[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The coronavirus disease 2019 (COVID-19) pandemic is a rapidly evolving global emergency that continues to strain healthcare systems. Emerging research describes a plethora of patient factors-including demographic, clinical, immunologic, hematological, biochemical, and radiographic findings-that may be of utility to clinicians to predict COVID-19 severity and mortality. We present a synthesis of the current literature pertaining to factors predictive of COVID-19 clinical course and outcomes. Findings associated with increased disease severity and/or mortality include age > 55 years, multiple pre-existing comorbidities, hypoxia, specific computed tomography findings indicative of extensive lung involvement, diverse laboratory test abnormalities, and biomarkers of end-organ dysfunction. Hypothesis-driven research is critical to identify the key evidence-based prognostic factors that will inform the design of intervention studies to improve the outcomes of patients with COVID-19 and to appropriately allocate scarce resources.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rmv.2146

Predictors of COVID-19 severity: A literature review.

COVID-19 has developed into a worldwide pandemic; early identification of severe illness is critical for controlling it and improving the prognosis of patients with limited medical resources. The present study aimed to analyze the characteristics of severe COVID-19 and identify biomarkers for differential diagnosis and prognosis prediction. In total, 27 consecutive patients with COVID-19 and 75 patients with flu were retrospectively enrolled. Clinical parameters were collected from electronic medical records. The disease course was divided into four stages: initial, progression, peak, and recovery stages, according to computed tomography (CT) progress. to mild COVID-19, the lymphocytes in the severe COVID-19 progressively decreased at the progression and the peak stages, but rebound in the recovery stage. The levels of C-reactive protein (CRP) in the severe group at the initial and progression stages were higher than those in the mild group. Correlation analysis showed that CRP (R = .62; P < .01), erythrocyte sedimentation rate (R = .55; P < .01) and granulocyte/lymphocyte ratio (R = .49; P < .01) were positively associated with the CT severity scores. In contrast, the number of lymphocytes (R = -.37; P < .01) was negatively correlated with the CT severity scores. The receiver-operating characteristic analysis demonstrated that area under the curve of CRP on the first visit for predicting severe COVID-19 was 0.87 (95% CI 0.10-1.00) at 20.42 mg/L cut-off, with sensitivity and specificity 83% and 91%, respectively. CRP in severe COVID-19 patients increased significantly at the initial stage, before CT findings. Importantly, CRP, which was associated with disease development, predicted early severe COVID-19.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmv.25871

C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early.

Rationale: In addition to the overwhelming lung inflammation that prevails in COVID-19, hypercoagulation and thrombosis contribute to the lethality of subjects infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Platelets are chiefly implicated in thrombosis. Moreover, they can interact with viruses and are an important source of inflammatory mediators. While a lower platelet count is associated with severity and mortality, little is known about platelet function during COVID-19. Objective: To evaluate the contribution of platelets to inflammation and thrombosis in COVID-19 patients. Methods and Results: Blood was collected from 115 consecutive COVID-19 patients presenting non-severe (n=71) and severe (n=44) respiratory symptoms. We document the presence of SARS-CoV-2 RNA associated with platelets of COVID-19 patients. Exhaustive assessment of cytokines in plasma and in platelets revealed the modulation of platelet-associated cytokine levels in both non-severe and severe COVID-19 patients, pointing to a direct contribution of platelets to the plasmatic cytokine load. Moreover, we demonstrate that platelets release their alpha- and dense-granule contents in both non-severe and severe forms of COVID-19. In comparison to concentrations measured in healthy volunteers, phosphatidylserine-exposing platelet extracellular vesicles were increased in non-severe, but not in severe cases of COVID-19. Levels of D-dimers, a marker of thrombosis, failed to correlate with any measured indicators of platelet activation. Functionally, platelets were hyperactivated in COVID-19 subjects presenting non-severe and severe symptoms, with aggregation occurring at suboptimal thrombin concentrations. Furthermore, platelets adhered more efficiently onto collagen-coated surfaces under flow conditions. Conclusions: Taken together, the data suggest that platelets are at the frontline of COVID-19 pathogenesis, as they release various sets of molecules through the different stages of the disease. Platelets may thus have the potential to contribute to the overwhelming thrombo-inflammation in COVID-19, and the inhibition of pathways related to platelet activation may improve the outcomes during COVID-19.

https://www.ahajournals.org/doi/pdf/10.1161/CIRCRESAHA.120.317703?download=true

Platelets Can Associate with SARS-Cov-2 RNA and Are Hyperactivated in COVID-19.

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.

/pdf/prognostic-assessment-of-covid-19-in-the-intensive-care-unit-3od3lff5bo.pdf

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

Lactate dehydrogenase and C-reactive protein as predictors of respiratory failure in CoVID-19 patients.

The most relevant manifestation of coronavirus disease 2019 (COVID-19) is interstitial pneumonia. Several lung ultrasound (US) protocols for pneumonia diagnosis are used in clinical practice, but none has been proposed for COVID-19 patients' screening in the emergency department. We adopted a simplified 6-scan lung US protocol for COVID-19 pneumonia diagnosis (LUSCOP) and compared its sensitivity with high resolution computed tomography (HRCT) in patients suspected for COVID-19, presenting to one Emergency Department from February 21st to March 15th, 2020, during the outbreak burst in northern Italy. Patients were retrospectively enrolled if both LUSCOP protocol and HRCT were performed in the Emergency Department. The sensitivity of LUSCOP protocol and HRCT were compared. COVID-19 pneumonia's final diagnosis was based on real-time reverse-transcription polymerase chain reaction from nasal-pharyngeal swab and on clinical data. Out of 150 suspected COVID-19 patients, 131 were included in the study, and 130 had a final diagnosis of COVID-19 pneumonia. The most frequent lung ultrasonographic features were: bilateral B-pattern in 101 patients (77%), B-pattern with subpleural consolidations in 26 (19.8%) and lung consolidations in 2 (1.5%). LUSCOP Protocol was consistent with HRCT in correctly screening 130 out of the 131 COVID-19 pneumonia cases (99.2%). In one case COVID-19 pneumonia was excluded by both HRCT and lung US. LUSCOP protocol showed optimal sensitivity and can be proposed as a simple screening tool for COVID-19 pneumonia diagnosis in the context of outbreak burst areas where prompt isolation of suspected patients is crucial for patients' and operators' safety.

/pdf/a-simple-lung-ultrasound-protocol-for-the-screening-of-covid-3lshjr57pi.pdf

Luca Rovero

Papers

Lactate dehydrogenase and C-reactive protein as predictors of respiratory failure in CoVID-19 patients.

A simple lung ultrasound protocol for the screening of COVID-19 pneumonia in the emergency department.