Immediate and long-term changes in the epidemiology, infection spectrum, and clinical characteristics of viral and bacterial respiratory infections in Western China after the COVID-19 outbreak: a modeling study.

BACKGROUND: The impact of COVID-19 on the epidemiology, clinical characteristics, and infection spectrum of viral and bacterial respiratory infections in Western China is unknown. METHODS: We conducted an interrupted time series analysis based on surveillance of acute respiratory infections (ARI) in Western China to supplement the available data. RESULTS: The positive rates of influenza virus, Streptococcus pneumoniae, and viral and bacterial coinfections decreased, but parainfluenza virus, respiratory syncytial virus, human adenovirus, human rhinovirus, human bocavirus, non-typeable Haemophilus influenzae, Mycoplasma pneumoniae, and Chlamydia pneumoniae infections increased after the onset of the COVID-19 epidemic. The positive rate for viral infection in outpatients and children aged <5 years increased, but the positive rates of bacterial infection and viral and bacterial coinfections decreased, and the proportion patients with clinical symptoms of ARI decreased after the onset of the COVID-19 epidemic. Non-pharmacological interventions reduced the positive rates of viral and bacterial infections in the short term but did not have a long-term limiting effect. Moreover, the proportion of ARI patients with severe clinical symptoms (dyspnea and pleural effusion) increased in the short term after COVID-19, but in the long-term, it decreased. CONCLUSIONS: The epidemiology, clinical characteristics, and infection spectrum of viral and bacterial infections in Western China have changed, and children will be a high-risk group for ARI after the COVID-19 epidemic. In addition, the reluctance of ARI patients with mild clinical symptoms to seek medical care after COVID-19 should be considered. In the post-COVID-19 era, we need to strengthen the surveillance of respiratory pathogens.

An artificial intelligence-based radiomics model for differential diagnosis between coronavirus disease 2019 and other viral pneumonias

OBJECTIVE: To set up a differential diagnosis radiomics model to identify coronavirus disease 2019 (COVID-19) and other viral pneumonias based on an artificial intelligence (AI) approach that utilizes computed tomography (CT) images. MATERIALS AND METHODS: This retrospective multi-center research involved 225 patients with COVID-19 and 265 patients with other viral pneumonias. The least absolute shrinkage and selection operator algorithm was used for the optimized features selection from 1218 radiomics features. Finally, a logistic regression (LR) classifier was applied to construct different diagnosis models. The receiver operating characteristic curve analysis was applied to evaluate the accuracy of different models. RESULTS: The patients were divided into a training set (313 of 392, 80%), an internal test set (79 of 392, 20%) and an external test set (n = 98). Thirteen features were selected to build the machine learning-based CT radiomics models. LR classifiers performed well in the training set (area under the curve [AUC] = 0.91), internal test set (AUC = 0.94), and external test set (AUC = 0.91). Delong tests suggested there was no significant difference between training and the two test sets (P > 0.05). CONCLUSION: The use of an AI-based radiomics model enables rapid discrimination of patients with COVID-19 from other viral infections, which can aid better surveillance and control during a pneumonia outbreak.

A Comparative Study of Severe and Critical Influenza B in Children in the 2021-2022 Winter Season.

Introduction: Influenza B viruses are less common than influenza A viruses in most seasons and cause relatively milder forms of infection that are less studied. We witnessed a dominance of influenza B in Shijiazhuang, China, in the 2021-2022 winter season. In this study, we comparatively investigated the severe and critical influenza B in pediatric patients. Methods: Children who were hospitalized from December 2021 to January 2022 and diagnosed with influenza B were included in this study. Those who tested positive for COVID-19 were excluded. Demographic data, clinical features, underlying medical conditions, laboratory testing results, and treatment outcomes were retrieved and analyzed retrospectively. Disease severity was classified as severe or critical according to Chinese expert consensus on diagnosis and treatment of influenza in children. Results: A significantly greater proportion of patients with critical influenza had extra-pulmonary complications and bacterial coinfections. Children with critical influenza B had substantially higher levels of procalcitonin and lactate dehydrogenase, a markedly higher neutrophil percentage and a significantly lower CD4+ lymphocyte percentage. Conclusion: Our findings suggest that, to effectively manage critical influenza B, therapeutic regimens should consist of organ-specific supportive care, antibiotic application if bacterial coinfection is present, and anti-inflammatory and immune-boosting treatments.

