Obesity Is Associated with Changes in Laboratory Biomarkers in Chilean Patients Hospitalized with COVID-19.

BACKGROUND AND AIMS: It is reported that patients with obesity are more frequently hospitalized for COVID-19, and evidence exists that obesity is a risk factor, regardless of other comorbidities. The objective of this study was to evaluate the association of obesity with changes in laboratory biomarkers in hospitalized Chilean patients. MATERIALS AND METHODS: A total of 202 hospitalized patients (71 with obesity and 131 without obesity) with a diagnosis of COVID-19 were included in the study. Demographic, clinical, and laboratory (days 1, 3, 7, 15) data were obtained. We performed a statistical analysis, assuming significance with a value of p < 0.05. RESULTS: Significant differences in chronic respiratory pathology are observed between patients with and without obesity. The inflammatory markers CPR, ferritin, NLR, and PLR are elevated during the evaluated period, while changes in leukocyte populations are present on day 1 (eosinophils) and day 3 (lymphocytes). Finally, a persistent elevation of D-dimer level is observed, presenting significant differences on day 7 between patients with and without obesity. Obesity had a positive correlation with admission to the critical patient unit, invasive mechanical ventilation, and length of hospital stay. CONCLUSION: Patients with obesity hospitalized for COVID-19 present marked elevations of inflammatory and hemostasis parameters, with a correlation between obesity, changes in laboratory biomarkers, and the risk of adverse clinical outcomes also observed.

Prognostic criteria for the development of severe clinical forms of COVID-19 in medical organization workers

Employees of medical organizations are one of the risk groups for infection with a new coronavirus infection (COVID-19), including with the development of severe clinical forms. The aim of the study was to analyze the clinical manifestations of a new coronavirus infection in medical workers with the determination of laboratory markers for the development of severe COVID-19. Material and methods. The study included 186 medical workers who had COVID-19 in 2020. In 67 people (observation group), the disease occurred in the form of pneumonia, in 119 people (comparison group) - acute respiratory infection caused by SARS-CoV-2. In the acute period of the disease, a laboratory examination was carried out: a general clinical blood test, CD-typing of lymphocyte subpopulations, assessment of biochemical parameters, determination of parameters of the hemostasis system and cytokine levels. Using the binary logistic regression method, we have built multifactor models. To determine the threshold values of the indicators, we used ROC analysis. Statistical processing of materials was carried out using Microsoft Office 2016 and IBM SPSS Statistics (version 26). The differences were considered statistically significant at p<0.05. Results and discussion. The most frequent clinical manifestations of COVID-19 were: weakness, fever, myalgia, arthralgia, difficulty in nasal breathing, serous-mucous discharge from the nose, sore throat, cough, feeling of "tightness" in the chest, shortness of breath, headache, pain in the eyeballs, dizziness, anosmia, ageusia and dyspeptic manifestations in the form of diarrhea, nausea or vomiting. Markers associated with the development of severe pneumonia associated with COVID-19 have been identified. Threshold values of laboratory parameters for predicting the severe course of COVID-19 were determined: the number of platelets (less than 239x109/l), lymphocytes (less than 1.955x109/l), cytotoxic T-lymphocytes (less than 0.455x109/l), T-helper cells (less than 0.855x109/l), NK-cells (less than 0.205x109/l), ESR (more than 11.5 mm/h), LDH (more than 196 units/l), total protein (less than 71.55 g/l), D-dimer (more than 0.325 mcg/ml), CRP (more than 4.17 mg/l), IL-6 (more than 3.63 pg/l). Conclusion. The data obtained make it possible to predict the possibility of developing a severe variant of the COVID-19 course.Copyright © 2022 Infectious Diseases: News, Opinions, Training. All rights reserved.

