US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images.

BACKGROUND: Numerous traditional filtering approaches and deep learning-based methods have been proposed to improve the quality of ultrasound (US) image data. However, their results tend to suffer from over-smoothing and loss of texture and fine details. Moreover, they perform poorly on images with different degradation levels and mainly focus on speckle reduction, even though texture and fine detail enhancement are of crucial importance in clinical diagnosis. METHODS: We propose an end-to-end framework termed US-Net for simultaneous speckle suppression and texture enhancement in US images. The architecture of US-Net is inspired by U-Net, whereby a feature refinement attention block (FRAB) is introduced to enable an effective learning of multi-level and multi-contextual representative features. Specifically, FRAB aims to emphasize high-frequency image information, which helps boost the restoration and preservation of fine-grained and textural details. Furthermore, our proposed US-Net is trained essentially with real US image data, whereby real US images embedded with simulated multi-level speckle noise are used as an auxiliary training set. RESULTS: Extensive quantitative and qualitative experiments indicate that although trained with only one US image data type, our proposed US-Net is capable of restoring images acquired from different body parts and scanning settings with different degradation levels, while exhibiting favorable performance against state-of-the-art image enhancement approaches. Furthermore, utilizing our proposed US-Net as a pre-processing stage for COVID-19 diagnosis results in a gain of 3.6% in diagnostic accuracy. CONCLUSIONS: The proposed framework can help improve the accuracy of ultrasound diagnosis.

Quality metrics and outcomes among critically ill patients in China: results of the national clinical quality control indicators for critical care medicine survey 2015-2019.

BACKGROUND: It is crucial to improve the quality of care provided to ICU patient, therefore a national survey of the medical quality of intensive care units (ICUs) was conducted to analyze adherence to quality metrics and outcomes among critically ill patients in China from 2015 to 2019. METHODS: This was an ICU-level study based on a 15-indicator online survey conducted in China. Considering that ICU care quality may vary between secondary and tertiary hospitals, direct standardization was adopted to compare the rates of ICU quality indicators among provinces/regions. Multivariate analysis was performed to identify potential factors for in-hospital mortality and factors related to ventilator-associated pneumonia (VAP), catheter-related bloodstream infections (CRBSIs), and catheter-associated urinary tract infections (CAUTIs). RESULTS: From the survey, the proportions of structural indicators were 1.83% for the number of ICU inpatients relative to the total number of inpatients, 1.44% for ICU bed occupancy relative to the total inpatient bed occupancy, and 51.08% for inpatients with Acute Physiology and Chronic Health Evaluation II scores ≥15. The proportions of procedural indicators were 74.37% and 76.60% for 3-hour and 6-hour surviving sepsis campaign bundle compliance, respectively, 62.93% for microbiology detection, 58.24% for deep vein thrombosis prophylaxis, 1.49% for unplanned endotracheal extubations, 1.99% for extubated inpatients reintubated within 48 hours, 6.38% for unplanned transfer to the ICU, and 1.20% for 48-hour ICU readmission. The proportions of outcome indicators were 1.28‰ for VAP, 3.06‰ for CRBSI, 3.65‰ for CAUTI, and 10.19% for in-hospital mortality. Although the indicators varied greatly across provinces and regions, the treatment level of ICUs in China has been stable and improved based on various quality control indicators in the past 5 years. The overall mortality rate has dropped from 10.19% to approximately 8%. CONCLUSIONS: The quality indicators of medical care in China's ICUs are heterogeneous, which is reflected in geographic disparities and grades of hospitals. This study is of great significance for improving the homogeneity of ICUs in China.

Acute respiratory distress syndrome: focusing on secondary injury.

ABSTRACT: Acute respiratory distress syndrome (ARDS) is one of the most common severe diseases seen in the clinical setting. With the continuous exploration of ARDS in recent decades, the understanding of ARDS has improved. ARDS is not a simple lung disease but a clinical syndrome with various etiologies and pathophysiological changes. However, in the intensive care unit, ARDS often occurs a few days after primary lung injury or after a few days of treatment for other severe extrapulmonary diseases. Under such conditions, ARDS often progresses rapidly to severe ARDS and is difficult to treat. The occurrence and development of ARDS in these circumstances are thus not related to primary lung injury; the real cause of ARDS may be the "second hit" caused by inappropriate treatment. In view of the limited effective treatments for ARDS, the strategic focus has shifted to identifying potential or high-risk ARDS patients during the early stages of the disease and implementing treatment strategies aimed at reducing ARDS and related organ failure. Future research should focus on the prevention of ARDS.

