Descriptive characteristics of continuous oximetry measurement in moderate to severe COVID-19 patients

Background: Non-invasive oxygen saturation (SpO2) measurement is a central vital sign that supports the management of COVID-19 patients. However, reports on SpO2 characteristics (patterns and dynamics) are scarce and none, to our knowledge, has analysed high resolution continuous SpO2 in COVID-19. Methods: SpO2 signal sampled at 1Hz and clinical data were collected from COVID-19 departments at the Rambam Health Care Campus (Haifa, Israel) between May 1st, 2020 and February 1st, 2021. Data from a total of 367 COVID-19 patients, totalling 27K hours of continuous SpO2 recording, could be retrieved, including 205 non-critical and 162 critical cases. Desaturations based on different SpO2 threshold definitions and oximetry derived digital biomarkers (OBMs) were extracted and compared across severity and support levels. Findings: An absolute SpO2 threshold at 93% was the most efficient in discriminating between critical and non-critical patients without support or under oxygen support. Under no support, the non-critical group depicted a fold change (FC) of 1,8 times more frequent desaturations compared to the critical group. However, the hypoxic burden was 1,6 times more important in critical versus non-critical patients. Other OBMs depicted significant differences, notably the percentage of time below 93% SpO2 (CT93) was the most discriminating OBM. Mechanical ventilation depicted a strong effect on SpO2 by significantly reducing the frequency (1,85 FC) and depth (1,21 FC) of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected up to several hours before the initiation of the mechanical ventilation. Interpretation: This is the first report investigating continuous SpO2 measurements in hospitalized patients affected with COVID-19. SpO2 characteristics differ between critical and non-critical patients and are impacted by the level of support. OBMs from high resolution SpO2 signal may enable to anticipate clinically relevant events, monitoring of treatment response and may be indicative of future deterioration.

Predictors of severity and development of critical illness of Egyptian COVID-19 patients: A multicenter study

Objectives We conducted the present multicenter, retrospective study to assess the epidemiological, clinical, laboratory, and radiological characteristics associated with critical illness among patients with COVID-19 from Egypt. Methods The present study was a multicenter, retrospective study that retrieved the data of all Egyptian cases with confirmed COVID-19 admitted to hospitals affiliated to the General Organization for Teaching Hospitals and Institutes (GOTHI) through the period from March to July 2020. The diagnosis of COVID-19 was based on a positive reverse transcription-polymerase chain reaction (RT-PCR) laboratory test. Results This retrospective study included 2724 COVID-19 patients, of whom 423 (15.52%) were critically ill. Approximately 45.86% of the critical group aged above 60 years, compared to 39.59% in the non-critical group (p = 0.016). Multivariate analysis showed that many factors were predictors of critically illness, including age >60 years (OR = 1.30, 95% CI [1.05, 1.61], p = 0.014), low oxygen saturation (OR = 0.93, 95% CI [0.91, 0.95], p<0.001), low Glasgow coma scale (OR = 0.75, 95% CI [0.67, 0.84], p<0.001), diabetes (OR = 1.62, 95% CI [1.26, 2.08], p<0.001), cancer (OR = 2.47, 95% CI [1.41, 4.35], p = 0.002), and serum ferritin (OR = 1.004, 95% CI [1.0003, 1.008], p = 0.031). Conclusion In the present report, we demonstrated that many factors are associated with COVID-19 critical illness, including older age groups, fatigue, elevated temperature, increased pulse, lower oxygen saturation, the preexistence of diabetes, malignancies, cardiovascular disease, renal diseases, and pulmonary disease. Moreover, elevated serum levels of ALT, AST, and ferritin are associated with worse outcomes. Further studies are required to identify independent predictors of mortality for patients with COVID-19.

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

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.

Evaluation of Severity of Infectious Pneumonia for Newborn Using Ultrasound Image under Adaptive Image Denoising Algorithm

This study was to analyze the ultrasound imaging characteristics of infectious pneumonia of newborn in different conditions and the differences in neurobehavioral development. An adaptive image denoising (AID) algorithm was constructed based on multiscale wavelet features. It was compared with the transform domain denoising (TDD) algorithm and spatial domain denoising (SDD) algorithm and applied to ultrasound images of newborns with infectious pneumonia. It was found that the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity index (FSIM) of the constructed algorithm were higher than those of the TDD and SDD algorithms ( P < 0.05 ). The ultrasound scores of newborns in noncritical group (group A, 1.54 ± 0.62 scores) were all lower than those of the critical group (group B, 3.96 ± 0.41 scores) and extremely critical group (group C, 4.25 ± 0.35 scores) ( P < 0.05 ). The behavioral ability, passive muscle tension, active muscle tension, and original reflection of the newborns in group A were better than other groups ( P < 0.05 ). It indicated that the constructed algorithm showed better denoising effect on ultrasound images, which could effectively evaluate the severity of newborns’ infectious pneumonia.