An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.

PURPOSE: To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images. MATERIALS AND METHODS: Data were retrospectively collected from children with pathogen-confirmed bacterial pneumonia including Gram-positive bacterial pneumonia (122/389, 31%), Gram-negative bacterial pneumonia (159/389, 41%) and atypical bacterial pneumonia (108/389, 28%) from January 1 to June 30, 2019. Nine machine-learning models were separately evaluated based on radiomics features extracted from CT images; three optimal submodels were constructed and integrated to form a multi-class classification model. RESULTS: We selected five features to develop three radiomics submodels: a Gram-positive model, a Gram-negative model and an atypical model. The comprehensive radiomics model using support vector machine method yielded an average area under the curve (AUC) of 0.75 [95% confidence interval (CI), 0.65-0.83] and accuracy (ACC) of 0.58 [sensitivity (SEN), 0.57; specificity (SPE), 0.78] in the training set, and an average AUC of 0.73 (95% CI 0.61-0.79) and ACC of 0.54 (SEN, 0.52; SPE, 0.75) in the test set. CONCLUSION: This auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.

An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators.

BACKGROUND: This study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia. METHODS: COVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram. RESULTS: Two clinical indicators that were age and chronic lung disease or asthma and 21 radiomics features were selected to build the radiomics nomogram. The radiomics nomogram yielded an Area Under The Curve1 (AUC) of 0.88 and accuracy of 0.80 in the training set, an AUC of 0.85 and accuracy of 0.77 in internal testing validation set and an AUC of 0.84 and accuracy of 0.75 in independent external validation set. The performance of radiomics nomogram was better than clinical model (AUC = 0.77, p < 0.001) and radiomics model (AUC = 0.72, p = 0.025) in independent external validation set. CONCLUSIONS: The radiomics nomogram may be used to assess the deterioration of COVID-19 pneumonia.

Delayed Stroke Treatment during COVID-19 Pandemic in China.

BACKGROUND: Social distance, quarantine, pathogen testing, and other preventive strategies implemented during CO-VID-19 pandemic may negatively influence the management of acute ischemic stroke (AIS). OBJECTIVE: The current study aimed to evaluate the impacts of COVID-19 pandemic on treatment delay of AIS in China. METHODS: This study included patients with AIS admitted in 2 hospitals in Jiangsu, China. Patients admitted before and after the COVID-19 pandemic outbreak (January 31, 2020, as officially announced by the Chinese government) were screened to collect sociodemographic data, medical history information, and symptom onset status from clinical medical records and compared for pre- (measured as onset-to-door time [ODT]) and posthospital delay (measured as door-to-needle time [DNT]). The influencing factors for delayed treatment (indicated as onset-to-needle time >4.5 h) were analyzed with multivariate logistic regression analysis. RESULTS: A total of 252 patients were included, of which 153 (60.7%) were enrolled before and 99 (39.3%) after the COVID-19 pandemic. ODT increased from 202 min (interquartile range [IQR] 65-492) before to 317 min (IQR 75-790) after the COVID-19 pandemic (p = 0.001). DNT increased from 50 min (IQR 40-75) before to 65 min (IQR 48-84) after the COVID-19 pandemic (p = 0.048). The proportion of patients with intravenous thrombolysis in those with AIS was decreased significantly after the pandemic (15.4% vs. 20.1%; p = 0.030). Multivariate logistic regression analysis indicated that patients after COVID-19 pandemic, lower educational level, rural residency, mild symptoms, small artery occlusion, and transported by other means than ambulance were associated with delayed treatment. CONCLUSIONS: COVID-19 pandemic has remarkable impacts on the management of AIS. Both pre- and posthospital delays were prolonged significantly, and proportion of patients arrived within the 4.5-h time window for intravenous thrombolysis treatment was decreased. Given that anti-COVID-19 measures are becoming medical routines, efforts are warranted to shorten the delay so that the outcomes of stroke could be improved.

Erratum: A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics.

[This corrects the article DOI: 10.1016/j.patter.2020.100092.].

Temporal risk of coronavirus disease 2019 in Gansu Province

Objective: To describe the temporal risk characteristics of coronavirus disease 2019 (COVID-19) in Gansu province.

FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient's clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement.

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics.

The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.