19. Severe/critical patients had higher scores for ground glass opacity, consolidation, crazy-paving pattern, and overall lung involvement than moderate cases. The ground glass opacity score in the second week had an optimal predictive value for escalation of disease severity during hospitalization in moderate patients on admission. The frequency of CT scan should be reduced after entering the recovery stage.


Subject(s)
Lung/diagnostic imaging , Radiography, Thoracic/standards , Tomography, X-Ray Computed/methods , China , Female , Humans , Male , Predictive Value of Tests , Radiography, Thoracic/methods , Spatial Analysis
7.
Radiol Med ; 125(10): 931-942, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-684337

ABSTRACT

PURPOSE: The purpose of our study was to assess the potential role of chest CT in the early detection of COVID-19 pneumonia and to explore its role in patient management in an adult Italian population admitted to the Emergency Department. METHODS: Three hundred and fourteen patients presented with clinically suspected COVID-19, from March 3 to 23, 2020, were evaluated with PaO2/FIO2 ratio from arterial blood gas, RT-PCR assay from nasopharyngeal swab sample and chest CT. Patients were classified as COVID-19 negative and COVID-19 positive according to RT-PCR results, considered as a reference. Images were independently evaluated by two radiologists blinded to the RT-PCR results and classified as "CT positive" or "CT negative" for COVID-19, according to CT findings. RESULTS: According to RT-PCR results, 152 patients were COVID-19 negative (48%) and 162 were COVID-19 positive (52%). We found substantial agreement between RT-PCR results and CT findings (p < 0.000001), as well as an almost perfect agreement between the two readers. Mixed GGO and consolidation pattern with peripheral and bilateral distribution, multifocal or diffuse abnormalities localized in both upper lung and lower lung, in association with interlobular septal thickening, bronchial wall thickening and air bronchogram, showed higher frequency in COVID-positive patients. We also found a significant correlation between CT findings and patient's oxygenation status expressed by PaO2/FIO2 ratio. CONCLUSION: Chest CT has a useful role in the early detection and in patient management of COVID-19 pneumonia in a pandemic. It helps in identifying suspected patients, cutting off the route of transmission and avoiding further spread of infection.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Mass Chest X-Ray/methods , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Aged , Aged, 80 and over , Coronavirus Infections/epidemiology , Early Diagnosis , Female , Humans , Italy/epidemiology , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Real-Time Polymerase Chain Reaction , Retrospective Studies , Specimen Handling/methods , Young Adult
8.
PLoS One ; 16(3): e0246582, 2021.
Article in English | MEDLINE | ID: covidwho-1125432

ABSTRACT

PURPOSE: To evaluate the discrimination of parenchymal lesions between COVID-19 and other atypical pneumonia (AP) by using only radiomics features. METHODS: In this retrospective study, 301 pneumonic lesions (150 ground-glass opacity [GGO], 52 crazy paving [CP], 99 consolidation) obtained from nonenhanced thorax CT scans of 74 AP (46 male and 28 female; 48.25±13.67 years) and 60 COVID-19 (39 male and 21 female; 48.01±20.38 years) patients were segmented manually by two independent radiologists, and Location, Size, Shape, and First- and Second-order radiomics features were calculated. RESULTS: Multiple parameters showed significant differences between AP and COVID-19-related GGOs and consolidations, although only the Range parameter was significantly different for CPs. Models developed by using the Bayesian information criterion (BIC) for the whole group of GGO and consolidation lesions predicted COVID-19 consolidation and AP GGO lesions with low accuracy (46.1% and 60.8%, respectively). Thus, instead of subjective classification, lesions were reclassified according to their skewness into positive skewness group (PSG, 78 AP and 71 COVID-19 lesions) and negative skewness group (NSG, 56 AP and 44 COVID-19 lesions), and group-specific models were created. The best AUC, accuracy, sensitivity, and specificity were respectively 0.774, 75.8%, 74.6%, and 76.9% among the PSG models and 0.907, 83%, 79.5%, and 85.7% for the NSG models. The best PSG model was also better at predicting NSG lesions smaller than 3 mL. Using an algorithm, 80% of COVID-19 and 81.1% of AP patients were correctly predicted. CONCLUSION: During periods of increasing AP, radiomics parameters may provide valuable data for the differential diagnosis of COVID-19.


