scholarly journals Risk Assessment Using Early Quantitative Chest CT Parameters for the Severity of COVID-19

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Xun Ding ◽  
Jia Xu ◽  
Haibo Xu ◽  
Jun Zhou ◽  
Qingyun Long
Keyword(s):  
Risk Assessment ◽  
Lung Volume ◽  
Roc Curve ◽  
Predictive Performance ◽  
Chest Ct ◽  
Critical Group ◽  
Quantitative Ct ◽  
Critical Stage ◽  
Volume Percentage ◽  
Risk Of Progression

Background: Today, the outbreak of coronavirus disease 2019 (COVID-19) is known as a public health emergency by the World Health Organization (WHO). Therefore, risk assessment is necessary for making a correct decision in disease management. Objectives: This study aimed to assess the risk of progression to the critical stage in COVID-19 patients, based on the early quantitative chest computed tomography (CT) parameters. Patients and Methods: In this case-control study, 39 laboratory-confirmed critical or expired COVID-19 cases (critical group), as well as 117 laboratory-confirmed COVID-19 patients including mild, moderate, and severe cases (non-critical group), were enrolled. Seven quantitative CT parameters, representing the lung volume percentages at different density intervals, were automatically calculated, using the artificial intelligence (AI) algorithms. Multivariable-adjusted logistic regression models, based on the quantitative CT parameters, were established to predict the adverse outcomes (critical vs. non-critical). The predictive performance was estimated using the receiver operating characteristic (ROC) curve analysis and by measuring the area under the ROC curve (AUC). The quantitative CT parameters in different stages were compared between the two groups. Results: No significant differences were found between the two groups regarding the lung volume percentages at different density intervals within 0 - 4 days (P = 0.596-0.938); however, this difference began to become significant within 5 - 9 days and persisted even after one month. Overall, the quantitative CT parameters could well predict the severity of COVID-19. The lung volume percentage of -7 Hounsfield units (-7 HUs) had the largest crude odds ratio (OR: 1.999; 95% CI, 1.453 ~ 2.750; P < 0.001) and adjusted OR (adjusted OR: 1.768; 95% CI, 1.114 ~ 2.808; P = 0.016). The lung volume percentage of -6 HU showed the best predictive performance with the largest AUC of 0.808; the cutoff value of 5.93% showed 71.79% sensitivity and 84.62% specificity. Conclusion: Early quantitative chest CT parameters can be measured to assess the risk of progression to the critical stage of COVID-19; this is of critical importance in the clinical management of this disease.

scholarly journals Quantitative CT lung densitometry for the diagnosis of bronchiolitis obliterans in children

2021 ◽  
Author(s):  
Hye Jin Lee ◽  
Seong koo Kim ◽  
Jae Wook Lee ◽  
Soo Ah Im ◽  
Nack Gyun Chung ◽  
...  
Keyword(s):  
Bronchiolitis Obliterans ◽  
Multivariate Logistic Regression Analysis ◽  
Retrospective Chart Review ◽  
Relative Volume ◽  
Chest Ct ◽  
Airflow Limitation ◽  
Quantitative Ct ◽  
Lung Density ◽  
Volume Percentage ◽  
The Difference