Laboratory markers and radiological signs of mild versus severe COVID-19 patients

Background Coronavirus disease 2019 (COVID-19), a global pandemic that has spread worldwide in a dramatic manner since its first emergence in December 2019 from Wuhan, China. To date, there is still lack of an appropriate protocol that predicts cases who are impending to develop severe COVID-19. Hence, this work was an attempt to determine the potential association of the clinical, laboratory, and radiological parameters with the severity of COVID-19 and the ability of these parameters to predict the severe cases. Patients and methods This is a retrospective study that was based on recruiting the data from the files of patients who attended the chest outpatient clinic, or admitted to the chest department or the ICU of our institution. The study included adult patients who were diagnosed with COVID-19. Patients were categorized into two groups: severe/critical cases and mild/moderate disease cases. Data concerning the patient history, clinical picture, and radiological data were obtained and analyzed. Results Eighty adult patients with COVID-19 were included in this study. They were classified into severe/critical (40 patients) or mild/moderate disease (40 patients). Patients with severe/critical COVID-19 disease were significantly older in age and had higher comorbidities, prevalence, higher incidence of cough, dyspnea, gastrointestinal tract symptoms and fatigue, elevated total leukocyte count, lower relative lymphocytes, lower absolute lymphocytes and higher neutrophils, higher blood glucose levels, higher alanine transaminase, higher aspartate aminotransferase and lower serum albumin, reduced Ca levels, elevated lactate dehydrogenase, serum ferritin, D-dimer, and C-reactive protein levels. They had significantly higher computed tomographic (CT) scores and CT chest with greater than 50% lesions or progressive lesions. The mortality rate was 10%, all of which were from the severe disease group. Conclusion The current study is confirming an overall substantial association between severe COVID-19 and older age, chronic diseases, CT imaging pattern, and severity score, leukocyte count, lymphopenia, blood glucose, serum albumin, alanine transaminase, aspartate aminotransferase, calcium levels, C-reactive protein, D-dimer, lactate dehydrogenase, and ferritin. These results highlighted the importance of using clinical, laboratory, and radiological features for monitoring of COVID-19 patients.Copyright © 2023 The Egyptian Journal of Chest Diseases and Tuberculosis.

Evaluation of the Hematological Patterns from Up to 985 Days of Long COVID: A Cross-Sectional Study.

Long COVID affects many individuals following acute coronavirus disease 2019 (COVID-19), and hematological changes can persist after the acute COVID-19 phase. This study aimed to evaluate these hematological laboratory markers, linking them to clinical findings and long-term outcomes in patients with long COVID. This cross-sectional study selected participants from a 'long COVID' clinical care program in the Amazon region. Clinical data and baseline demographics were obtained, and blood samples were collected to quantify erythrogram-, leukogram-, and plateletgram-related markers. Long COVID was reported for up to 985 days. Patients hospitalized in the acute phase had higher mean red/white blood cell, platelet, and plateletcrit levels and red blood cell distribution width. Furthermore, hematimetric parameters were higher in shorter periods of long COVID than in longer periods. Patients with more than six concomitant long COVID symptoms had a higher white blood cell count, a shorter prothrombin time (PT), and increased PT activity. Our results indicate there may be a compensatory mechanism for erythrogram-related markers within 985 days of long COVID. Increased levels of leukogram-related markers and coagulation activity were observed in the worst long COVID groups, indicating an exacerbated response after the acute disturbance, which is uncertain and requires further investigation.

Machine learning prediction for COVID-19 disease severity at hospital admission.

IMPORTANCE: Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation. OBJECTIVE: To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data. DESIGN, SETTING, AND PARTICIPANTS: We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest's feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods. MAIN OUTCOMES AND MEASURES: Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation. RESULTS: This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html . CONCLUSIONS AND RELEVANCE: In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19.

Metabolic Profile of Patients with Long COVID: A Cross-Sectional Study.