Subject(s)
/diagnostic imaging , Pneumonia, Mycoplasma/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Aged , Aged, 80 and over , Bayes Theorem , Cross-Sectional Studies , Diagnosis, Differential , Disease Progression , Female , Humans , Lung/pathology , Lung Diseases, Interstitial/pathology , Male , Middle Aged , Mycoses/pathology , Parenchymal Tissue/diagnostic imaging , Pneumonia, Mycoplasma/pathology , Retrospective Studies , Thorax , Tomography, Emission-Computed/methods
9.
Sci Rep ; 11(1): 5148, 2021 03 04.
Article in English | MEDLINE | ID: covidwho-1117663

ABSTRACT

This study aimed to clarify and provide clinical evidence for which computed tomography (CT) assessment method can more appropriately reflect lung lesion burden of the COVID-19 pneumonia. A total of 244 COVID-19 patients were recruited from three local hospitals. All the patients were assigned to mild, common and severe types. Semi-quantitative assessment methods, e.g., lobar-, segmental-based CT scores and opacity-weighted score, and quantitative assessment method, i.e., lesion volume quantification, were applied to quantify the lung lesions. All four assessment methods had high inter-rater agreements. At the group level, the lesion load in severe type patients was consistently observed to be significantly higher than that in common type in the applications of four assessment methods (all the p < 0.001). In discriminating severe from common patients at the individual level, results for lobe-based, segment-based and opacity-weighted assessments had high true positives while the quantitative lesion volume had high true negatives. In conclusion, both semi-quantitative and quantitative methods have excellent repeatability in measuring inflammatory lesions, and can well distinguish between common type and severe type patients. Lobe-based CT score is fast, readily clinically available, and has a high sensitivity in identifying severe type patients. It is suggested to be a prioritized method for assessing the burden of lung lesions in COVID-19 patients.


Subject(s)
/diagnostic imaging , Lung/diagnostic imaging , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Adult , Age Factors , Female , Humans , Male , Middle Aged , Retrospective Studies , Severity of Illness Index
10.
Can J Physiol Pharmacol ; 99(3): 328-331, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1115153

ABSTRACT

A total of 115 convalescent inpatients with COVID-19 were enrolled. According to the results of scans of lung lesions via computed tomography (CT), the patients were divided into mild, moderate, and severe groups. The clinical data of the patients were collected, including age, gender, finger pulse oxygen pressure, ventricular rate, body temperature, etc. The correlation between the clinical indicators and the lesions of high-resolution CT (HRCT) and bronchiectasis was analyzed. Among the 115 patients, 82 had no bronchiectasis and 33 had bronchiectasis. The bronchodilation-prone layers mainly included the left and right lower lobe of the lung. The probability of branching in the inflamed area was greater than that in the noninflamed area in patients with COVID-19. There were significant differences in gender, CT lesion range, and number of incidents of bronchiectasis between noninflamed and inflamed areas (P < 0.05). Moreover, there were significant differences in age, total proportion of CT lesions, volume of CT lesions, and total number of patients with bronchiectasis among the three groups (P < 0.05). CT lesion range was positively correlated with the total number of patients with bronchiectasis and patient age (respectively, r = 0.186, P < 0.05; r = 0.029, P < 0.05). The lesion range in HRCT images of lungs in patients with COVID-19 is correlated with bronchodilation. The larger the lesion, the higher the probability of bronchiectasis and the more incidents of bronchiectasis.