Background: The purpose of this study was to evaluate the quantitative diagnostic performance of computed tomography (CT) densitometry in pediatric bronchiolitis obliterans (BO) patients. Methods: A retrospective chart review was performed on 109 children under age 18 who underwent 3D chest CT from March 2019 to March 2021. We measured the mean lung density (MLD) and calculated the difference of MLD (MLDD) in expiratory and inspiratory phase, the expiratory to inspiratory ratio of mean lung density (E/I MLD), and the relative volume percentage of lung density at 50 HU intervals (E600 to E950). We calculated the sensitivity, specificity, and diagnostic accuracy of lung density indices for the diagnosis of BO. Results: A total of 81 patients, 51 BO patients and 30 controls, were included in this study (mean age: 12.7 vs 11.4 years). Expiratory (EXP) MLD, MLDD, E/I MLD, and E900 were all statistically significantly worse in the BO group. Multivariate logistic regression analysis showed that MLDD (odds ratio [OR] = 0.98, p < .001), E/I MLD (OR = 1.39, p < .001), and E850 (OR = 1.54, p = 0.003) were significant densitometry parameters for BO diagnosis. In ROC analysis, E900 (cut-off 1.4%; AUC = 0.920), E/I MLD (cut-off 0.87; AUC = 0.887), and MLDD (cut-off 109 HU; AUC = 0.867) showed high accuracy in diagnosis of BO. Conclusion: The quantification of lung density with chest CT complements the diagnosis by providing additional indications of expiratory airflow limitation in pediatric BO patients.

scholarly journals Compromised Lung Volume and Hemostatic Abnormalities in COVID-19 Pneumonia: Results from an Observational Study on 510 Consecutive Patients

2021 ◽  
Vol 10 (13) ◽  
pp. 2894
Author(s):  
Ezio Lanza ◽  
Maria Elisa Mancuso ◽  
Gaia Messana ◽  
Paola Ferrazzi ◽  
Costanza Lisi ◽  
...  
Keyword(s):  
Lung Volume ◽  
Laboratory Data ◽  
Multivariate Regression Analysis ◽  
Cross Sectional Study ◽  
Chest Ct ◽  
Lung Damage ◽  
Quantitative Ct ◽  
Cross Sectional ◽  
Unenhanced Ct ◽  
D Dimer

Background: Hemostatic abnormalities have been described in COVID-19, and pulmonary microthrombosis was consistently found at autopsy with concomitant severe lung damage. Methods: This is a retrospective observational cross-sectional study including consecutive patients with COVID-19 pneumonia who underwent unenhanced chest CT upon admittance at the emergency room (ER) in one large academic hospital. QCT was used for the calculation of compromised lung volume (%CL). Clinical data were retrieved from patients’ files. Laboratory data were obtained upon presentation at the ER. Aim: The aim of this study was to evaluate the correlation between hemostatic abnormalities and lung involvement in patients affected by COVID-19 pneumonia as described using computer-aided quantitative evaluation of chest CT (quantitative CT (QCT)). Results: A total of 510 consecutive patients (68% males), aged 67 years in median, diagnosed with COVID-19 pneumonia, who underwent unenhanced CT scan upon admission to the ER, were included. In all, 115 patients had %CL > 23%; compared to those with %CL < 23%, they showed higher levels of D-dimer, fibrinogen, and CRP, greater platelet count, and longer PT ratio. Via multivariate regression analysis, BMI ≥ 30 kg/m2, D-dimer levels > 500 ng/mL, CRP > 5.0 ng/mL and PT ratio > 1.2 were found to be independent predictors of a %CL > 23% (adjusted odds ratios (95% confidence intervals): 2.1 (1.1–4.0), 3.1 (1.6–5.8), 2.4 (1.3–4.5), and 3.4 (1.4–8.5), respectively). Conclusions: Hemostatic abnormalities in patients affected by COVID-19 correlate with the severity of lung injury as measured by %CL. Our results underline the pathogenetic role of hemostasis in COVID-19 pneumonia beyond the presence of clinically evident thromboembolic complications.

scholarly journals Quantitative Chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation

2020 ◽  
Author(s):  
EZIO LANZA ◽  
Riccardo Muglia ◽  
Isabella Bolengo ◽  
Orazio Giuseppe Santonocito ◽  
Costanza Lisi ◽  
...  
Keyword(s):  
Risk Factor ◽  
Lung Volume ◽  
Significant Risk ◽  
Chest Ct ◽  
Hospital Death ◽  
Outcome Predictor ◽  
Quantitative Ct ◽  
Automated Method ◽  
Increased Risk ◽  
Ct Analysis