A significant proportion of patients experience a wide range of symptoms following acute coronavirus disease 2019 (COVID-19). Laboratory analyses of long COVID have demonstrated imbalances in metabolic parameters, suggesting that it is one of the many outcomes induced by long COVID. Therefore, this study aimed to illustrate the clinical and laboratory markers related to the course of the disease in patients with long COVID. Participants were selected using a clinical care programme for long COVID in the Amazon region. Clinical and sociodemographic data and glycaemic, lipid, and inflammatory screening markers were collected, and cross-sectionally analysed between the long COVID-19 outcome groups. Of the 215 participants, most were female and not elderly, and 78 were hospitalised during the acute COVID-19 phase. The main long COVID symptoms reported were fatigue, dyspnoea, and muscle weakness. Our main findings show that abnormal metabolic profiles (such as high body mass index measurement and high triglyceride, glycated haemoglobin A1c, and ferritin levels) are more prevalent in worse long COVID presentations (such as previous hospitalisation and more long-term symptoms). This prevalence may suggest a propensity for patients with long COVID to present abnormalities in the markers involved in cardiometabolic health.

Correlation of Viral Load With the Biochemical and Hematological Profiles of COVID-19 Patients in Al Buraimi Hospital, Sultanate of Oman: A Cross-Sectional Study.

Background Rapid identification of COVID-19 is crucial during the pandemic for the treatment and management of patients. Thus, early diagnosis of the disease using laboratory parameters can help in the rapid management of infected patients. This study aimed to investigate the correlation of viral load with hematological and biochemical parameters. This will ultimately help physicians to better understand the dynamics of this novel virus and aid in the management of patients. Methodology Laboratory confirmation of SARS-CoV-2 was performed by reverse transcription-polymerase chain reaction (RT-PCR) at the Al-Buraimi Hospital Laboratory Department using oropharyngeal and nasopharyngeal swabs. Positive cases were collected from July 2020 to January 2021 to be enrolled in this study. Results In this study, 264 confirmed positive patients were included initially and divided into three groups according to their cycle threshold (Ct) values obtained by PCR. Out of the total 264 patients, 174 (65.9%) were male, while 90 (34.1%) were female. However, the final sample was only 253 patients who met the inclusion criteria. With regard to Ct values, the study population was divided into the following three groups: Group 1 with Ct values of 9-20 (n = 87; 34.4%), group 2 with Ct values of 21-30 (n = 122; 47.8%), and group 3 with Ct values of 31-42 (n = 44; 17.4%). Conclusions We found that the proportion of male patients infected with COVID-19 was higher compared to females. In addition, the highest incidence was among patients in the age group of 51-70 years. The ferritin and alanine transaminase levels were highest in the initial stage of the infection (group 1) and decreased at the recovery stage. However, neutrophil, lymphocyte, alkaline phosphatase, and C-reactive protein showed an increasing trend from high viral load groups to low viral load groups. The values of the rest of the parameters, such as albumin, total bilirubin, lactate dehydrogenase, and D-dimer, were slightly higher in the initial stage of the infection but the decreasing trend was low; therefore, they were not considered helpful in predicting the disease severity reflected by their Ct value in the three different groups.

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.

BACKGROUND: The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients. METHODS: This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models. RESULTS: Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features. CONCLUSIONS: Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.

Effect of EARLY administration of DEXamethasone in patients with COVID-19 pneumonia without acute hypoxemic respiratory failure and risk of development of acute respiratory distress syndrome (EARLY-DEX COVID-19): study protocol for a randomized controlled trial.