Retrospective Study of Critically Ill COVID-19 Patients With and Without Extracorporeal Membrane Oxygenation Support in Wuhan, China.

Background: Extracorporeal membrane oxygenation (ECMO) might benefit critically ill COVID-19 patients. But the considerations besides indications guiding ECMO initiation under extreme pressure during the COVID-19 epidemic was not clear. We aimed to analyze the clinical characteristics and in-hospital mortality of severe critically ill COVID-19 patients supported with ECMO and without ECMO, exploring potential parameters for guiding the initiation during the COVID-19 epidemic. Methods: Observational cohort study of all the critically ill patients indicated for ECMO support from January 1 to May 1, 2020, in all 62 authorized hospitals in Wuhan, China. Results: Among the 168 patients enrolled, 74 patients actually received ECMO support and 94 not were analyzed. The in-hospital mortality of the ECMO supported patients was significantly lower than non-ECMO ones (71.6 vs. 85.1%, P = 0.033), but the role of ECMO was affected by patients' age (Logistic regression OR 0.62, P = 0.24). As for the ECMO patients, the median age was 58 (47-66) years old and 62.2% (46/74) were male. The 28-day, 60-day, and 90-day mortality of these ECMO supported patients were 32.4, 68.9, and 74.3% respectively. Patients survived to discharge were younger (49 vs. 62 years, P = 0.042), demonstrated higher lymphocyte count (886 vs. 638 cells/uL, P = 0.022), and better CO2 removal (PaCO2 immediately after ECMO initiation 39.7 vs. 46.9 mmHg, P = 0.041). Age was an independent risk factor for in-hospital mortality of the ECMO supported patients, and a cutoff age of 51 years enabled prediction of in-hospital mortality with a sensitivity of 84.3% and specificity of 55%. The surviving ECMO supported patients had longer ICU and hospital stays (26 vs. 18 days, P = 0.018; 49 vs. 29 days, P = 0.001 respectively), and ECMO procedure was widely carried out after the supplement of medical resources after February 15 (67.6%, 50/74). Conclusions: ECMO might be a benefit for severe critically ill COVID-19 patients at the early stage of epidemic, although the in-hospital mortality was still high. To initiate ECMO therapy under tremendous pressure, patients' age, lymphocyte count, and adequacy of medical resources should be fully considered.

The Risk Factors for Weaning Failure of Mechanically Ventilated Patients With COVID-19: A Retrospective Study in National Medical Team Work.

Purpose: This study aimed to describe the clinical and laboratory characteristics and the parameters of the respiratory mechanics of mechanically ventilated patients with confirmed COVID-19 pneumonia and to clarify the risk or protective factors for weaning failure. Methods: Patients diagnosed with COVID-19 pneumonia were selected from the special intensive care unit (ICU) of the Sino-French New City Branch of Tong Ji Hospital, Wuhan, and treated by the National Medical Team Work. They were divided into successful weaning (SW) group (N = 15) and unsuccessful weaning (USW) group (N = 18) according to the prognosis. Information of these patients was analyzed. Results: There were 33 patients included in this study. Patients in the USW group were associated with a poor outcome; the 28-day mortality rate was higher than in the SW group (86.7 vs. 16.7% p < 0.001). By comparison, we found that the initial plateau pressure (Pplat) and driving pressure (DP) of the USW group were higher and that compliance was lower than that of the SW group, but there was no difference between positive end-expiratory pressure (PEEP), partial pressure of carbon dioxide (PCO2), and the ratio of partial pressure arterial oxygen and fraction of inspired oxygen (P/F ratio). Comparing the worst respiratory mechanics parameters of the two groups, the results of the Pplat, DP, compliance, and PEEP were the same as the initial data. The PCO2 of the USW group was higher, while the P/F ratio was lower. A logistic regression analysis suggested that higher Pplat might be an independent risk factor and that higher compliance and lower DP might be protective factors for weaning failure of invasive mechanically ventilated patients with COVID-19 pneumonia. Conclusions: Patients with USW were associated with a poor outcome, higher Pplat might be a risk factor, and a higher compliance and a lower DP might be protective factors for the weaning failure of ventilated COVID-19 patients. Mechanical ventilation settings will affect the patient's prognosis.

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.

BACKGROUND: Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients' prognosis early and administer precise treatment are of great significance. OBJECTIVE: The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. METHODS: In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. RESULTS: Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. CONCLUSIONS: The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

IP-10 and MCP-1 as biomarkers associated with disease severity of COVID-19.