Subject(s)
Bronchiectasis/pathology , Bronchiectasis/virology , /virology , Lung/pathology , Lung/virology , Pneumonia/pathology , Pneumonia/virology , Adult , Female , Humans , Male , Retrospective Studies , Severity of Illness Index , Tomography, X-Ray Computed/methods
11.
BMJ Case Rep ; 14(3)2021 Mar 02.
Article in English | MEDLINE | ID: covidwho-1115112

ABSTRACT

A 30-year-old, multiparous widow, with postpolio residual paralysis, presented with complaints of dull aching abdominal pain for 15 days. Ultrasound showed a mixed echogenic right adnexal mass with free fluid in the pelvis and abdomen. CT abdomen and pelvis revealed partially defined peripherally enhancing collection in lower abdomen and right adnexa suggestive of tubo-ovarian abscess. There was mild ileal wall thickening and few enlarged mesenteric lymph nodes. Ascitic fluid did not show acid fast bacilli and cultures were sterile. Extensive diagnostic laboratory work was done which was inconclusive. Diagnostic laparoscopy could not be performed due to non-availability of elective operation theatre in the COVID-19 pandemic. Presumptive extrapulmonary tuberculosis was clinically and radiologically diagnosed. She was started on daily anti tuberculosis treatment. This case shows us the importance of imaging as a diagnostic tool and as an alternative for laparoscopy in COVID-19 pandemic to diagnose abdomino-pelvic tuberculosis.


Subject(s)
Abdominal Abscess , Adnexal Diseases , Antitubercular Agents/administration & dosage , Tuberculosis, Urogenital , Abdominal Abscess/diagnostic imaging , Abdominal Abscess/etiology , Abdominal Pain/diagnosis , Adnexal Diseases/diagnosis , Adnexal Diseases/physiopathology , Adnexal Diseases/therapy , Adult , /therapy , Diagnosis, Differential , Female , Humans , Pelvis/diagnostic imaging , Postpoliomyelitis Syndrome/complications , Tomography, X-Ray Computed/methods , Tuberculosis, Urogenital/complications , Tuberculosis, Urogenital/diagnosis , Tuberculosis, Urogenital/physiopathology , Tuberculosis, Urogenital/therapy , Ultrasonography/methods
12.
Medicine (Baltimore) ; 100(9): e25072, 2021 Mar 05.
Article in English | MEDLINE | ID: covidwho-1114904

ABSTRACT

RATIONALE: Northern Italy has been particularly hit by the current Covid-19 pandemic. Italian deceased patients have a mean age of 78.5 years and only 1.2% have no comorbidities. These data started a public debate whether patients die "with" or "from" Covid-19. If on one hand the public opinion has been persuaded to believe that Covid-19 infection has poor outcomes just in elderly and/or fragile subjects, on the other hand, hospitals are admitting an increasing number of healthy young patients needing semi-intensive or intensive care units. PATIENT CONCERNS: At the end of March 2020, a 79-year-old patient (M.G.) was admitted to the emergency department of our hospital with a 5 days history of fever, dyspnea, and cough. He was known for hypertension and coronary artery disease with a previous coronary artery stenting. Both the comorbidities were carried out without complications and the patient was previously asymptomatic and in good health. At admission, he was febrile and showed signs of respiratory failure with hypoxia and hypocapnia at blood gas analysis. DIAGNOSIS: The day after, he was tested for SARS-CoV-2 with a real-time reverse transcriptase-polymerase chain reaction assay of nasopharyngeal swab, which turned positive and a chest CT-Scan was consistent with the diagnosis of interstitial pneumonia. INTERVENTIONS: He was treated with i.v. diuretics, paracetamol, prolonged noninvasive ventilation (CPAP), and empiric antibiotic therapy on top of his chronic treatment. OUTCOMES: A treatment with heparin and corticosteroids was started; however, he developed irreversible respiratory failure. Invasive ventilation was not considered appropriate due to his comorbidities, low chances of recovery, and intensive care unit overcrowding. The patient died 9 days after admission. LESSONS: Health conditions that are most reported as risk factors are common cardiovascular diseases that can be managed in modern clinical practice. Through a brief illustrative clinical case, we would like to underline how Covid-19 can be per se the cause of death in patients that would otherwise have had an acceptable life expectancy.