Abstract OBJECTIVE: Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of Quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19.METHODS: We performed a single centre retrospective study on COVID-19 patients hospitalized from January 25th, 2020 to April 28th 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D-Slicer). Lungs were divided by Hounsfield Unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (-500,100HU). We collected patient’s clinical data including oxygenation support throughout hospitalization.RESULTS: Two hundred twenty-two patients (163 males, median age 66, IQR 54-6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression, p<0.001). %CL values in the 6-23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO2/FiO2 ratio (p<.001) and was a risk factor for in-hospital mortality (p<.001)CONCLUSIONS: QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19.

scholarly journals Wound Infections of Snakebites from the Venomous Protobothrops mucrosquamatus and Viridovipera stejnegeri in Taiwan: Bacteriology, Antibiotic Susceptibility, and Predicting the Need for Antibiotics—A BITE Study

Toxins ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 575 ◽  
Author(s):  
Chih-Chuan Lin ◽  
Yen-Chia Chen ◽  
Zhong Ning Leonard Goh ◽  
Chen-Ken Seak ◽  
Joanna Chen-Yeen Seak ◽  
...  
Keyword(s):  
Wound Infection ◽  
Roc Curve ◽  
Predictive Performance ◽  
Antibiotic Usage ◽  
Emergency Department Visits ◽  
Treatment Modalities ◽  
Wound Infections ◽  
Neutrophil Lymphocyte Ratio ◽  
Cell Counts ◽  
Freeze Dried

Snakebites from Taiwan habus (Protobothrops mucrosquamatus) and green bamboo vipers (Viridovipera stejnegeri) account for two-thirds of all venomous snakebites in Taiwan. While there has been ongoing optimization of antivenin therapy, the proper management of superimposed bacterial wound infections is not well studied. In this Bacteriology of Infections in Taiwanese snake Envenomation (BITE) study, we investigated the prevalence of wound infection, bacteriology, and corresponding antibiotic usage in patients presenting with snakebites from these two snakes. We further developed a BITE score to evaluate the probability of wound infections and guide antibiotic usage in this patient population. All snakebite victims who presented to the emergency departments of seven training and research hospitals and received at least one vial of freeze-dried hemorrhagic antivenin between January 2001 and January 2017 were identified. Patient biodata, laboratory investigation results, and treatment modalities were retrieved. We developed our BITE score via univariate and multiple logistic regression analyses. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance of the BITE score. Out of 8,295,497 emergency department visits, 726 patients presented with snakebites from a Taiwan habu or a green bamboo viper. The wound infection rate was 22.45%, with seven positive wound cultures, including six polymicrobial infections. Morganella morganii, Enterococcus spp., Bacteroides fragilis, and Aeromonas hydrophila were most frequently cultured. There were no positive blood cultures. A total of 33.0% (n = 106) of snakebite patients who received prophylactic antibiotics nevertheless developed wound infections, while 44.8% (n = 73) of wound infection patients were satisfactorily treated with one of the following antibiotics: amoxicillin/clavulanic acid, oxacillin, cefazolin, and ampicillin/sulbactam. With the addition of gentamicin, the success of antibiotic therapy increased by up to 66.54%. The prognostic factors for the secondary bacterial infection of snakebites were white blood cell counts, the neutrophil lymphocyte ratio, and the need for hospital admission. The area under the ROC curve for the BITE score was 0.839. At the optimal cut-off point of 5, the BITE score had a 79.58% accuracy, 82.31% sensitivity, and 79.71% specificity when predicting infection in snakebite patients. Our BITE score may help with antibiotic stewardship by guiding appropriate antibiotic use in patients presenting with snakebites. It may also be employed in further studies into antibiotic prophylaxis in snakebite patients for the prevention of superimposed bacterial wound infections.