Characteristics of Lymphocyte Subsets and Cytokine Profiles of Patients With COVID-19

Background: Abnormalities of lymphocyte subsets and cytokine profiles have been observed in most patients with coronavirus disease (COVID-19). Here, we explore the role of lymphocyte subsets and cytokines at hospital admission in predicting the severity of COVID-19.Methods: This study included 214 patients with COVID-19 who were treated at Three Gorges Hospital Affiliated with Chongqing University from January 19, 2020 to April 30, 2020. Patients were divided into the non-intensive care unit (ICU) (mild/moderate) group and the ICU (severe/critical) group, according to the severity of the disease. Clinical and laboratory data, including peripheral lymphocyte subsets and cytokines, were analyzed and compared. Logistic regression was used to analyze the predictive factors for ICU admission. Receiver operating characteristic (ROC) curves were drawn to evaluate the predictive value of selected indicators for the severity of COVID-19.Results: Of the 214 patients enrolled, 161 were non-ICU patients and 53 were ICU patients. At hospital admission, lymphopenia was observed in nearly all of the ICU patients (96.2%) and 84.5% of the non-ICU patients. The absolute number of lymphocytes, CD3+ T cells, CD4+ T cells, CD8+ T cells, CD19+ B cells, and natural killer (NK) cells was lower in the ICU group (659.00 × 106/L, 417.00 × 106/L, 261.00 × 106/L, 140.00 × 106/L, 109.00 × 106/L, 102.00 × 106/L, respectively) than in the non-ICU group (1063.00 × 109/L, 717.00 × 106/L, 432.00 × 106/L, 271.00 × 106/L, 133.00 × 106/L, 143.00 × 106/L, respectively). Interleukin (IL)-6 was significantly higher in the ICU patients than in the non-ICU patients (18.08 pg/mL vs. 3.13 pg/mL). Multivariate logistic regression analysis showed that age (odds ratio: 1.067 [1.034–1.101]), diabetes mellitus (odds ratio: 9.154 [2.710–30.926]), CD3+ T cells (odds ratio: 0.996 [0.994–0.997]), and IL-6 (odds ratio: 1.006 [1.000–1.013]) were independent predictors for the development of severe disease. ROC curve analysis showed that the area under the ROC curve (AUC) of CD3+ T cells and IL-6 was 0.806 (0.737–0.874) and 0.785 (0.705–0.864), respectively, and the cutoff values were 510.5 × 106/L (sensitivity, 71.7%; specificity, 79.5%) and 6.58 pg/mL (77.4%, 74.5%), respectively. There were no statistical differences among all tested indicators of lymphocyte subsets and cytokines between the severe group (n = 38) and the critical group (n = 15) at hospital admission or ICU admission.Conclusions: The levels of lymphocyte subsets decreased and the level of IL-6 increased significantly in the ICU patients compared with the non-ICU patients. Therefore, the number of CD3+ T cells and the level of IL-6 at hospital admission may serve as powerful factors for identifying patients who will have severe disease.

Chest CT and Clinical Features of COVID-19 Patients in the Early Stage of the Epidemic: An Observational Study from China

Background: COVID-19 had caused more than 2.8 million deaths globally, and the epidemic will persist for an extended period of time. We analyzed clinical features of patients in the early stage of the epidemic, so as to deepen the understanding of the disease.Methods: In this retrospective study, we included 84 confirmed cases of COVID-19 during February 1, 2020 and March 31, 2020. Baseline data were used to classify patients as moderate (57%) or severe/critical based on Chinese protocol. We focused on analyzing the differences in chest computed tomography (CT) between the two groups. Results: Of the 84 cases, 50 were male and the median age was 69 years. 55 (65%) patients had comorbidities at admission, more in the severe/critical group (P=0.040). 94% patients had bilateral lesions on CT, up to 68% had lesions involving all lobes. Ground glass opacification (GGO) (96%), consolidation (44%), Linear opacities (50%) and Air bronchogram (23%) were the mainly lesions. The lesion was gradually absorbed over time, but imaging abnormalities can persist for a long time. Compared with moderate cases, the severe/critical group had more pulmonary consolidation changes (P=0.044) and significantly higher CT severity Score (CTSS) (P=0.040). Lymphocyte counts were significantly lower (P=0.011) and NLR were higher (P=0.029) in severe/critical cases. Conclusions: Chest CT showed bilateral and multiple GGO and consolidation mainly. After treatment, pulmonary lesions were gradually absorbed over time, and imaging abnormalities can be persistent for a long time. Lung consolidation, CTSS, comorbidity, lymphocyte counts, and NLR may be predictors of severe COVID-19.