Subject(s)
Coronary Artery Disease , Hypertension , Patient Care Management/methods , Pneumonia, Viral , Aged , Blood Gas Analysis/methods , /mortality , /methods , Clinical Deterioration , Coronary Artery Disease/epidemiology , Coronary Artery Disease/physiopathology , Coronary Artery Disease/therapy , Fatal Outcome , Humans , Hypertension/epidemiology , Hypertension/physiopathology , Hypertension/therapy , Male , Pneumonia, Viral/blood , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/physiopathology , Pneumonia, Viral/therapy , Risk Assessment , Tomography, X-Ray Computed/methods
13.
J Coll Physicians Surg Pak ; 30(1): S19-S22, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1112942

ABSTRACT

In this study, we report a familial cluster of cases which included five patients and two close contacts who were confirmed to have coronavirus disease 2019 (COVID-19). These participants had received real-time reverse transcription-polymerase chain reaction (RT-PCR) and chest X-rays (CXRs) before diagnosis. The follow-up CXRs of three patients in the family showed significant progression, with COVID-19 pneumonia, clinically worsening in a short period of time. Therefore, the results of follow-up CXRs in the short-term may be an adjunctive diagnostic method for COVID-19 disease diagnosis and its progression. Key Words: Chest X-ray, COVID-19, RT-PCR, Familial clustering.


Subject(s)
/diagnosis , Lung/diagnostic imaging , Pandemics , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Aged , Female , Follow-Up Studies , Humans
14.
J Coll Physicians Surg Pak ; 30(1): S1-S6, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1112938

ABSTRACT

OBJECTIVE: To compare the diagnostic accuracies of HRCT chest and RT-PCR results in diagnosis of coronavirus disease (COVID-19) in a tertiary care hospital in Lahore. STUDY DESIGN: Descriptive study. PLACE AND DURATION OF STUDY: Department of Radiology and Central Research Lab, Lahore General Hospital, Lahore, from April to July, 2020. METHODOLOGY: Patients aged 18 to 83 years, who had clinically suspected symptoms of COVID-19 (fever, cough/sore throat or shortness of breath) presenting in outpatient or emergency department, were included. These patients had their HRCT chest conducted from Radiology Department and RT-PCR performed at Central Research Lab. These data were retrieved from electronic system of PACS. Results were categorised into positive and negative findings for COVID-19. Diagnostic accuracies of HRCT chest and first RT-PCR along with 95% confidence interval were calculated. RESULTS: A total of 94 patients, 55 (58.5%) males and 39 (41.5%) were females. Out of them, 83% patients had positive HRCT chest findings of COVID-19, 17% had negative HRCT chest findings; while 40.4% had positive and 59.6% had negative first PCR. Among the repeat second PCR, 19.6% had negative, 1.8% had positive PCR results; while 78.6% patients didn't undergo repeat PCR. The sensitivity, specificity, NPV, PPV and accuracy of HRCT chest was 92%, 23%, 81%, 45%, and 51%; while of first RT-PCR was 45%, 81%, 23%, 92% and 51%, respectively. CONCLUSION: The sensitivity of HRCT chest is higher (92%) as compared to first RT-PCR (45%). Key Words: COVID-19, RT-PCR, HRCT chest, Sensitivity, Specificity.


Subject(s)
/diagnosis , Pandemics , Reverse Transcriptase Polymerase Chain Reaction/methods , Tomography, X-Ray Computed/methods , Adult , Aged , /virology , Female , Humans , Male , Middle Aged , Pakistan/epidemiology , Retrospective Studies
15.
J Korean Med Sci ; 36(8): e51, 2021 Mar 01.
Article in English | MEDLINE | ID: covidwho-1112584