BACKGROUND: COVID-19 is a viral respiratory disease caused by the severe acute respiratory syndrome-Coronavirus type 2 (SARS-CoV-2). Patients with this disease may be more prone to venous or arterial thrombosis because of the activation of many factors involved in it, including inflammation, platelet activation and endothelial dysfunction. Interferon gamma inducible protein-10 (IP-10), monocyte chemoattractant protein-1 (MCP-1) and macrophage inflammatory protein 1-alpha (MIP1α) are cytokines related to thrombosis. Therefore, this study focused on these three indicators in COVID-19, with the hope to find biomarkers that are associated with patients' outcome. METHODS: This is a retrospective single-center study involving 74 severe and critically ill COVID-19 patients recruited from the ICU department of the Tongji Hospital in Wuhan, China. The patients were divided into two groups: severe patients and critically ill patients. The serum IP-10, MCP-1 and MIP1α level in both groups was detected using the enzyme-linked immunosorbent assay (ELISA) kit. The clinical symptoms, laboratory test results, and the outcome of COVID-19 patients were retrospectively analyzed. RESULTS: The serum IP-10 and MCP-1 level in critically ill patients was significantly higher than that in severe patients (P < 0.001). However, no statistical difference in MIP1α between the two groups was found. The analysis of dynamic changes showed that these indicators remarkably increased in patients with poor prognosis. Since the selected patients were severe or critically ill, no significant difference was observed between survival and death. CONCLUSIONS: IP-10 and MCP-1 are biomarkers associated with the severity of COVID-19 disease and can be related to the risk of death in COVID-19 patients.

Correlation between cytokines and coagulation-related parameters in patients with coronavirus disease 2019 admitted to ICU.

BACKGROUND: The novel SARS-CoV-2 caused a large number of infections and deaths worldwide. Thus, new ideas for an appropriated assessment of patients' condition and clinical treatment are of utmost importance. Therefore, in this study, the laboratory parameters of patients with coronavirus disease 2019 (COVID-19) were evaluated to identify the correlation between cytokine expression and other laboratory parameters. METHODS: A retrospective and single-center study was performed in Wuhan, involving 83 severe or critical COVID-19 patients admitted to the intensive care unit (ICU). Laboratory parameters in ICU patients with laboratory-confirmed infection of SARS-CoV2 were collected. The association between parameters was assessed by Spearman's rank correlation. RESULTS: Patients' median age was 66 years (IQR, 57-73), and 55 (66%) were men. Among the 83 patients, 61 (73%) had 1 or more coexisting medical condition. The median concentration of IL-2R, IL-6, IL8, IL10, and TNFα were above the normal range, without IL-1ß. A significant negative correlation between IL-6 and platelet count was discovered (r2 = -0.448, P < 0.001) as well as a significant correlation between IL-6 and other platelet parameters. Finally, a correlation between multiple cytokines and coagulation indicators was found, pro-inflammatory factors were found to be more associated to coagulation parameters, with the highest correlation between IL-6 and the International normalized ratio (INR) (r2 = 0.444, P < 0.001). CONCLUSIONS: Our results suggested that cytokines play an important role in the pathogenesis of COVID-19. In addition, IL-6 seems more relevant in the evaluation of the condition of COVID-19 patients.

Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China.

Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to support other metropolitan areas and large cities outside China with their emerging cases. We used data reported from January 24, 2020, to February 23, 2020, to fit a model of infection, estimate the likely number of infections in four high-risk metropolitan areas based on the number of cases reported, and increase the understanding of the COVID-19 spread pattern. Considering the effect of the official quarantine regulations and travel restrictions for China, which began January 23~24, 2020, we used the daily travel intensity index from the Baidu Maps app to roughly simulate the level of restrictions and estimate the proportion of the quarantined population. A group of SEIR model statistical parameters were estimated using Markov chain Monte Carlo (MCMC) methods and fitting on the basis of reported data. As a result, we estimated that the basic reproductive number, R 0, was 2.91 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen based on the data from January 24, 2020, to February 23, 2020. In addition, we inferred the prediction results and compared the results of different levels of parameters. For example, in Beijing, the predicted peak number of cases was 467 with a peak time of March 01, 2020; however, if the city were to implement different levels (strict, moderate, or weak) of travel restrictions or regulation measures, the estimation results showed that the transmission dynamics would change and that the peak number of cases would differ by between 54% and 209%. We concluded that public health interventions would reduce the risk of the spread of COVID-19 and that more rigorous control and prevention measures would effectively contain its further spread, and awareness of prevention should be enhanced when businesses and social activities return to normal before the end of the epidemic. Further, the experiences gained and lessons learned from China offer the potential to provide evidence supporting other metropolitan areas and big cities with their emerging cases outside China.