BACKGROUND: Corticosteroids are one of the few drugs that have shown a reduction in mortality in coronavirus disease 2019 (COVID-19). In the RECOVERY trial, the use of dexamethasone reduced 28-day mortality compared to standard care in hospitalized patients with suspected or confirmed COVID-19 requiring supplemental oxygen or invasive mechanical ventilation. Evidence has shown that 30% of COVID-19 patients with mild symptoms at presentation will progress to acute respiratory distress syndrome (ARDS), particularly patients in whom laboratory inflammatory biomarkers associated with COVID-19 disease progression are detected. We postulated that dexamethasone treatment in hospitalized patients with COVID-19 pneumonia without additional oxygen requirements and at risk of progressing to severe disease might lead to a decrease in the development of ARDS and thereby reduce death. METHODS/DESIGN: This is a multicenter, randomized, controlled, parallel, open-label trial testing dexamethasone in 252 adult patients with COVID-19 pneumonia who do not require supplementary oxygen on admission but are at risk factors for the development of ARDS. Risk for the development of ARDS is defined as levels of lactate dehydrogenase > 245 U/L, C-reactive protein > 100 mg/L, and lymphocyte count of < 0.80 × 109/L. Eligible patients will be randomly assigned to receive either dexamethasone or standard of care. Patients in the dexamethasone group will receive a dose of 6 mg once daily during 7 days. The primary outcome is a composite of the development of moderate or more severe ARDS and all-cause mortality during the 30-day period following enrolment. DISCUSSION: If our hypothesis is correct, the results of this study will provide additional insights into the management and progression of this specific subpopulation of patients with COVID-19 pneumonia without additional oxygen requirements and at risk of progressing to severe disease. TRIAL REGISTRATION: ClinicalTrials.gov NCT04836780. Registered on 8 April 2021 as EARLY-DEX COVID-19.

Longitudinal changes in inflammatory biomarkers among patients with COVID-19: A nationwide study in Iceland.

OBJECTIVES: All SARS-CoV-2-positive persons in Iceland were prospectively monitored and those who required outpatient evaluation or were admitted to hospital underwent protocolized evaluation that included a standardized panel of biomarkers. The aim was to describe longitudinal changes in inflammatory biomarkers throughout the infection period of patients with COVID-19 requiring different levels of care. DESIGN: Registry-based study. SETTING: Nationwide study in Iceland. PATIENTS: All individuals who tested positive for SARS-CoV-2 by real-time polymerase chain reaction (RT-PCR) from February 28 to December 31, 2020 in Iceland and had undergone blood tests between 5 days before and 21 days following onset of symptoms. MEASUREMENTS AND MAIN RESULTS: Data were collected from the electronic medical record system of Landspitali-The National University Hospital of Iceland. Data analyses were descriptive and the evolution of biomarkers was visualized using locally weighted scatterplot smoothing curves stratified by the worst clinical outcome experienced by the patient: outpatient evaluation only, hospitalization, and either intensive care unit (ICU) admission or death. Of 571 included patients, 310 (54.3%) only required outpatient evaluation or treatment, 202 (35.4%) were hospitalized, and 59 (10.3%) were either admitted to the ICU or died. An early and persistent separation of the mean lymphocyte count and plasma C-reactive protein (CRP) and ferritin levels was observed between the three outcome groups, which occurred prior to hospitalization for those who later were admitted to ICU or died. Lower lymphocyte count, and higher CRP and ferritin levels correlated with worse clinical outcomes. Patients who were either admitted to the ICU or died had sustained higher white blood cell and neutrophil counts, and elevated plasma levels of procalcitonin and D-dimer compared with the other groups. CONCLUSIONS: Lymphocyte count and plasma CRP and ferritin levels might be suitable parameters to assess disease severity early during COVID-19 and may serve as predictors of worse outcome.

Validation of Neutrophil-to-Lymphocyte Ratio Cut-off Value Associated with High In-Hospital Mortality in COVID-19 Patients.