ABSTRACT

BACKGROUND: Few studies have classified chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) and analyzed their correlations with prognosis. The present study aimed to evaluate retrospectively the clinical and chest CT findings of COVID-19 and to analyze CT findings and determine their relationships with clinical severity. METHODS: Chest CT and clinical features of 271 COVID-19 patients were assessed. The presence of CT findings and distribution of parenchymal abnormalities were evaluated, and CT patterns were classified as bronchopneumonia, organizing pneumonia (OP), or diffuse alveolar damage (DAD). Total extents were assessed using a visual scoring system and artificial intelligence software. Patients were allocated to two groups based on clinical outcomes, that is, to a severe group (requiring O2 therapy or mechanical ventilation, n = 55) or a mild group (not requiring O2 therapy or mechanical ventilation, n = 216). Clinical and CT features of these two groups were compared and univariate and multivariate logistic regression analyses were performed to identify independent prognostic factors. RESULTS: Age, lymphocyte count, levels of C-reactive protein, and procalcitonin were significantly different in the two groups. Forty-five of the 271 patients had normal chest CT findings. The most common CT findings among the remaining 226 patients were ground-glass opacity (98%), followed by consolidation (53%). CT findings were classified as OP (93%), DAD (4%), or bronchopneumonia (3%) and all nine patients with DAD pattern were included in the severe group. Uivariate and multivariate analyses showed an elevated procalcitonin (odds ratio [OR], 2.521; 95% confidence interval [CI], 1.001-6.303, P = 0.048), and higher visual CT scores (OR, 1.137; 95% CI, 1.042-1.236; P = 0.003) or higher total extent by AI measurement (OR, 1.048; 95% CI, 1.020-1.076; P < 0.001) were significantly associated with a severe clinical course. CONCLUSION: CT findings of COVID-19 pneumonia can be classified into OP, DAD, or bronchopneumonia patterns and all patients with DAD pattern were included in severe group. Elevated inflammatory markers and higher CT scores were found to be significant predictors of poor prognosis in patients with COVID-19 pneumonia.


Subject(s)
/diagnostic imaging , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , C-Reactive Protein/analysis , Female , Humans , Logistic Models , Male , Middle Aged , Procalcitonin/blood , Prognosis , Retrospective Studies , Young Adult
16.
Chest ; 159(3): e159-e162, 2021 03.
Article in English | MEDLINE | ID: covidwho-1108124

ABSTRACT

CASE PRESENTATION: A 78-year-old woman was admitted to the ED with a 10-day history of diarrhea and recent onset of dry cough, fever, and asthenia. She had a medical history of obesity (BMI 32) and arterial hypertension treated with irbesartan. In the context of a large-scale lockdown in France during the COVID-19 pandemic, she only had physical contact with her husband, who did not report any symptoms. She required mechanical ventilation because of severe hypoxemia within 1 hour after admission to the ED.


Subject(s)
/methods , Lung/diagnostic imaging , Respiration, Artificial/methods , Respiratory Insufficiency , Tomography, X-Ray Computed/methods , Aged , /epidemiology , /therapy , Comorbidity , Diagnosis, Differential , Diarrhea/diagnosis , Diarrhea/etiology , Female , Humans , Obesity/diagnosis , Obesity/epidemiology , Respiratory Insufficiency/diagnosis , Respiratory Insufficiency/etiology , Respiratory Insufficiency/therapy , Treatment Outcome
17.
Pan Afr Med J ; 35(Suppl 2): 138, 2020.
Article in English | MEDLINE | ID: covidwho-1106484

ABSTRACT

Ground-glass opacity is a CT sign that is currently experiencing renewed interest since it is very common in patients with COVID-19. However, this sign is not specific to any disease. Besides, the possibility of false positive ground-glass opacity related to insufficient inspiration during the acquisition of the chest CT should be known. We report the case of a 36-year-old patient suspected of COVID-19 and in whom a second acquisition of chest CT was necessary to remove false ground-glass opacities that erroneously supported the diagnosis of COVID-19.


Subject(s)
Artifacts , Betacoronavirus , Clinical Laboratory Techniques , Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Coronavirus Infections/diagnosis , False Positive Reactions , Female , Humans , Inhalation , Pandemics
20.
PLoS One ; 16(2): e0247176, 2021.
Article in English | MEDLINE | ID: covidwho-1099926

ABSTRACT

The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.


Subject(s)
/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Radiography/methods , Algorithms , Artificial Intelligence , Databases, Factual , Deep Learning , Humans , Image Processing, Computer-Assisted/methods , Lung/diagnostic imaging , Lung/pathology , Machine Learning , Neural Networks, Computer , Thorax/diagnostic imaging , Tomography, X-Ray Computed/methods
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