scholarly journals Generalized Read-Across prediction using genra-py

2021 ◽  
Author(s):  
Imran Shah ◽  
Tia Tate ◽  
Grace Patlewicz
Keyword(s):  
Risk Assessment ◽  
Human Health Risk ◽  
Chemical Properties ◽  
Predictive Performance ◽  
Hazard Identification ◽  
Data Driven ◽  
Chemical Safety ◽  
Physico Chemical ◽  
Data Driven Approach ◽  
Assessment Problems

Abstract Motivation Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert’s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties. Results We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation. Availability and implementation The package is available from github.com/i-shah/genra-py.

scholarly journals Factors Associated with Acute Community-Acquired Pyelonephritis Caused by Extended-Spectrum β-Lactamase-Producing Escherichia coli

2021 ◽  
Vol 10 (21) ◽  
pp. 5192
Author(s):  
Mónica Romero Nieto ◽  
Sara Maestre Verdú ◽  
Vicente Gil ◽  
Carlos Pérez Barba ◽  
Jose Antonio Quesada Rico ◽  
...  
Keyword(s):  
Roc Curve ◽  
Goodness Of Fit ◽  
Logistic Models ◽  
Predictive Performance ◽  
Demographic Characteristics ◽  
E Coli ◽  
Factors Associated ◽  
Extended Spectrum ◽  
Male Sex ◽  
Tract Infections

This study aimed to identify the factors associated with the presence of extended-spectrum ß-lactamase-(ESBL) in patients with acute community-acquired pyelonephritis (APN) caused by Escherechia coli (E. coli), with a view of optimising empirical antibiotic therapy in this context. We performed a retrospective analysis of patients with community-acquired APN and confirmed E. coli infection, collecting data related to demographic characteristics, comorbidities, and treatment. The associations of these factors with the presence of ESBL were quantified by fitting multivariate logistic models. Goodness-of-fit and predictive performance were measured using the ROC curve. We included 367 patients of which 51 presented with ESBL, of whom 90.1% had uncomplicated APN, 56.1% were women aged ≤55 years, 33.5% had at least one mild comorbidity, and 12% had recently taken antibiotics. The prevalence of ESBL-producing E. coli was 13%. In the multivariate analysis, the factors independently associated with ESBL were male sex (OR 2.296; 95% CI 1.043–5.055), smoking (OR 4.846, 95% CI 2.376–9.882), hypertension (OR 3.342, 95% CI 1.423–7.852), urinary incontinence (OR 2.291, 95% CI 0.689–7.618) and recurrent urinary tract infections (OR 4.673, 95% CI 2.271–9.614). The area under the ROC curve was 0.802 (IC 95% 0.7307–0.8736), meaning our model can correctly classify an individual with ESBL-producing E. coli infection in 80.2% of cases.

Low- and high-attenuation lung volume in quantitative chest CT in children

2021 ◽  
Author(s):  
Dimitrios Moutafidis ◽  
Maria Gavra ◽  
Sotirios Golfinopoulos ◽  
Antonios Kattamis ◽  
George Chrousos ◽  
...  
Keyword(s):  
Lung Volume ◽  
Chest Ct ◽  
High Attenuation

scholarly journals A Multi-Factor Analysis of Forecasting Methods: A Study on the M4 Competition

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 41
Author(s):  
Pantelis Agathangelou ◽  
Demetris Trihinas ◽  
Ioannis Katakis
Keyword(s):  
Risk Assessment ◽  
Factor Analysis ◽  
Everyday Life ◽  
Predictive Performance ◽  
Computational Performance ◽  
Forecasting Methods

As forecasting becomes more and more appreciated in situations and activities of everyday life that involve prediction and risk assessment, more methods and solutions make their appearance in this exciting arena of uncertainty. However, less is known about what makes a promising or a poor forecast. In this article, we provide a multi-factor analysis on the forecasting methods that participated and stood out in the M4 competition, by focusing on Error (predictive performance), Correlation (among different methods), and Complexity (computational performance). The main goal of this study is to recognize the key elements of the contemporary forecasting methods, reveal what made them excel in the M4 competition, and eventually provide insights towards better understanding the forecasting task.

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