INTRODUCTION: The neutrophil-to-lymphocyte ratio (NLR) could be a predictive factor of severe COVID-19. However, most relevant studies are retrospective, and the optimal NLR cut-off point has not been determined. The objective of our research was identification and validation of the best NLR cut-off value on admission that could predict high in-hospital mortality in COVID-19 patients. METHODS: Medical files of all patients admitted for COVID-19 pneumonia in our dedicated COVID-units between March and April 2020 (derivation cohort) and between October and December 2020 (validation cohort) were reviewed. RESULTS: Two hundred ninety-nine patients were included in the study (198 in the derivation and 101 in the validation cohort, respectively). Youden's J statistic in the derivation cohort determined the optimal cut-off value for the performance of NLR at admission to predict mortality in hospitalized patients with COVID-19. The NLR cut-off value of 5.94 had a sensitivity of 62% and specificity of 64%. In ROC curve analysis, the AUC was 0.665 [95% CI 0.530-0.801, p= 0.025]. In the validation cohort, the best predictive cut-off value of NLR was 6.4, which corresponded to a sensitivity of 63% and a specificity of 64% with AUC 0.766 [95% CI 0.651-0.881, p <0.001]. When the NLR cut-off value of 5.94 was applied in the validation cohort, there was no significant difference in death and survival in comparison with the derivation NLR cut-off. Net reclassification improvement (NRI) analysis showed no significant classification change in outcome between both NLR cut-off values (NRI:0.012, p=0.31). CONCLUSION: In prospective analysis, an NLR value of 5.94 predicted high in-hospital mortality upon admission in patients hospitalized for COVID-19 pneumonia.

Clinical Laboratory Markers in COVID-19

Background: The causative agent of the present COVID-19 pandemic is a novel RNA virus called SARS CoV-2. Clinical laboratory has a central role in the diagnosis, prognosis, and predicting the progression of the disease. Several hematological, biochemical, immunological, and coagulation parameters change during the course of the disease. Based on the information from several studies, it is presumed that virus replication alters the immune system of the body. These alterations cause cellular damage in various organs like the lungs, liver, heart, and bone marrow. Ultimately, it may lead to multi-organ failure and death. Methods: An Internet search in Medline, PubMed, Scopus, and Scholarly articles was performed. Studies reporting on changes in laboratory parameters in COVID-19 were selected, data extracted, and analyzed. Conclusion: Laboratory markers are helpful in the diagnosis of cases and more importantly, to identify those patients where chances of disease progression to severity are present. This will not only reduce the burden on the health care system but also reduce the mortality rate by channelizing resources to those cases who need critical care and management.

Efficiencies of Laboratory Parameters in Covid-19 Patients Follow up: Turkish Journal of Medical and Surgical Intensive Care Medicine

Introduction: Diagnostic efficiencies of laboratory parameters used in COVID-19 patients and their association with disease severity were evaluated. Materials and Methods: Laboratory parameters of COVID-19 patients hospitalized in Dr. Lütfi Kırdar Kartal City Hospital between March and August 2020 were evaluated. The patients were grouped as non-severe and severe according to the interim guidance of the World Health Organization (WHO). The diagnostic performances of NLR, D-dimer, CRP, procalcitonin, IL-6, LDH, and ferritin in discrimination of severe cases were evaluated by Receiver operator’s characteristics (ROC) analysis. Generalized lineer model Analysis (GLM) was performed with mortality as a dependent variable and age, gender, NLR, D-dimer, CRP, Procalcitonin, IL-6, LDH, and ferritin as an independent variables. Results: A total of 257 patients were evaluated and there was a significant difference between non-severe and severe cases in terms of NLR, D-dimer, CRP, Procalcitonin, IL-6, LDH, and Ferritin values. All the parameters showed comparable performances in discriminating severe disease;D-dimer with the least (AUC 73.5%), and NLR with the highest (AUC 80.7%) efficiency. Values above 4.5 for NLR, 930 ug/L for D-dimer, 64 mg/L for CRP, 0.136 ug/L for procalcitonin, 44.3 pg/mL for IL-6, 304 IU for LDH, and 312 ug/L for ferritin were associated with severe disease. Contribution of age, NLR, D-dimer, and CRP were found significant on the model. Conclusions: NLR, D-dimer, CRP, procalcitonin, IL-6, LDH, and ferritin showed comparable performances in discriminating severe cases with predefined cut-offs. Age, NLR, D-dimer, and CRP may be considered as predictors of mortality in COVID-19 patients.