Taxonomy, Ecology and Distribution of Juniperus oxycedrus L. Group in the Mediterranean Basin Using Bioclimatic, Phytochemical and Morphometric Approaches, with Special Reference to the Iberian Peninsula

Several studies have been conducted in the past to clarify various aspects of species in the genus Juniperus L. One critical group is Juniperus oxycedrus L., especially from the taxonomical point of view. For this reason, we have studied the ecology, taxonomy and distribution of the taxa in the J. oxycedrus group. From an ecological and distribution standpoint, in this work we use the ombroedaphoxeric index (Ioex) to explain the presence of Juniperus populations in ombrotypes that are not optimum for these taxa. The controversy over the taxonomy of J. oxycedrus subsp. badia (H. Gay) Debeaux and J. oxycedrus subsp. lagunae (Pau ex C. Vicioso) Rivas Mart. is clarified, and it is accepted as a valid name, J. oxycedrus subsp. badia. The phytochemical differences in essential oils (EO) are addressed and their similarities analyzed; greater similarities are observed between oxycedrus and badia, and between navicularis Gand. and macrocarpa (Sm.) Ball. species. The phytochemical, molecular and distribution differences allow J. oxycedrus subsp. macrocarpa (Sm.) Ball and J. navicularis Gand. to be maintained as species. The results obtained make it possible to establish the rank to which the taxa belong and allow clear discrimination between species in groups that are difficult to interpret. Ecological, bioclimatic, phytochemical and morphometric similarities allow us to subordinate the subsp. macrocarpa to the species J. navicularis.

Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments

Background: The coronavirus disease 2019 (COVID-19) outbreak is spreading rapidly around the world.Purpose: We aimed to explore early warning information for patients with severe/critical COVID-19 based on quantitative analysis of chest CT images at the lung segment level.Materials and Methods: A dataset of 81 patients with coronavirus disease 2019 (COVID-19) treated at Wuhan Wuchang hospital in Wuhan city from 21 January 2020 to 14 February 2020 was retrospectively analyzed, including ordinary and severe/critical cases. The time course of all subjects was divided into four stages. The differences in each lobe and lung segment between the two groups at each stage were quantitatively analyzed using the percentage of lung involvement (PLI) in order to investigate the most important segment of lung involvement in the severe/critical group and its corresponding time point.Results: Lung involvement in the ordinary and severe/critical groups reached a peak on the 18th and 14th day, respectively. In the first stage, PLIs in the right middle lobe and the left superior lobe between the two groups were significantly different. In the second stage and the fourth stage, there were statistically significant differences between the two groups in the whole lung, right superior lobe, right inferior lobe and left superior lobe. The rapid progress of the lateral segment of the right middle lobe on the second day and the anterior segment of the right upper lobe on the 13th day may be a warning sign for severe/critical patients. Age was the most important demographic characteristic of the severe/critical group.Conclusion: Quantitative assessment based on the lung segments of chest CT images provides early warning information for potentially severe/critical patients.

HGF, IL-1α, and IL-27 Are Robust Biomarkers in Early Severity Stratification of COVID-19 Patients

Pneumonia is the leading cause of hospital admission and mortality in coronavirus disease 2019 (COVID-19). We aimed to identify the cytokines responsible for lung damage and mortality. We prospectively recruited 108 COVID-19 patients between March and April 2020 and divided them into four groups according to the severity of respiratory symptoms. Twenty-eight healthy volunteers were used for normalization of the results. Multiple cytokines showed statistically significant differences between mild and critical patients. High HGF levels were associated with the critical group (OR = 3.51; p < 0.001; 95%CI = 1.95–6.33). Moreover, high IL-1α (OR = 1.36; p = 0.01; 95%CI = 1.07–1.73) and low IL-27 (OR = 0.58; p < 0.005; 95%CI = 0.39–0.85) greatly increased the risk of ending up in the severe group. This model was especially sensitive in order to predict critical status (AUC = 0.794; specificity = 69.74%; sensitivity = 81.25%). Furthermore, high levels of HGF and IL-1α showed significant results in the survival analysis (p = 0.033 and p = 0.011, respectively). HGF, IL-1α, and IL 27 at hospital admission were strongly associated with severe/critical COVID-19 patients and therefore are excellent predictors of bad prognosis. HGF and IL-1α were also mortality biomarkers.