Epidemiological and clinical characteristics of 3334 cases with prediagnosis coronavirus disease - 2019 (COVID-19) in Turkey.

AIM: This study aims to analyse the epidemiological and clinical features of the patients admitted to the hospital with the prediagnosis of coronavirus disease 19 (COVID-19) in Turkey. MATERIALS AND METHODS: In this retrospective study, epidemiological and clinical features, laboratory markers, radiological features, therapeutic approaches, and survival conditions of the patients with the prediagnosis of COVID-19 from March 11th to June 30th, 2020 have been analysed and reported. The data of the cases were divided into four groups and then compared with each other: first group includes confirmed cases with positive reverse transcriptase polymerase chain reaction (RT-PCR) and chest computed tomography (CT) imaging results considered as COVID-19 lung involvement, second group includes the clinically diagnosed cases with negative RT-PCR and positive CT imaging abnormalities, third group includes mild and asymptomatic cases with positive RT-PCR and negative CT findings, fourth group includes suspected cases with negative RT-PCR and negative CT findings. Post-hoc analysis was performed to evaluate the differences among the groups. RESULTS: In total, 3334 patients with the prediagnosis of COVID-19 admitted to the emergency department. Based on the post hoc analyses, significant differences were found among the four groups in terms of their test results of leukocytes, haemoglobin, platelet, neutrophils, urea and C-reactive protein (CRP) (P < .001). Furthermore, the factors of age groups, hospitalisation, intensive care unit follow-up and mortality rate of the four groups showed a significant difference among the groups (P = .001). CONCLUSION: The mean leukocytes, neutrophils and platelet counts of patients with positive RT-PCR were found to be lower than the ones with negative RT-PCR. The mean serum levels of CRP were found to be higher in patients with lung involvement compared with other patient groups.

Expert consensus on the monitoring of transthyretin amyloid cardiomyopathy.

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a life-threatening condition with a heterogeneous clinical presentation. The recent availability of treatment for ATTR-CM has stimulated increased awareness of the disease and patient identification. Stratification of patients with ATTR-CM is critical for optimal management and treatment; however, monitoring disease progression is challenging and currently lacks best-practice guidance. In this report, experts with experience in treating amyloidosis and ATTR-CM developed consensus recommendations for monitoring the course of patients with ATTR-CM and proposed meaningful thresholds and frequency for specific parameters. A set of 11 measurable features across three separate domains were evaluated: (i) clinical and functional endpoints, (ii) biomarkers and laboratory markers, and (iii) imaging and electrocardiographic parameters. Experts recommended that one marker from each of the three domains provides the minimum requirements for assessing disease progression. Assessment of cardiac disease status should be part of a multiparametric evaluation in which progression, stability or improvement of other involved systems in transthyretin amyloidosis should also be considered. Additional data from placebo arms of clinical trials and future studies assessing ATTR-CM will help to elucidate, refine and define these and other measurements.

Laboratory markers included in the Corona Score can identify false negative results on COVID-19 RT-PCR in the emergency room.

After December 2019 outbreak in China, the novel Coronavirus infection (COVID-19) has very quickly overflowed worldwide. Infection causes a clinical syndrome encompassing a wide range of clinical features, from asymptomatic or oligosymptomatic course to acute respiratory distress and death. In a very recent work we preliminarily observed that several laboratory tests have been shown as characteristically altered in COVID-19. We aimed to use the Corona score, a validated point-based algorithm to predict the likelihood of COVID-19 infection in patients presenting at the Emergency rooms. This approach combines chest images-relative score and several laboratory parameters to classify emergency room patients. Corona score accuracy was satisfactory, increasing the detection of positive patients' rate.